Author: tritonstation

Stacy McGaugh is an astrophysicist and cosmologist who studies galaxies, dark matter, and theories of modified gravity. He is an expert on low surface brightness galaxies, a class of objects in which the stars are spread thin compared to bright galaxies like our own Milky Way. He demonstrated that these dim galaxies appear to be dark matter dominated, providing unique tests of theories of galaxy formation and modified gravity. Professor McGaugh is currently the chair of the Department of Astronomy at Case Western Reserve University in Cleveland, Ohio, and director of the Warner and Swasey Observatory. Previously he was a member of the faculty at the University of Maryland, having also held research fellowships at Rutgers, the Department of Terrestrial Magnetism of the Carnegie Institution of Washington, and the Institute of Astronomy at the University of Cambridge after earning his Ph.D. from the University of Michigan.

Holiday Concordance

Holiday Concordance

Screw the Earth and its smoking habit. The end of 2023 approaches, so let’s talk about the whole universe, which is its own special kind of mess.

As I’ve related before, our current cosmology, LCDM, was established over the course of the 1990s through a steady drip, drip, drip of results in observational cosmology – what Peebles calls the classic cosmological tests. There were many contributory results; I’m not going to attempt to go through them all. Important among them were the age problem, the realization that the mass density was lower than expected, and that there was more structure on large scales+ than predicted. These established LCDM in the mid-1990s as the “concordance model” – the most probable flavor of FLRW universe. Here is the key figure from Ostriker & Steinhardt depicting the then-allowed region of the density parameter and Hubble constant:

The addition of the cosmological constant to the standard model – replacing SCDM with LCDM – was a brain-wrenching ordeal. Lambda had long been anathema, and there was a region in which an open universe was possible, even reasonable (stripes over shade in the figure above). Moreover, this strange new LCDM made the seemingly inconceivable prediction that not only was the universe expanding [itself the older mind-bender brought to us by Hubble (and Slipher and Lemaître)], the expansion rate should be accelerating. This sounded like crazy talk at the time, so it was greeted with great rejoicing when corroborated by observations of Type Ia supernovae.

A further prediction that could distinguish LCDM from then-viable open models was the geometry of the universe. Open models have a negative curvature (Ωk < 0, in which initially parallel light beams diverge) while the geometry in LCDM should be uniquely flat (Ωk = 0, in which initially parallel light beams remain parallel forever). Uniqueness is important, as it makes for a strong prediction, such as the location of the first peak of the acoustic power spectrum of the cosmic microwave background. In LCDM, this location was predicted to be ℓ ≈ 200 with little flexibility. For viable open models, it was more like ℓ ≈ 800 with a great deal of flexibility. The interpretation of the supernova data relied heavily on the assumption of a flat geometry, so I recall breathing a sigh of relief* when ℓ ≈ 200 was clearly observed.

Where are we now? I decided to reconstruct the Ostriker & Steinhardt plot with modern data. Here it is, with the axes swapped for reasons unrelated to this post. Deal with it.

The concordance region (white space) in the mass density-expansion rate space where the allowed regions (colored bands) of many constraints intersect. Illustrated constraints include a direct measurement of the Hubble constant, the age of the universe, the cluster baryon fraction, and large scale structure. Also shown are the best-fit values from CMB fits labeled by their date of publication (WMAP in orange; Planck in yellow). These follow the green line of constant ΩmH03; combinations of parameters along the line are tolerable but regions away from it are strongly excluded.

There is lots to be said here. First, note the scale. As the accuracy of data have improved, it has become possible to zoom in. My version of the figure is a wee postage stamp on that of Ostriker & Steinhardt. Nevertheless, the concordance region is in pretty much the same spot. Not exactly, of course; the biggest thing that has changed is that the age constraint is now completely incompatible with an open universe, so I haven’t bothered depicting it. Indeed, for the illustrated Hubble constant, the Hubble time (the age of a completely empty, “coasting” universe) is 13.4 Gyr. This is consistent with the illustrated age (13.80 ± 0.75 Gyr) only for Ωm ≈ 0, which is far off the left edge of the plot.

Second, the CMB best-fit values follow a line of constant ΩmH03. This is a deep trench in χ2 space. The region outside this trench is strongly excluded – it’s kinda the grand canyon of cosmology. Even a little off, and you’re standing on the rim looking a long way down, knowing that a much better fit is only a short step away. Once you’re in the valley of χ2, one must hunt along its bottom to find the true minimum. In the mid-`00s, a decade after Ostriker & Steinhardt, the best fit fell smack in the middle of the concordance region defined by completely independent data. It was this additional concordance that impressed me most, more than the detailed CMB fits themselves. This convinced the vast majority of scientists practicing in the field that it had to be LCDM and could only be LCDM and nothing but LCDM.

Since that time, the best-fit CMB value has wandered down the trench, away from the concordance region. These are the results that changed, not everything else. This temporal variation suggests a systematic in the interpretation of the CMB data rather than in the local distance scale.

I recall being at a conference (the Bright & Dark Universe in Naples in 2017) when the latest Planck results were announced. There was a palpable sense in the audience of having been whacked by a blunt object, like walking into a closed door you thought was open. We’d been doing precision cosmology for a long time and had settled on an answer informed by lots of independent lines of evidence, but they were telling us the One True answer was off over there. Not crazy far, but not consistent with the concordance we had come to expect. Worse, they had these crazy tiny error bars – not only were they getting an answer outside the concordance region, it was in tension with pretty much everything else. Not strong tension, but enough to make us all uncomfortable if not outright object. Indeed, there was a definite vibe that people were afraid to object. Not terrified, but nervous. Worried about being on the wrong side of the community. I get it. I know a lot about that.

People are remarkably talented at refashioning the past. Over the past five years, the Planck best-fit parameters have come to be synonymous with LCDM: all else is moot. Young scientists can be forgiven for not realizing it was ever otherwise, just as they might have been taught that cosmic acceleration was discovered by the supernova experiments totally out of the blue. These are convenient oversimplifications that elide so many pertinent events as to be tantamount to gaslighting. We refashion the past until there was never a serious controversy, then it seems strange that some of us think there still is. Sorry, not so fast, there definitely is: if you use the Planck value of the Hubble constant to estimate distances to local galaxies, you will get it wrong%, along with all distance-dependent quantities.

I’m old enough to remember a time when there was a factor of two uncertainty in the Hubble constant (50 vs. 1000) and the age constraint was the most accurate one in this plot. Thanks to genuine progress, the Hubble constant is now the more precise. Consequently, of all the data one could plot above, this is the choice that matters most to where the concordance region falls. If I adopt our own estimate (H0 = 75.1 ± 2.3 km/s/Mpc), then the concordance band gets wider and slides up a little but is basically the same as above. If instead I adopt the lowest highly accurate value, H0 = 69.8 ± 0.8 km/s/Mpc, the window slides down, but not enough to be consistent with the Planck results. Indeed, it stays to the left of the CMB constraint, becoming inconsistent with the mass density as well as the expansion rate.

Dang it, now I want to make that plot. Processing… OK, here it is:

As above, but with a lower measurement of H0. Only the range of statistical uncertainty is illustrated as a systematic uncertainty corresponds to a calibration error that slides H0 up and down – i.e., the exact situation being illustrated relative to the figure above. These two plots illustrate the range of outcomes that are possible from slightly discordant direct modern measurements of the Hubble constant; it is hard to go lower. Doing so doesn’t really help as it would just shift the tension from H0 to Ωm.

Yes, as I expected: the allowed range slides down but remains to the left of the green line. It is less inconsistent with the Planck H0, but that isn’t the only thing that matters. It is also inconsistent with the matter density. Indeed, it misses the CMB-allowed trench entirely. There is no allowed FLRW universe here.

These are only two parameters. Though arguably the most important, there are others, all of which matter to CMB fits. These are difficult to visualize simultaneously. We could, for starters, plot the baryon density as a third axis. If we did so, the concordance region would become a 3D object. It would also get squeezed, depending on what we think the baryon density actually is. Even restricting ourselves to the above-plotted constraints, there is some tension between the cluster baryon fraction and large scale structure constraint along the new third axis. I’m sure I could find in the literature more or less consistent values; this way the madness of cherry-picking lies.

There are many other constraints that could be added here. I’ve tried to stay consistent with the spirit of the original plot without making it illegible by overburdening it with lots and lots of data that all say pretty much the same thing. Nor do I wish to engage in cherry-picking. There are so many results out there that I’m sure one could find some combination that slides the allowed box this way or that – but only a little.

Whenever I’ve taught cosmology, I’ve made it a class exercise$ to investigate diagrams like this, with each student choosing an observational constraint to explore and champion. as a result, I’ve seen many variations on the above plots over the years, but since I first taught it in 1999 they’ve always been consistent with pretty much the same concordance region. It often happens that there is no concordance region; there are so many constraints that when you put them all together, nothing is left. We then debate which results to believe, or not, a process that has always been a part of the practice of cosmology.

We have painted ourselves into a corner. The usual interpretation is that we have painted ourselves into the correct corner: we live in this strange LCDM universe. It is also possible that there really is nothing left, the concordance window is closed, and we’ve falsified FLRW cosmology. That is a fate most fear to contemplate, and it seems less likely than mistakes in some discordant results, so we inevitably go down the path of cognitive dissonance, giving more credence to results that are consistent with our favorite set of LCDM parameters and less to those that do not. This is widely done without contemplating the possibility that the weird FLRW parameters we’ve ended up with are weird because they are just an approximation to some deeper theory.

So, as 2023 winds to an end, we [still] know pretty well what the parameters of cosmology are. While the tension between H0 = 67 and 73 km/s/Mpc is real, it seems like small beans compared to the successful isolation of a narrow concordance window. Sure beats arguing between 50 and 100! Even deciding which concordance window is right seems like a small matter compared to the deeper issues raised by LCDM: what is the cold dark matter? Does it really exist, or is it just a mythical entity we’ve invented for the convenient calculation of cosmic quantities? What the heck do we even mean by Lambda? Does the whole picture hang together so well that it must be correct? Or can it be falsified? Has it already been? How do we decide?

I’m sure we’ll be arguing over these questions for a long time to come.

+Structure formation is often depicted as a great success of cosmology, but it was the failure of the previous standard model, SCDM, to predict enough structure on large scales that led to its demise and its replacement by LCDM, which now faces a similar problem. The observer’s experience has consistently been that there is more structure in place earlier and on larger scales than had been anticipated before its observation.

*I believe in giving theories credit where credit is due. Putting on a cosmologist’s hat, the location of the first peak was a great success of LCDM. It was the amplitude of the second peak that came as a great surprise – unless you can take off the cosmology hat and don a MOND hat – then it was predicted. What is surprising from that perspective is the amplitude of the third peak, which makes more sense in LCDM. It seems impossible to some people that I can wear both hats without my head exploding, so they seem to simply assume I don’t think about it from their perspective when in reality it is the other way around.

%As adjudicated by galaxies with distances known from direct measurements provided by Cepheids or the tip of the red giant branch or surface brightness fluctuations or geometric methods, etc., etc., etc.

$This is a great exercise, but only works if CMB results are excluded. There has to be some narrative suspense: will the various disparate lines of evidence indeed line up? Since CMB fits constrain all parameters simultaneously, and brook no dissent, they suck the joy away from everything else in the sky and drain all interest in the debate.

Global climate basics

Global climate basics

Last time, I expressed extreme disappointment that fossil fuel executives had any role in leading the climate meeting COP28. This is a classic example of putting the the fox in charge of the hen house. The issue is easily summed up:

It’s difficult to get a man to understand something when his salary depends on not understanding it.

Upton Sinclair

Setting aside economic self-interest and other human foibles, it is clear from the comments that the science is not as clear to everyone as it is to me. That’s fair; I’ve followed this subject for half a lifetime, and it is closely related to my own field.

Stars are fusion reactors surrounded by big balls of gas; understanding how they work was a major triumph of 20th century astrophysics. We understand these things. Planetary atmospheres are also balls of gas; there is some rich physics there but the problem is in many ways simpler when they aren’t acting as the container for a giant fusion reactor. We understand these things. The atmospheres of Venus and Mars come up when teaching Astronomy 101, these planets represent opposite extremes of climate change run amok. From that perspective, Earth is a nice problem to have. We understand these things.

It is easy to get distracted by irrelevant details. No climate model is ever perfect, but that doesn’t mean we don’t understand what’s going on. The issue is basic physics, which has been understood for well over a century. Not only is the physics incredibly clear; so too is the need to take collective action to ameliorate the effects of climate change. The latter has itself been clear since 1990+ at least.

The temperature of a planet is the balance between heating by the sun during the day and re-radiation of that heat at night. The effectiveness of both depend on the properties of the planet. What is the albedo? That is, how much of the incident radiation is reflected into space without heating? Once heated, how efficiently can the heated surface cool by radiating energy to space?

If a planet has no atmosphere, it is a straightforward calculation to find the balance point. If the Earth had no atmosphere, the average temperature would be much colder than it is, about -18 C. Thankfully, we have an atmosphere. There is a natural greenhouse effect – nothing to do with human activity – that makes the actual average temperature more like +15 C. I, for one, am grateful for this. It also means that changing the composition of the atmosphere will change the balance point.

The bulk of Earth’s atmosphere is nitrogen and oxygen. These gases are transparent to the incoming optical radiation from the sun that heats the surface. They are also transparent to the outgoing infrared radiation that cools the surface. Despite composing the bulk of the atmosphere, they play basically zero role in the greenhouse effect. As far as climate goes, having only these gases in the atmosphere returns the same answer as the zero atmosphere case.

The natural greenhouse effect is entirely due to trace gases like water vapor and carbon dioxide. I note this because one reasonable-sounding falsehood that gets repeated a lot is that CO2 is a trace gas, so it can’t possibly make a difference. That’s like saying adding a small dash of poison to a beverage isn’t dangerous. Or that it makes no difference to draw a shade over a window. The shade may be much thinner than the glass of the window, but unlike the transparent glass, the shade is opaque. That’s the property greenhouse gases provide, even in trace quantities: they are opaque to the infrared radiation that is trying to cool the surface by escaping to space.

If we looked down on the Earth with eyes that saw in the infrared part of the spectrum where greenhouse gases trap heat, we’d wouldn’t see the surface of the planet. Instead, we’d see a hazy ball: the effective altitude in the atmosphere from which infrared radiation can escape to space. This isn’t a solid surface any more than the edge of a cloud is – to you and me. To the photons seeking escape, it is an effective barrier. Some don’t make it out.

The greenhouse gases are like a fog bank that has to be traversed before the heat carried by the infrared radiation can escape into space. If we add greenhouse gases to the atmosphere, it makes the fog bank thicker, effectively trapping more heat. At a basic level, the issue is that simple. The science is entirely settled; no one seriously* debates this. It has been known for over a century.

Article published in 1912 in the Braidwood Dispatch and Mining Journal, via the National Library of Australia

The leading greenhouse gas in Earth’s atmosphere is water vapor. You don’t need a fancy scientific instrument to detect this effect, just your own senses. High humidity leads to hot, sultry nights while low humidity allows rapid cooling. To feel this, visit a humid place like New Orleans and an arid one like the desert of the US west. These places feel very different at night even when their daytime temperatures are similar. The humid place cannot cool effectively because of the greenhouse effect provided by water vapor, and nighttime temperatures can remain unpleasantly high. In the dry desert, the temperature drops like a rock as soon as the sun sets, and it can get rather chilly even if it was baking hot all day long. I’ve personally experienced both conditions many times; the difference is stark and obvious.

The amount of water vapor the atmosphere can hold is a function of temperature, but on bulk it is always less than half a percent. That trace gas is nevertheless 100% of what you care about in the morning weather forecast, as it leads to rain, snow, sleet, hail, cloud cover, and all the other weather phenomena that makes life near the triple point of water interesting. Indeed, clouds increase the albedo of the planet, reflecting some of the incoming solar radiation, so water in the atmosphere prevents some heating as well as helping to retain warmth once heated. This is pretty much in balance, as the limit on how much water vapor the atmosphere can hold means that equilibrium is achieved on a short time scale: too much humidity, and it rains. The sources and sinks of H2O in the atmosphere balance out on short timescales readily perceptible to humans. It’s what we call weather.

The next most important greenhouse gas is CO2. That too has a natural level with sources and sinks. The issue that induces human-caused climate change is the extra CO2 we put in the atmosphere by burning coal, oil, etc. for the energy it provides. This does not balance out on a short timescale, so there is a cumulative effect on the climate, as anticipated in 1912.

Producing energy is a good thing; no one here is advocating that we stop doing this add return to living like cavemen. Heck, even cavemen had an environmental impact: they burned enough wood to blacken many a cave roof. Human activity has always left a mark; the problem today is that there are 8+ billion of us doing a lot more than making campfires. That adds up to a measurable change in the composition of the atmosphere.

The natural pre-industrial level of CO2 was about 277 parts per million (ppm). Here is a graph of the CO2 content of the atmosphere over the past few centuries, extending to back before the onset of the industrial revolution when our collective experiment in atmospheric physics got going. We know how much carbon we’ve burned (that’s economic activity with profits and receipts, we know this number quite well) and we can measure how much CO2 is in the atmosphere directly. They ramp up together.

Mass of CO2 in the atmosphere (in gigatonnes) since 1700. Modern measurements (blue line) come from the Mauna Loa observatory courtesy of the NOAA Global Monitoring Laboratory; older measurements (black line) come from the Law Dome Antarctic ice core data. The red line is the cumulative CO2 added to the atmosphere by human activity. I’ve added the pre-industrial value to this in the upper (thin) red line to show how it compares with the measured CO2 content.

There is lots that can be said about this plot. Just some basic points: the amount of CO2 in the atmosphere has gone up as we have burned coal and oil to generate energy. We have measurably changed the composition of the atmosphere we all breathe. The current CO2 content of the atmosphere is 424 ppm, which is much larger than the pre-industrial level of 277 ppm. That by itself ought to give one pause: we are conducting an uncontrolled experiment in atmospheric physics on a global scale. That seems like a bad idea, even if we didn’t understand heat propagation in the atmosphere, which we do.

Not only has the amount of CO2 in the atmosphere increased as we’ve burned things, it is accumulating. There are natural sinks, which is why the extra amount of CO2 in the atmosphere is less than what we’ve added: not all of it sticks around. Much of it has been absorbed by the ocean, which is acidifying as a result. But lots of CO2 persists in the atmosphere: the timescale for it to “rain out” is much longer than for water. It will take many decades and probably centuries to restore anything resembling equilibrium. We aren’t just adding CO2 to the atmosphere, we’re making a long-term investment in having it there. Future generations will have to contend with the consequences of what we’ve already done.

What have we already done? I’ve outlined the basic physics; let’s now check the predictions of one of the earliest forecasts. This is from a 1982 report generated by Exxon scientists:

Forecast atmospheric CO2 content of the atmosphere (upper line; left axis) and the corresponding temperature change (lower line; right axis). I’ve added current values for both CO2 (green) and the temperature anomaly (orange). Looks like they pretty much nailed it. Note also that the null hypothesis of no climate change, i.e., a constant temperature with increasing CO2 content, is strongly rejected.

The study made over forty years ago accurately forecast where we are today. These predictions have repeatedly been corroborated. Some models may miss minor details here and there, but the basic picture is crystal clear. Anyone who tells you otherwise has some fossil fuels to sell.

Enough has been written on this subject; I won’t suggest solutions nor delve into likely impacts. But there is absolutely no doubt that climate change is real and that we caused it. None. That this simple, plain fact is not obvious to everyone at this point is a credit to the power of disinformation and propaganda. The best course forward from here is debatable. Pretending like it isn’t a problem is straight-up reality denial.

+It has become a trope of wingnut politics in the U.S. that scientists only say climate change is real so they can get research grants. That’s ridiculous on many levels. One reason that such grants exist is that right wing politicians asked for more research. This was a delaying tactic employed in the early 1990s by then-president and oil magnate George H. W. Bush.

Fresh off the success of regulatory repair to the ozone hole problem in the late 1980s, it was reasonable to hope that we could start tackling the threat of climate change. This was a much bigger problem encompassing a broader range of human activity, but the basic science is far simpler than the atmospheric chemistry that threatened ozone. Industries that didn’t want to be regulated whined about that as usual, but no one seriously questioned the science. After the usual wailing and gnashing of teeth, appropriate regulatory action was taken, and it worked.

When it came to doing the same thing with the oil industry when an oil baron was president, well, harrumph harrumph, more research was needed. The first Bush was a Republican, but he wasn’t a backwards science-denying goon, so he offered to fund more research. It was an obvious delaying tactic, but the argument in favor of it was to make the case more convincing. So the science community was like, sure, the basic answer is already clear, but there are things that we could understand better, so we’ll do more research if that helps you to also understand the problem. But it hasn’t helped people who don’t want to understand to do so, and never will, because the problem is with them, not with the science. So now, thirty years on from Bush I, the same political party that demanded more research be done now routinely attacks scientists for doing the research they asked scientists to do.

Sorry, not sorry: just because you don’t like the answer science gives doesn’t make it wrong. It is well past time for climate denying snowflakes to stop having emotional meltdowns and grow up already.

*Sometimes it is asserted that the opacity of CO2 is already saturated, so adding more doesn’t matter. Yes on one, no on two. Even at saturation we can still make the fog bank thicker by adding more CO2 – just ask Venus. Indeed, we’re dang lucky that the CO2 bands are already saturated; if not for that, the response of the climate to adding as much CO2 as we have would be much stronger. If these features were not saturated the response would be linear instead of incremental, so the temperature would have already increased by about an extra 7 C, not the mere 1 C we’ve so far** accomplished.

**Just how much we’ve added depends on how you define “before.” Modern studies often seem to adopt the average temperature measured between 1980 and 2000, presumably because the data with which to do so are very good. This gives an increase since then around 1.1 C, which is a remarkable amount of growth in just a few decades: we’ve tipped the climate system out of anything resembling equilibrium hard and fast. Of course, the impact of human activity was already palpable before 1980, so the total change since the industrial revolution is closer to 1.5 C. We’re not quite to that arbitrary threshold yet, but I see no way to avoid blowing past it. Talk of doing so is predicated on giving us a half degree mulligan by defining “average” during a period that is not average. So if you think portrayals of the problem are exaggerated, it is actually already worse than generally depicted.

Cop28 president not even trying to hide his obvious bias

In 1986, I was a grad student at Princeton, working in the atomic physics lab of Will Happer. It was at a department colloquium that I first heard a science talk that raised serious concerns about our use of fossil fuels potentially impacting the climate. This was not received well.

People asked all sorts of questions, with much of the discussion revolving around feedback effects. Perhaps warmer weather from CO2 will result in higher humidity, making more clouds*, and reflecting more sunlight into space. It does not. What about ice cover? This is actually a positive feedback – as the globe warms, ice coverage is replaced by darker surfaces, leading to more absorption of the incident solar radiation. And so on.

I thought the speaker did a creditable job of answering the concerns raised, repeatedly making the point that most feedback effects would make things worse, not better. It was entirely new to me at the time; I didn’t have any context to judge the relative merits of the discussion. Prof. Happer is one of those remarkable people who seems to know a lot about everything. So, as I related before, I asked him. His immediate and harsh retort was

“We can’t turn off the wheels of industry, and go back to living like cavemen.”

I relate this story again because the same language comes up today in a story I saw in the Guardian:

The president of Cop28, Sultan Al Jaber, has claimed there is “no science” indicating that a phase-out of fossil fuels is needed** to restrict global heating to 1.5C, the Guardian and the Centre for Climate Reporting can reveal.

Al Jaber also said a phase-out of fossil fuels would not allow sustainable development “unless you want to take the world back into caves”.

COP23 article, 3 December 2023

This is exactly the same solution aversion that Happer displayed, using exactly the same language. It doesn’t address the actual question. It leaps ahead to the worst conceivable consequence, doesn’t like it, and so reverts to reality denial: We don’t want that to happen, so the evidence must be wrong!

We humans excel at reality-denial. It is not helpful. Rather than starting to deal with the problem of climate change thirty years ago – the science was already crystal clear by then – we’ve dug ourselves a much deeper hole. That’s not to say we should abandon all hope and revert to living in caves, but we do need to take serious and rapid steps to reform the ways in which we generate power. It is doing more of the same that risks sending us back into caves.

The quoted reaction to this assertion in the story is predictably tepid. The quote in the Guardian is “The comments were `incredibly concerning’ and `verging on climate denial’, scientists said.” As a scientist who is not directly involved with dealing with these people, let me be more blunt:

ARE YOU FUCKING KIDDING ME?

Al Jaber’s attitude isn’t verging on climate denial, it is the archetype of climate denial. Literally the same thing that climate deniers said in the 1980s. It expresses an attitude that was clearly wrong and dangerously backwards by the early 1990s. And this guy is the president of COP28? I say again

ARE YOU FUCKING KIDDING ME?

And who is this guy? The Guardian reports “Al Jaber is also the chief executive of the United Arab Emirates’ state oil company, Adnoc, which many observers see as a serious conflict of interest.” A conflict of interest? Really? Do you think? Again I say

ARE YOU FUCKING KIDDING ME?

This is obviously a conflict of interest, of the worst sort. His personal wealth, and the sovereign wealth of his nation, is entirely based on the production and sale of fossil fuels. Mitigating climate change means reducing our consumption of fossil fuels, which is a direct threat to the economic interests he represents. Talk about putting the fox in charge of the hen house.

I am impressed by how the moneyed interests have managed to slither their way into positions of consequence on discussions in which they have an obvious conflict. I guess money always finds a way in. But can we please stop being so polite that we fail to call out obvious bullshit wherever it crops up? It seems to be spreading at the rate of made-up conspiracy nonsense on that site formerly known as Twitter. We should stop putting up with it already.

*Ironically, SO2 pollution from ocean-going vessels does have this effect, and as these emissions have been cleaned up, we can see the effect in global temperatures. This is not to advocate for SO2 pollution! though injecting aerosols like SO2 into the stratosphere is one of geoengineering approaches that gets discussed. Before going down that path, the obvious first step is to stop pouring petrol on the fire by continuing to add CO2 to the atmosphere.

**We’re already committed to 1.5C. I see no conceivable way that we can curb emissions fast enough to avoid that. So I guess this statement is true, from a certain point of view – that of a liar. A less misleading statement would be that a phase-out of fossil fuels is necessary to prevent things from getting much worse than forecast for the 1.5C threshold.

A post in which some value judgements are made about the situation with wide binaries

A post in which some value judgements are made about the situation with wide binaries

I have tried very hard to remain objective and even handed, but I find that I weary of the wide binary debate. I don’t know what the right answer will turn out to be. But I do have opinions.

For starters, it is a big Galaxy. There is just too much to know. When I wrote about the Milky Way earlier this year, the idea was to set up an expectation value for wide binaries in the solar neighborhood. That devolved into at least eight other posts on the Milky Way itself, because our Galaxy is too damn interesting, and has its own controversies. So it occurs to me that I never really got on with the regularly scheduled program.

In my assessment, the radial acceleration at the solar circle is 2.2 x 10-10 m/s/s, which in terms of the MOND acceleration scale is 1.8 a0. We live on the Newtonian side of the transition to the MOND regime. The ideal place to test MOND with wide binaries would be the deep MOND regime, well below a0. That is in a part of the Galaxy that is far, far away, and not currently accessible to us. What is accessible are wide binaries in the solar neighborhood (within 250 pc, about 1% of the Galaxy’s radius) as mapped by Gaia. Locally, the MOND effect is modest, but nonzero. We’re close enough to the transition for there to be a small, detectable effect.

Local binaries that are widely separated enough for their internal acceleration to drop below a0 find themselves in the regime dominated by the field of the rest of the Galaxy and subject to the so-called External Field Effect (EFE). This situation is illustrated in the lower right panel below.

Mass estimators in different regimes of acceleration. The top row illustrates pure Newtonian (left) and MOND (right) regimes. The bottom row illustrates the case of small systems embedded in larger systems. A low acceleration system embedded in a Newtonian external field is Newtonian (left) while a very low acceleration system embedded in a merely low acceleration system is quasi-Newtonian (right). Wide binaries fall in the last category.

Intriguingly, orbits in the EFE regime remain Keplerian. The rotation curves of nearby binaries, if you could map them, are not expected to be flat in MOND. They should, however, experience enhanced speeds, with a boost to the effective value of Newton’s constant: G → γG. The value of γ depends on the sum of internal and external acceleration as well as the shape of the interpolation function when near a0. That’s one reason to prefer to do this experiment in the deep MOND regime, where the shape of the interpolation function doesn’t matter. But that’s not where we live. For Galactic data and viable possibilities* for the interpolation function, a reasonable expectation value is γ = 1.4 ± 0.1. This is what the wide binary papers attempt to measure. So, what do they find?

Hernandez et al. find γ = 1.0±0.1 for 466 close binaries with 2D separations less than 0.01 pc (about 2000 AU) and γ = 1.5±0.2 for 108 wide binaries with 2D separations greater than 0.01 pc. A purely Newtonian result (γ = 1) is recovered in the high acceleration regime of relatively close binaries where this is expected to be the case. For wider binaries, one finds a boost value consistent with the prediction of MOND and differing from Newton with modest significance (2.6σ).

Chae reports+ γ = 1.49(+0.21/-0.19) for 2,463 “pure” binaries in the low acceleration regime, consistent with his earlier result γ = 1.43±0.06 for 26,615 wide binaries. The larger numbers make the formal error smaller, hence a formally more significant departure from Newton. Many of these binaries are impure in the sense of being triples with one member being itself a close binary as discussed previously, an effect that has to be modeled in large samples. The point of the smaller samples is to select true binaries so that this modeling is unnecessary. For his smaller pure binary sample, Chae finds a smooth transition from γ ≈ 1 at high acceleration (10-8 m/s/s ≈ 100a0) through γ ≈ 1.11 around 7a0 to γ ≈ 1.49 at local Galactic saturation (1.8 a0).

Banik et al. use a slightly different language. Translating, they find γ = 1 at high confidence (16σ)$ from 8,611 wide binaries with separations from 2,000 to 30,000 AU. Newtonian behavior persists at all scales and accelerations; they find no significant deviations from γ = 1 anywhere. Note that despite going out very far, to 30,000 AU, they do not reach especially low accelerations because the EFE of the Galaxy is effectively constant in the solar neighborhood. There is no getting away from the Galaxy’s 1.8 a0. They also do not reach particularly high, purely Newtonian accelerations: 2,000 AU is in the transition regime where MOND effects are perceptible.

Here is Fig. 11 from Banik et al., the key figure Dr. Banik was advocating in his comments to the previous post:

Fig. 11 of Banik et al. shows the median dimensionless characteristic velocity as a function of dimensionless binary separation for several bins of the data (solid lines). The predicted MOND effect increases with separation until saturating in the Galactic field (dashed lines). This is not seen in most of the data, with only a hint in the highest velocity bin that represents only a few percent of the data.

A flat line in this plot indicates no boost in velocity with diminishing acceleration, so one can clearly see the source of the claim that Newton works better than MOND. There is little indication that the velocity increases at wider separations. Effectively, γ ≈ 1 pretty much everywhere.

Both Chae and Hernandez have pointed out that the lack of a constraint on the high acceleration Newtonian regime is problematic. Orbits are Keplerian in the quasi-Newtonian regime, so the behavior looks Newtonian. Lacking an anchor in the high acceleration regime, it is conceivable that the analysis of Banik is detecting the predicted MOND quasi-Newtonian behavior and defining it to be purely Newtonian. It’s just a modest offset in qualitatively similar behavior. In this context, it is worth noting that Chae and Hernandez independently measure γ ≈ 1 at high acceleration as well as γ ≈ 1.5 at low acceleration: they [claim to] detect the difference between these regimes in a way Banik does not probe.

Now let’s look at the plot that gave me the heebie-jeebies with data on it, Banik et al.’s Fig. 12:

The left two panels of Fig. 12 from Banik et al. showing the probability of observing a particular dimensionless velocity in two bins of radial separation: 2,000 to 3,000 AU (top) and 5,000 to 12,000 AU (bottom). The histograms are the Gaia data. The black lines show the Newtonian prediction while the blue lines show that of MOND. These predictions depend on many things besides the underlying theory, sampling over many astrophysical complications like the distribution of stellar masses, orbital orientations to the line of sight, orbital phase, orbital eccentricity, the close binary fraction, and probably other things that I don’t instantly recall.

One can see the basis of the concern. At high acceleration, the prediction of Newton and MOND are identical. The top bin is the closest we get to that, yet there is a clear difference in the predictions. This bin is in the transition region; there is no bin at sufficiently high acceleration for the predictions to align and provide the self-calibration that both Chae and Hernandez independently exploit.

Looking at the data in the top panel, it clearly agrees better with the Newtonian prediction. I can believe that; what concerns me is the lack grounding at still higher acceleration where the black and blue lines should coincide. I do not have a sufficiently clear understanding of all the machinations (and their inevitable foibles) that go into the predicted lines to trust that this constitutes a definitive test.

Looking at the data in the bottom panel, it clearly agrees better with the prediction of MOND. The histogram of the data follows the blue line of MOND more closely than the Newtonian black line. This is so obvious that I wondered if the colors were wrong – maybe there had been some inadvertent switcheroo in the line color in the plotting code. Apparently not, as this point is addressed in the text of Banik et al.: [in the bottom panel,] “MOND performs somewhat better in a handful of pixels around the peak region…” Yes. Yes it does. That’s… a really weird way of putting it. They go on to say “…though given the uncertainties, the Newtonian model is not that far off.” One could just as well say that about MOND in the top panel. Just looking at this figure, one might conclude that they have detected MOND at large separations.

One of the things that gives me the heebie-jeebies about this figure is that there isn’t much difference in the location of the peaks of the distributions. Some, yes, but not much: Newton and MOND apparently predict very nearly the same typical velocity. Yet that is what Fig. 11 traces: the typical (median) normalized velocity. That only tells a tiny bit of the story that is in Fig. 12, and does not appear to be a particularly sensitive indicator of the effect we’re testing for.

Returning to the matter of statistics, the attentive reader might have noted that I have not said much about the number of binaries included in each analysis. These range from a few hundred to many thousands to tens of thousands. More is better, right?

In this case, I think not. There is always a tension between data quality and quantity. Quantity helps with the statistics, but only so long as there is a signal to be dug out. At some point, it becomes a matter of garbage in, garbage out. In this respect, I am inclined to agree with Ernest Rutherford:

If your experiment requires statistics, you ought to have done a better experiment.

Ernest Rutherford$

I suspect that we’re squeezing the stone of statistics too hard here. When we do this, we get the appearance of a signal when really we’re just grinding metal. I am reminded that any time we do a big experiment like this (dark matter searches are a great example), the first thing we learn about are all the false signals we didn’t anticipate. That happens no matter how well we construct the experiment, and I give Banik et al. credit for planning this out ahead of time. That doesn’t guarantee that everything comes out right on the first attempt. There is just so much junk that the universe can and does throw at us that it is easy to imagine that the samples with large numbers of binaries bring with them too much junk (e.g., false binaries). If the fraction of junk is high, then it will look like junk at all scales – there will be no trend with increasing separation even if there is a signal buried in junk.

Consequently, I am at present inclined to trust more the super-clean sample of Hernandez – the high quality binaries where there is a chance that we’re actually measuring what we want to measure. There are only a few hundred such binaries, so the statistical confidence is modest (2.6σ). I worry that in setting the highly restrictive standards necessary to select the best binaries that we might unintentionally omit objects that could change the answer. But at least there is some confidence that these are real binaries that stands above the accumulation of all the gratuitously enormous amount of junk the universe has to throw our way.

I hope the principal scientists can come to agreement about what the data show and not just wind up having the same argument over and over forever more. That’s what usually happens. I ask all parties to remember that it is important to retain the ability to change one’s mind. All of them have demonstrated the ability to do this previously, and somebody will need to do it again.

*In principle, one might also hope to distinguish between specific theories of MOND. Examples of modified gravity like AQUAL and QUMOND give slightly different predictions. To be able to do this seems… optimistic at this point.

In modified inertia theories, the interpolation function is a chimera that depends on each orbital trajectory, so its effective realization may differ between the nearly circular motions of rotation curves and eccentric wide binaries. If so, the interpolation function defined by external galaxies may not be relevant to the problem. Ultimately, we need a theory that automatically results in the MONDian phenomenology in galaxies. Whatever it is wide binaries are doing should help inform this theory development as well as test alternatives and hopefully exclude some of them.

+The work of Chae has gone through some revisions in response to a referee, but the basic findings are unchanged. Having read an earlier version, I appreciate the clarity provided by the additions: this is a case where the refereeing process was beneficial. (I was not the referee of this or any of these papers. Editors keep me busy enough as it is, thank you very much.)

$Outside of long-established observations like the value of Gauss’s constant, there is no such thing as 16σ confidence in astronomy. The tails of real probability distributions are never as tiny as a pure Gaussian. This is a remarkably naive assertion.

Full speed in reverse!

Full speed in reverse!

People have been asking me about comments in a recent video by Sabine Hossenfelder. I have not watched it, but the quote I’m asked about is “the higher the uncertainty of the data, the better MOND seems to work” with the implication that this might mean that MOND is a systematic artifact of data interpretation. I believe, because they consulted me about it, that the origin of this claim emerged from recent work by Sabine’s student Maria Khelashvili on fitting the SPARC data.

Let me address the point about data interpretation first. Fitting the SPARC data had exactly nothing to do with attracting my attention to MOND. Detailed MOND fits to these data are not particularly important in the overall scheme of these things as I’ll discuss in excruciating detail below. Indeed, these data didn’t even exist until relatively recently.

It may, at this juncture in time, surprise some readers to learn that I was once a strong advocate for cold dark matter. I was, like many of its current advocates, rather derisive of alternatives, the most prominent at the time being baryonic dark matter. What attracted my attention to MOND was that it made a priori predictions that were corroborated, quite unexpectedly, in my data for low surface brightness galaxies. These results were surprising in terms of dark matter then and to this day remain difficult to understand. After a lot of struggle to save dark matter, I realized that the best we could hope to do with dark matter was to contrive a model that reproduced after the fact what MOND had predicted a priori. That can never be satisfactory.

So – I changed my mind. I admitted that I had been wrong to be so completely sure that the solution to the missing mass problem had to be some new form of non-baryonic dark matter. It was not easy to accept this possibility. It required lengthy and tremendous effort to admit that Milgrom had got right something that the rest of us had got wrong. But he had – his predictions came true, so what was I supposed to say? That he was wrong?

Perhaps I am wrong to take MOND seriously? I would love to be able to honestly say it is wrong so I can stop having this argument over and over. I’ve stipulated the conditions whereby I would change my mind to again believe that dark matter is indeed the better option. These conditions have not been met. Few dark matter advocates have answered the challenge to stipulate what could change their minds.

People seem to have become obsessed with making fits to data. That’s great, but it is not fundamental. Making a priori predictions is fundamental, and has nothing to do with fitting data. By construction, the prediction comes before the data. Perhaps this is one way to distinguish between incremental and revolutionary science. Fitting data is incremental science that seeks the best version of an accepted paradigm. Successful predictions are the hallmark of revolutionary science that make one take notice and say, hey, maybe something entirely different is going on.

One of the predictions of MOND is that the RAR should exist. It was not expected in dark matter. As a quick review of the history, here is the RAR as it was known in 2004 and now (as of 2016):

The radial acceleration relation constructed from data available in 2004 and that from 2016.

The big improvement provided by SPARC was a uniform estimate of the stellar mass surface density of galaxies based on Spitzer near-infrared data. These are what are used to construct the x-axis: gbar is what Newton predicts for the observed mass distribution. SPARC was a vast improvement over the optical data we had previously, to the point that the intrinsic scatter is negligibly small: the observed scatter can be attributed to the various uncertainties and the expected scatter in stellar mass-to-light ratios. The latter never goes away, but did turn out to be at the low end of the range we expected. It could easily have looked worse, as it did in 2004, even if the underlying physical relation was perfect.

Negligibly small intrinsic scatter is the best one can hope to find. The issue now is the fit quality to individual galaxies (not just the group plot above). We already know MOND fits rotation curve data. The claim that appears in Dr. Hossenfelder’s video boils down to dark matter providing better fits. This would be important if it told us something about nature. It does not. All it teaches us about is the hazards of fitting data for which the errors are not well behaved.

While SPARC provides a robust estimate of gbar, gobs is based on a heterogeneous set of rotation curves drawn from a literature spanning decades. The error bars on these rotation curves have not been estimated in a uniform way, so we cannot blindly fit the data with our favorite software tool and expect that to teach us something about physical reality. I find myself having to say this to physicists over and over and over and over and over again: you cannot trust astronomical error bars to behave as Gaussian random variables the way one would like and expect in a controlled laboratory setting.

Astronomy is not conducted in a controlled laboratory. It is an observational science. We cannot put the entire universe in a box and control all the variables. We can hope to improve the data and approach this ideal, but right now we’re nowhere near it. These fitting analyses assume that we are.

Screw it. I really am sick of explaining this over and over, so I’m just going to cut & paste verbatim what I told Hossenfelder & Khelashvili by email when they asked. This is not the first time I’ve written an email like this, and I’m sure it won’t be the last.

Excruciating details: what I said to Hossenfelder & Khelashvili about the perils of rotation curve fitting on 22 September 2023 in response for their request for comments on the draft of the relevant paper:

First, the work of Desmond is a good place to look for an opinion independent of mine. 

Second, in my experience, the fit quality you find is what I’ve found before: DM halos with a constant density core consistently give the best fits in terms of chi^2, then MOND, then NFW. The success of cored DM halos happens because it is an extremely flexible fitting function: the core radius and core density can be traded off to fit any dog’s leg, and is highly degenerate with the stellar M*/L. NFW works less well because it has a less flexible shape. But both work because they have more parameters [than MOND].

Third, statistics will not save us here. I once hoped that the BIC would sort this out, but having gone down that road, I believe the BIC does not penalize models sufficiently for adding free parameters. You allude to this at the end of section 3.2. When you go from MOND (with fixed a0 it has only one parameter, M*/L, to fit to account for everything) to a dark matter halo (which has at a minimum 3 parameters: M*/L plus two to describe the halo) then you gain an enormous amount of freedom – the volume of possible parameter space grows enormously. But the BIC just says if you had 20 degrees of freedom before, now you have 22. That does not remotely represent the amount of flexibility that represents: some free parameters are more equal than others. MOND fits and DM halo fits are not the same beast; we can’t compare them this way any more than we can compare apples and snails. 

Worse, to do this right requires that the uncertainties be real random errors. They are not. SPARC provides homogeneous mass models based on near-IR observations of the stellar mass distribution. Those should be OK to the extent that near-IR light == stellar mass. That is a decent mapping, but not perfect. Consequently, we expect the occasional galaxy to misbehave. UGC 128 is a case where the MOND fit was great with optical data then became terrible with near-IR data. The absolute difference in the data are not great, but in terms of the formal chi^2 it is. So is that a failure of the model, or of the data to represent what we want it to represent?

This happens all the time in astronomy. Here, we want to know the circular velocity of a test particle in the gravitational potential predicted by the baryonic mass distribution. We never measure either of those quantities. What we measure is the (i) stellar light distribution and the (ii) Doppler velocities of gas. We assume we can map stellar light to stellar mass and Doppler velocity to orbital speed, but no mass model is perfect, nor is any patch of observed gas guaranteed to be on a purely circular orbit. These are known unknowns: uncertainties that we know are real but we cannot easily quantify. These assumptions that we have to make to do the analysis dominate over the random errors in many cases. We also assume that galaxies are in dynamical equilibrium, but 20% of spirals show gross side-to-side asymmetries, and at least 50% mild ones. So what is the circular motion in those cases? (F579-1 is a good example)

While SPARC is homogeneous in its photometry, it is extremely heterogeneous in its rotation curve measurements. We’re working on fixing that, but it’ll take a while. Consequently, as you note, some galaxies have little constraining power while others appear to have lots. That’s because many of the rotation curve velocity uncertainties are either grossly over or underestimated. To see this, plot the cumulative distribution of chi^2 for any of your models (or see the CDF published by Li et al 2018 for the RAR and Li et al 2020 for dark matter halos of many flavors. So many, I can’t recall how many CDF we published.) Anyway, for a good model, chi^2 is always close to one, so the CDF should go up sharply and reach one quickly – there shouldn’t be many cases with very low chi^2 or very high chi^2. Unfortunately, rotation curve data do not do this for any type of model. There are always way too many cases with chi^2 << 1 and also too many with chi^2 >> 1. One might conclude that all models are unacceptable – or that the error bars are Messed Up. I think the second option is the case. If so, then this sort of analysis will always have the power to mislead. 

I insert Fig. 1 from Li et al. (2020) so you don’t have to go look it up. The CDF of a statistically good model would rise sharply, being an almost vertical line at chi^2 = 1. No model of any flavor does that. That’s in large part because the uncertainties on some rotation curves are too large, while those on others are too small. The greater flexibility of dark matter models make them incrementally better than MOND for the cases with error bars that are too small – hence the corollary statement that “the higher the uncertainty of the data, the better MOND seems to work.” This happens because dark matter models are allowed to chase bogus outliers with tiny error bars in a way that MOND cannot. That doesn’t make dark matter better, it just makes it is easier to fool.

  A key thing to watch out for is the outsized effects of a few points with tiny error bars. Among galaxies with high chi^2, what often happens is that there is one point with a tiny error bar that does not agree with any of the rest of the data for any smoothly continuous rotation curve. Fitting programs penalize a model for missing this point by many sigma, so will do anything they can to make it better. So what happens is that if you let a0 vary with a flat prior, it will got to some very silly values in order to buy a tiny improvement in chi^2. Formally, that’s a better fit, so you say OK, a0 has to vary. But if you plot the fitted RCs with fixed and variable a0, you will be hard pressed to see the difference. Chi^2 is different, sure, but both will have chi^2 >> 1, so a lousy fit either way, and we haven’t really gained anything meaningful from allowing for the greater fitting freedom. Really it is just that one point that is Wrong even though it has a tiny error bar – which you can see relative to the other points, never mind the model. Dark matter halos have more flexibility from the beginning, so this is less obvious for them even though the same thing happens.

So that’s another big point – what is the prior for a dark matter halo? [Your] Table 1 allows V200 and C200 to be pretty much anything. So yes, you will find a fit from that range. For Burkert halos, there is no prior, since these do not emerge from any theory – they’re just a flexible French curve. For NFW halos, there is a prior from cosmology – see McGaugh et al (2007) among a zillion other possible references, including Li et al (2020). In any[L]CDM cosmology, the parameters V200 and C200 correlate – they are not independent. So a reasonable prior would be a Gaussian in log(C200) at a given V200 as specified by some simulation (Macio et al; see Li et al 2020). Another prior is how V200 (or M200) relates to the observed baryonic mass (or stellar mass). This one is pretty dodgy. Originally, we expected a fixed ratio between baryonic and dark mass. So when I did this kind of analysis in the ’90s, I found NFW flunked hard compared to MOND. (I didn’t know about the BIC then.) Galaxy DM halos simply do not look like NFW halos that form in LCDM and host galaxies with a few percent of their mass in the luminous disk even though this was the standard model for many years (Mo, Mao, & White 1998). If we drop the assumption that luminous galaxies are always a fixed fraction of their dark matter halos, then better fits can be obtained. I suspect your uniform prior fits have halo masses all over the place; they probably don’t correlate well with the baryonic mass, nor are their C and V200 parameters likely to correlate as they are predicted to do. You could apply the expected mass-concentration and stellar mass-halo mass relations as priors, then NFW will come off worse in your analysis because you’ve restricted them to where they ought to live.

So, as you say – it all comes down to the prior.

Even applying a stellar mass-halo mass relation from abundance matching isn’t really independent information, though that’s the best you can hope to do. But I was saying 20+ years ago that fixed mass ratios wouldn’t work, but nobody then wanted to abandon that obvious assumption. Since then, they’ve been forced to do so. But there is no good physical reason for it (feedback is the deus ex machina of all problems in the field), what happened is that the data forced us to drop the obvious assumption. Data including kinematic data (McGaugh et al 2010). So adopting a modern stellar mass-halo mass relation will give you a stronger prior than a uniform prior, but that choice has already been informed by the kinematic data that you’re trying to fit. How do we properly penalize the model for cheating about its “prior” by peaking at past data?

So, as you say – it all comes down to the prior. I think it would be important here to better constrain the priors on the DM halo fits. Li et al (2020) discuss this. Even then we’re not done, because galaxy formation modifies the form of the halo function we’re fitting. They shouldn’t end up as NFW even if they start out that way – see Li et al 2022a & b. Those papers consider the inevitable effects of adiabatic compression, but not of feedback. If feedback really has the effects on DM halos that is frequently advertised, then neither NFW or Burkert are appropriate fitting functions – they’re not what LCDM+feedback predicts. Good luck extracting a legitimate prediction from simulations, though. So we’re stuck doing what you’re trying to do: adopt some functional form to represent the DM halo, and see what fits. What you’ve done here agrees with my experience: cored DM halos work best. But they don’t represent an LCDM prediction, or any other broader theory, so – so what? 

Another detail to be wary of – the radial range over which the RC data constrain the DM halo fit is often rather limited compared to the size of the halo. To complicate matters further, the inner regions are often star-dominated, so there is not much of a handle on DM from where the data are best, at least beyond many galaxies preferring not to have a cusp since the stars already get the job done at small R. So, one ends up with V_DM(R) constrained from 3% to 10% of the virial radius, or something like that. V200 and C200 are defined at the notional virial radius, so there are many combinations of these parameters that might adequately fit the observed range while being quite different elsewhere. Even worse, NFW halos are pretty self-similar – there are combinations of (C200,V200) that are highly degenerate, so you can’t really tell the difference between them even with excellent data – the confidence contours look like bananas in C200-V200 space, with low C/high V often being as good as high C/low V. Even even even worse is that the observed V_DM(R) is often approximately a straight line. Any function looks like a straight line if you stretch it out enough. Consequently, the fits to LSB galaxies often tend to absurdly low C and high V200: NFW never looks like a straight line, but it does if you blow it up enough. So one ends up inferring that the halo masses of tiny galaxies are nearly as big as those of huge galaxies, or more so! My favorite example was NGC 3109, a tiny dwarf on the edge of the Local Group. A straight NFW fit suggests that the halo of this one little galaxy weighs more than the entire Local Group, M31 + MW + everything else combined. This is the sort of absurd result that comes from fitting the NFW halo form to a limited radial range of data. 

I don’t know that this helps you much, but you see a few of the concerns. 

Wide binary debate heats up again

Wide binary debate heats up again

One of the most interesting and contentious results concerning MOND this year has been the dynamics of wide binaries. When last I wrote on this topic, way back at the end of August, Chae (2023) and Hernandez (2023) both had new papers finding evidence for MONDian behavior in wide binaries. Since that time, they each have written additional papers on the subject. These independent efforts both report strong evidence for MONDian behavior in wide binaries, so for all of October it seemed like Game Over for conventional* dark matter.

I refrained from writing a post then because I was still waiting to see if there would be a contradictory paper. Now there is. And boy, is it contradictory! Where Hernandez et al. find 2.6σ evidence for non-Newtonian behavior and Chae finds ~5σ evidence for non-Newtonian behavior, both consistent with MOND, Banik et al. find purely Newtonian behavior and claim to exclude MOND at 19σ. That’s pretty high confidence!

Well, which is it, young feller? You got proof of non-Newtonian dynamics, or you want to insist that’s impossible?

After the latest results appeared, a red-hot debate [re]ignited on e-mail, largely along the lines of what was discussed at the conference in St. Andrews. Banik et al say that they can reproduce the MOND-like signal of Chae, but that it goes away when the data quality restriction is applied to physical velocity uncertainties (arguing that this is what you want to know) rather than to raw observational uncertainties. Chae and Hernandez counter that the method Banik et al. apply is not grounded in the Newtonian regime where everyone agrees on what should happen, so they could be calibrating the signal away. This is one thing that I had the impression that everyone had agreed to work on in St. Andrews, but it doesn’t appear that we’re there yet.

Banik et al. do a carefully planned Bayesian analysis. This approach in principle allows one to separate many effects simultaneously, one of which is close binaries (CB**). I look at the impact that close binaries have on the analysis, and it gives me the heebie-jeebies:

One panel from Fig. 10 of Banik et al.

This figure illustrates the probability of measuring a characteristic velocity in MOND for the noted range of projected sky separation. If it is just wide binaries (WB), you get the blue line. If there are some close binaries, the expected distribution changes dramatically. This change is rather larger than the signal expected from the nominal difference in gravity. You can in principle fit for everything simultaneously, but extracting the right small signal when there is a big competing signal can be tricky. Bayesian analyses can help, but they are also a double-sided sledge-hammer: a powerful tool with which to pound the data, but also a tool that can bounce back and smack you in the face. Having done such analyses, and been smacked around a few times (and having seen others get smacked around), looking at this plot really does give me the heebie-jeebies. There are lots of ways in which this can go wrong – or even just overstate the confidence of a correct result.

Everyone uses Bayesian methods these days.***

I expect people are expecting me to comment on this hot mess. Some have already asked me to do so. I really don’t want to. I’ve already said more than I should.

There are very earnest, respectable people doing this work; I don’t think anyone is being intentionally misleading. Somebody must be wrong, but it isn’t my job to sort out who. Moreover, these are long and involved analyses; it will take me time to read all the papers and make sense of them. Maybe once I do, I’ll have something more cogent to say.

I make no promises.

*By conventional dark matter, I mean new particles that only communicate with baryons via gravity.

**CB: In principle, some of the wide binaries detected by Gaia will also be close binaries, in the sense that one of the two widely separated stars is itself not a single star but an unrecognized close binary. We know this happen in nature: the nearest star system, αCentauri, is an example. The main A&B components compose a close binary with Proxima Centauri being widely separated. Modeling how often this happens in the Gaia data gives me the willies.

***To paraphrase Churchill: Many forms of statistics have been tried, and will be tried in this science of sin and woe. No one pretends+ that Bayes is perfect or all-wise. Indeed it has been said that Bayes is the worst form of statistics except for all those other forms that have been tried from time to time.

+Lots of people pretend that Bayes is perfect and all-wise.

How things go mostly right or badly wrong

How things go mostly right or badly wrong

People often ask me of how “perfect” MOND has to be. The short answer is that it agrees with galaxy data as “perfectly” as we can perceive – i.e., the scatter in the credible data is accounted for entirely by known errors and the expected scatter in stellar mass-to-light ratios. Sometimes it nevertheless looks to go badly wrong. That’s often because we need to know both the mass distribution and the kinematics perfectly. Here I’ll use the Milky Way as an example of how easily things can look bad when they aren’t.

First, an update. I had hoped to stop talking about the Milky Way after the recent series of posts. But it is in the news, and there is always more to say. A new realization of the rotation curve from the Gaia DR3 data has appeared, so let’s look at all the DR3 data together:

Gaia DR3 realizations of the Milky Way rotation curve. The most recent version of these data from Poder et al (2023) are shown as blue squares over the range 5 < R < 13 kpc. Other Gaia DR3 realizations include Ou et al. (2023, green circles), Wang et al. (2023, magenta downward pointing triangles), and Zhou et al. (2023, purple triangles).

The new Gaia realization does not go very far out, and has larger uncertainties. That doesn’t mean it is worse; it might simply be more conservative in estimating uncertainties, and not making a claim where the data don’t substantiate it. Neither does that mean the other realizations are wrong: these differences are what happens in different analyses. Indeed, all the independent realizations of the Gaia data are pretty consistent, despite the different stellar selection criteria and analysis techniques. This is especially true for R < 17 kpc where there are lots of stars informing the measurements. Even beyond that, I would say they are consistent at the level we’d expect for astronomy.

Zooming out to compare with other results:

The Milky Way rotation curve. The model line from McGaugh (2018) is shown with data from various sources. The abscissa switches from linear to logarithmic at 10 kpc to wedge it all in. The location of the Large Magellanic Cloud at 50 kpc is noted. Gaia DR3 data (Poder et al., Ou et al., Wang et al., and Zhou et al.) are shown as in the plot above. The small black squares are the Gaia DR2 realization of Eilers et al. (2019) reanalyzed to include the effect of bumps and wiggles by McGaugh (2019). Non-Gaia data include blue horizontal branch stars (light blue squares) and red giants (red squares) in the stellar halo (Bird et al. 2022), globular clusters (Watkins et al. 2019, pink triangles), VVV stars (Portail et al. 2017, dark grey squares at R < 2.2 kpc), and terminal velocities (McClure-Griffiths & Dickey 2007, 2016, light grey points from 3 < R < 8 kpc). These terminal velocities are the only data that inform the model line; everything else follows.

Overall, I would say the data paint a pretty consistent picture. The biggest tension amongst the data illustrated here is between the outermost Gaia points around R = 25 kpc and the corresponding results from halo stars. One is consistent with the model line and the other is not. We shouldn’t allow the model to inform our interpretation; the important point is that the independent data disagree with each other. This happens all the time in astronomy. Sometimes it boils down to different assumptions; sometimes it is a real discrepancy. Either way, one has to learn* to cope.

The sharp-eyed will also notice an apparent tension between the DR2 data (black squares) and DR3 around 6 and 7 kpc. This is not real – it is an artifact of different treatments of the term in the Jeans equation for the logarithmic derivative of the density profile of the tracer particles. That’s a choice made in the analysis. The data are entirely consistent when treated consistently.

Putting on an empiricist’s hat, I will say that the kink in the slope of the Gaia data around R = 18 kpc looks unnatural. That doesn’t happen in other galaxies. Rather than belabor the point further, I’ll simply say that this is how things mostly go right but also a little wrong. This is as good as we can hope for in [extra]galactic astronomy.

In contrast, it is easy to go very wrong. To give an example, here is a model of the Milky Way that was built to approximately match the rotation curve of Sofue (2020).


Fig. 1 from Dai et al. (2022). Note the logarithmic abscissa. Their caption: The rotation curve of the Milky Way. The data (solid dark circles with error bars) for r < 100kpc come from [22], while for r > 100kpc from [23]. The solid, dashed and doted lines describe the contribution from the bulge, stellar disk and dark matter halo respectively, within a ΛCDM model of the galaxy. The dashed-dot line is the total contribution of all three components.The parameters of each component are taken from [24]. For comparison, the Milky way rotation curve from Gaia DR2 is shown in color. The red dots are data from [34], the blue upward-pointing triangles are from [35], while the cyan downward-pointing triangles are from [36].

This realization of the rotation curve is very different from that seen above. Note that the rotation curve (black points) is very different from that of Gaia (red points) over the same radial range. These independent data are inconsistent; at least one of them is wrong. The data extend to very large radii, encompassing not only the LMC but also Andromeda (780 kpc away). I am already concerned about the effects of the LMC at 50 kpc; Andromeda is twice the baryonic mass of the Milky Way so anything beyond 260 kpc is more Andromeda’s territory than ours – depending on which side we’re talking about. The uncertainties are so big out there they provide no constraining power anyway.

In terms of MOND-required perfection, things fall apart for the Dai model already at very small radii. Dai et al. (2022) chose to fit their bulge component to the high amplitude terminal velocities of Sofue. That’s a reasonable thing to do, if we think the terminal velocities represent circular motion. Because of the non-circular motions that sustain the Galactic bar, they almost certainly do not – that’s why I restricted use of terminal velocities to larger radii. We also know something about the light distribution:

The inner 3 kpc of the Milky Way. The circles are the terminal velocities of Sofue (2020); the squares are the equivalent circular velocity of the potential reconstructed from the kinematics of stars in the VVV survey (Portail et al. 2017). The line is the bulge-bar model of McGaugh (2008) based on the light distribution reported by Binney et al (1997).

This is essentially the same graph as I showed before, but showing only the Newtonian bulge-bar component, and on a logarithmic abscissa for comparison with the plot of Dai et al. The two bulge models are very different. That of Dai et al. is more massive and more compact, as required to match the terminal velocities. There may be galaxies out there that look like this, but the Milky Way is not one of them.

Indeed, Newton’s prediction for the rotation curve of the bulge-bar component – the line labeled bulge/bar based on what the Milky Way looks like – is in good agreement with the effective circular speed curve obtained from stellar data. It is not consistent with the terminal velocities. We could increase the amplitude of the Newtonian prediction by increasing the mass-to-light ratio of the stars (I have adopted the value I expect for stellar populations), but the shape would still be wrong. This does not come as a surprise to most Galactic astronomers, because we know there is a bar in the center of the Milky Way and we know that bars induce non-circular motions, so we do not expect the terminal velocities to be a fair tracer of the rotation curve in this region. That’s why Portail et al. had to go to great lengths in their analysis to reconstruct the equivalent circular velocity, as did I just to build the bulge-bar model.

The thing about predicting rotation curves from the observed mass, as MOND does, is that you have to get both the kinematic data and the mass distribution right. The velocity predicted at any radius depends on the mass enclosed by that radius. So if we get the bulge badly wrong, everything spirals down the drain from there.

Dai et al. (2022) compare their model to the acceleration residuals predicted by MOND for their mass model. If all is well, the data should scatter around the constant line at zero in this graph:

Fig. 4 from Dai et al. (2022). Their caption: [The radial acceleration relation] recast as a comparison between the total acceleration, a, and the MOND prediction, aM , as a function of the acceleration due to baryons aB. The solid horizontal line is a = aM. The circles and squares with error bars represent the Milky Way and M31 data, while the gray dots are from the EAGLE simulation of ΛCDM in [1]. For aB > 10−10m/s2 any difference between a and aM is unclear. However, once aB drops well below 10−11m/s2, the discrepancy emerges. The short-dashed line is the ΛCDM fitting curve of the MW. The dash-dot line is the ΛCDM fitting curve of M31. The mass range** of galaxies in EAGLE’s data is chosen to be between 5 × 1010M⊙ to 5 × 1011M⊙. For comparison, the Milky way rotation curve from GAIA data release II is shown in color. The red dots are data from [34], the blue triangles are from [35], while the cyan down triangles are from [36]. While the EAGLE simulation does not match the data perfectly, these plots indicate that it is much easier to accommodate a systematic downward trend with the ΛCDM model than with MOND.

Things are not well.

The interpretation that is offered (right in the figure caption) is that MOND is wrong and the LCDM-based EAGLE simulation does a better if not perfect job of explaining things. We already know that’s not right. The alternate interpretation is that this is not a valid representation of the prediction of MOND, because their mass model does not follow from the observed distribution of light. They get neither the baryonic mass distribution and its predicted acceleration ab nor the total acceleration a right in the plot above.

In terms of dark matter, the model of Dai et al. may appear viable. In terms of MOND, it is way off, not just a little off. The residuals are only zero, as they should be, for a narrow range of accelerations, 2 to 3 x 10-10 m/s/s. That’s more Newton than MOND, and appears to correspond to the limited range in radii over which their model matches the rotation curve data in their Fig. 1 (roughly 4 to 6 kpc). It doesn’t really fit the data elsewhere, and the restrictions on a MOND fit are considerably more stringent than on the sort of dark matter model they construct: there’s no reason to expect their model to behave like MOND in the first place.

And, hoo boy, does it ever not behave like MOND. Look at how far those red points – the Gaia DR2 data – deviate from zero in their Fig. 4. Those are the exact same data that agree well with the model line I show above – the data that were correctly predicted in advance. This model is a reasonable representation of the radial force predicted by MOND, with the blue line in my plot being equivalent to the zero line in theirs.

This is how things can go badly wrong. To properly apply MOND, we need to measure both the kinematics and baryonic mass distribution correctly. If we screw either up, as is easy to do in astronomy, then the result will look very wrong, even if it shouldn’t. Combine this with the eagerness many people have to dismiss MOND outright, and you wind up with lots of articles claiming that MOND is wrong – even when that’s not really the story the data tell. Happens over and over again, so the field remains stagnant.

*This is a large part of the cultural difference between physics and astronomy. Physicists are spoiled by laboratory experiments done in controlled conditions in which one can measure to the sixth place of decimals. In contrast, astronomy is an observational rather than experimental science. We can’t put the universe in a box and control all the systematics – measuring most quantities to 1% is a tall order. Consequently, astronomers are used to being wrong. While I wouldn’t say that astronomers cope with it gracefully, they’re well aware that it happens, that is has happened a lot historically, and will continue to happen in the future. It is a risk we all take in trying to understand a universe so much vaster than ourselves. This makes astronomers rather more tolerant of surprising results – results where the first response is “that can’t be right!” but also informed by the experience that “we’ve been wrong before!” Physicists coming to the field generally lack this experience and take the error bars way too seriously. I notice this attitude is creeping into the younger generation of astronomers; people who’ve received their data from distant observatories and performed CPU-intensive MCMC error analyses, so want to believe them, but often lack the experience of dozens of nights spent at the observatory sweating a thousand ill-controlled but consequential details, like walking out to a beautiful sunrise decorated by wisps of cirrus clouds. When did those arrive?!?

**The data that define the radial acceleration relation come from galaxies spanning six decades in stellar mass, so this one decade range from the simulations is tiny – it is literally comparing a factor of ten to a a factor of a million. What happens outside the illustrated mass range? Are lower masses even resolved?

A Response to Recent Developments Concerning the Gravitational Potential of the Milky Way

A Response to Recent Developments Concerning the Gravitational Potential of the Milky Way

In the series of recent posts I’ve made about the Milky Way, I missed an important reply made in the comments by Francois Hammer, one of the eminent scientists doing the work. I was on to writing the next post when he wrote it, and simply didn’t see it until yesterday. Dr. Hammer has some important things to say that are both illustrative of the specific topic and also of how science should work. I wanted to highlight his concerns with their own post, so, with his permission, I cut & paste his comments below, making this, in effect, a guest post by Francois Hammer.

There are two aspects we’d like to mention, as they may help to clarify part of the debate:
1- When saying “Gaia is great, but has its limits. It is really optimized for nearby stars (within a few kpc). Outside of that, the statistics… leave something to be desired. Is it safe to push out beyond 20 kpc?”, one may wonder whether the significance of Gaia data has been really understood.
In the Eilers et al. 2019 DR2 rotation curve, you may see points with small error bar up to 21-22 kpc. Gaia DR3 provides proper motion (systematics) uncertainties that are 2 times smaller than from Gaia DR2, so it can easily goes to 25 kpc or more.
The gain in quality for parallaxes is indeed smaller (30% gain). However, our results cannot be affected by distance estimates, since the large number of stars with parallax estimates in Wang et al. (2023) is giving the same rotation curve than that from (a lower number of) RGB stars with spectrophotometric distances (Ou et al. 2023), i.e., following Eilers et al. 2019. And both show a Keplerian decline, which was already noticeable with DR2 results from Eilers et al 2019. The latter authors said in their conclusions: “We do see a mild but significant deviation from the straightly declining circular velocity curve at R≈19–21 kpc of Δv≈15 km s−1.” Our work using Gaia DR3 is nothing else than having a factor 2 better in accounting for systematics, and then being able to resolve what looks like a Keplerian decrease of the rotation curve.
We may also mention here that one of us participated to an unprecedented study of the kinematics LMC (Gaia Collaboration 2021, Luri’s paper), which is at 50 kpc. Unless one proves everything that people has done about the LMC and MW is wrong, and that the data are too uncertain to conclude anything about what happens at R=17-25 kpc, the above clarifications about Gaia accuracy are truly necessary for people reading your blog.
2- The argument that the result “violates a gazillion well-established constraints.” has to be taken with some caution, since otherwise, no one can do any progress in the field. In fact, the problem with many probes (so-called “satellites”) in the MW halo, is the fact that one cannot guarantee whether or not their orbits are at equilibrium with the MW potential. This is the reverse for the MW disk, for which stars are rotating in the disk, and, e.g., at 25 kpc, they have likely experience 7-8 orbits since the last merger (Gaia-Sausage-Enceladus), about 9 billion years go. In other words, the mass provided by a system mostly at equilibrium, likely supersedes masses provided by systems that equilibrium conditions are not secured. An interesting example of this is given by globular clusters (GCs). If taken as an ensemble of 156 GCs (from Baumgardt catalog), just by removing Pyxis and Terzan 8, the MW mass inside 50 kpc passes from 5.5 to 2.1 10^11 Msun. This is likely because these two GCs may have come quite recently, meaning that their initial kinetic energy is still contributing to their total energy. A similar mass overestimate could happen if one accounts the LMC or Leo I as MW satellites at equilibrium with the MW potential.
So we agree that near 25 kpc the disk of the MW may show signs of less-equilibrium, or sign of slightly less circular orbits due to different phenomenas discussed in the blog. However, why taking into account objects for which there is no proof they are at equilibrium as being the true measurements?
In our work, we have considerably focused in understanding and expanding the whole contribution of systematics, which may comes from Gaia data, but also from assumptions about stellar profile (i.e., deviations from exponential profiles), from the Sun distance and proper motion and so on. You may find a description in Ou et al.’s Figure 5 and Jiao et al.’s Figure 4, both showing that systematics cannot gives much more than 10% error on circular velocity estimates. This is an area where we are considered by the Local Group community as being quite conservative, and following Gaia specialists with who we have worked to deliver the EDR3 catalog of dwarf galaxy motions (Li, Hammer, Babusiaux et al 2021) up to about 150 kpc. Jiao et al. paper main contribution is the fair accounting of systematics, which analysis shows error bars that are much larger than those from other sources of errors especially in MW outskirts (see Fig. 2).

Francois Hammer, 24 September 2023

The image at top is Fig. 2 from Jiao et al. illustrating their assessment of the rotation curve and its systematic uncertainties.

OSIRIS-REx returns safely

OSIRIS-REx returns safely

Taking a break from galaxies and cosmology, I’d like to post a little praise of NASA for safely returning a piece of an asteroid to Earth.

One of the amazing things to me about astronomy & astrophysics is that we have learned how to decipher the composition of distant stars and gas clouds by observing their spectra. I worked on this early in my career and retain an interest in the cosmic abundance of the elements. Fun fact: though often overlooked because it is a boring noble gas that doesn’t bind chemically into any common molecules or minerals, neon is number 5 on the list of most common elements, which goes hydrogen, helium, oxygen, carbon, neon. Nitrogen is number 6 by number, but iron supplants it if we weight by mass – there are more nitrogen atoms by number in the sun but the iron weighs more because of the greater mass of each atom. The order of the first five remains the same by either accounting.

Amazing as it is that we can do this, it can only be accomplished by passive observation. What we’d really like to do is get samples of the remote universe to analyze in the laboratory where precision is much higher and we can better control for systematic effects. Of course we can’t travel to stars and nebulae that are many light-years distant, let alone return from there. But we can do it within the solar system, which is amazing enough. The Apollo astronauts brought back rocks from the moon that helped determine the age of the solar system (4.568 billion years, give or take a million), and the period of “late heavy bombardment” when most big lunar craters were formed – a mere 3.9 billion years ago. This in turn calibrates crater densities; counting craters on other solar system bodies lets us gauge the age of a surface. Lots of craters means old; few craters means something interesting had to happen to cover up all the craters that formed during heavy bombardment. It’s not like all those early meteoroids were dodging the Earth while hammering the moon; it’s just that the Earth has covered it up since.

One of the most interesting things scientifically are samples of pristine material – the stuff from which the solar system formed. The Earth is a remarkably active planet geologically, which means that its rocks are always getting remade by erosion, subduction, and volcanism. They’re about as far from pristine as a rock can get. The closest we expect we can get are the comets and asteroids orbiting safely away from the big planets that have a complex history of their own.

Hence the idea for a mission that could return a sample from a remote asteroid. This is what OSIRIS-REx has now accomplished. It is worth pausing to reflect what an amazing feat this is.

We’ve only had the capacity to launch things beyond the atmosphere of our planet for 66 years. Though satellite launches are now relatively common, the most frequent destination is low earth orbit. That’s only a couple thousand kilometers, which is about a third of an Earth radius, so still pretty close. It is a distance that planes traverse horizontally all the time, if only at an altitude of 10 km or so. It’s just not that far on an interplanetary scale.

Deep space missions that leave Earth’s gravity well are harder and much less common. Those that go out to an asteroid, grab a piece, and return are even harder. It’s one thing to shoot something off a rocket so hard it never comes back. It’s quite another to do that and then turn around and come back at a time and place of our choosing. That’s a remarkable feat of celestial navigation and rocket engineering. Oh, and pause on the way to graze an asteroid, grab a sample, and store it for safe return.

Safe is key here. If one wants a pristine sample of the early solar system, you not only need to go to deep space to collect it, but you have to keep it safe through the rigors of reentry, collect it, and get it to your lab unsullied by terrestrial contaminants. Lots that can go wrong. The spacecraft has to endure the heat of reentry, suffer no leaks, and land gently in a spot where the sample can be retrieved. This all went well for OSIRIS-REx. It doesn’t always work so well.

Genesis was another sample return mission. Launched in 2001, it collected particles from the solar wind – a good way to get a measure of the composition of the sun. It did this for several years before returning 19 years ago to the month, to the same landing area as OSIRIS-REx. As it happened, I had just flown to Tucson to observe at Kitt Peak, and found myself having breakfast in the La Quinta next to the airport before renting a car to drive up the mountain. The landing was on the TV there, so it was breakfast and a show.

Only the show didn’t go so well. A helicopter was supposed to snag the capsule as it drifted at the end of its parachute to ensure no contamination from the ground. Through some amazing camera work, they showed a fairly zoomed-in image of the return capsule as it hurtled from the sky. Spinning, spinning, spinning… it looked out of control. Shouldn’t the parachute have deployed by now? Maybe not – that’s often done at fairly low altitude where the air is thick enough to bite. So I watched, spinning, spinning, as seconds stretched into minutes, spinning, spinning, surely the parachute will deploy any moment now, spinning, spinning, any moment now, spinning, spinning, really, any moment now, spinning, spinning, SMACK! into the ground.

Genesis did not experience a gentle landing. Photo credit: USAF, public domain.

The parachute failed to deploy. Apparently Lockheed Martin installed it backwards, a mistake for which I’m sure they were well remunerated. This is but one of the hazards of space travel.

So it was with a little trepidation that I watched the return of OSIRIS-REx this morning. There was again some amazing camera work. First we saw the blaze of reentry, then after that faded the capsule itself emerged, becoming visible while still at high altitude. Spinning, spinning.

As I was watching on NASA TV, it was announced that the order to deploy the parachute had been issued. Spinning, spinning. Good. Spinning, spinning. No parachute. Was there a time delay on that order? Still seemed high to be deploying a chute, but it was hard to judge the altitude from watching a small spinning blob on TV. Spinning, spinning. I am old and jaded, so I didn’t feel nervous – yet. Spinning, spinning. Only a tiny bit of anxiety. Spinning, spinning. Then it was announced that the parachute was scheduled to deploy at 49 minutes past the hour – still two minutes away. Spinning, spinning. Then, at 48 minutes past the hour, the parachute deployed. I was so enthused to see it that I didn’t worry that it had come a bit early – better than too late! Apparently it deployed at an altitude of 20,000 feet when it wasn’t supposed to deploy until 5,000. So that went wrong, but only a tiny bit wrong – it came gently to rest on the ground near the edge of the target ellipse – i.e., within the error bars.

Osiris-Rex return capsule where it landed in Utah. Screen shot from NASA TV. As in, I took a picture of the TV with my phone.

This time there was no unnecessarily elaborate plan to snag the capsule out of the air with a helicopter as there had been for Genesis. But a helicopter was used to transport the capsule, dangled from the end of a long rope, to a temporary clean room that had been set up nearby. From there it will be transported to the Astromaterials facility at the Johnson Space Center in Houston, where they have an office of Astromaterials Acquisition and Curation. Sounds very Indiana Jones in space.

Science to follow.

Recent Developments Concerning the Gravitational Potential of the Milky Way. III. A Closer Look at the RAR Model

Recent Developments Concerning the Gravitational Potential of the Milky Way. III. A Closer Look at the RAR Model

I am primarily an extragalactic astronomer – someone who studies galaxies outside our own. Our home Galaxy is a subject in its own right. Naturally, I became curious how the Milky Way appeared in the light of the systematic behaviors we have learned from external galaxies. I first wrote a paper about it in 2008; in the process I realized that I could use the RAR to infer the distribution of stellar mass from the terminal velocities observed in interstellar gas. That’s not necessary in external galaxies, where we can measure the light distribution, but we don’t get a view of the whole Galaxy from our location within it. Still, it wasn’t my field, so it wasn’t until 2015/16 that I did the exercise in detail. Shortly after that, the folks who study the supermassive black hole at the center of the Galaxy provided a very precise constraint on the distance there. That was the one big systematic uncertainty in my own work up to that point, but I had guessed well enough, so it didn’t make a big change. Still, I updated the model to the new distance in 2018, and provided its details on my model page so anyone could use it. Then Gaia data started to pour in, which was overwhelming, but I found I really didn’t need to do any updating: the second data release indicated a declining rotation curve at exactly the rate the model predicted: -1.7 km/s/kpc. So far so good.

I call it the RAR model because it only involves the radial force. All I did was assume that the Milky Way was a typical spiral galaxy that followed the RAR, and ask what the mass distribution of the stars needed to be to match the observed terminal velocities. This is a purely empirical exercise that should work regardless of the underlying cause of the RAR, be it MOND or something else. Of course, MOND is the only theory that explicitly predicted the RAR ahead of time, but we’ve gone to great lengths to establish that the RAR is present empirically whether we know about MOND or not. If we accept that the cause of the RAR is MOND, which is the natural interpretation, then MOND over-predicts the vertical motions by a bit. That may be an important clue, either into how MOND works (it doesn’t necessarily follow the most naive assumption) or how something else might cause the observed MONDian phenomenology, or it could just be another systematic uncertainty of the sort that always plagues astronomy. Here I will focus on the RAR model, highlighting specific radial ranges where the details of the RAR model provide insight that can’t be obtained in other ways.

The RAR Milky Way model was fit to the terminal velocity data (in grey) over the radial range 3 < R < 8 kpc. Everything outside of that range is a prediction. It is not a prediction limited to that skinny blue line, as I have to extrapolate the mass distribution of the Milky Way to arbitrarily large radii. If there is a gradient in the mass-to-light ratio, or even if I guess a little wrong in the extrapolation, it’ll go off at some point. It shouldn’t be far off, as V(R) is mostly fixed by the enclosed mass. Mostly. If there is something else out there, it’ll be higher (like the cyan line including an estimate of the coronal gas in the plot that goes out to 130 kpc). If there is a bit less than the extrapolation, it’ll be lower.

The RAR model Milky Way (blue line) together with the terminal velocities to which it was fit (light grey points), VVV data in the inner 2.2 kpc (dark grey squares), and the Zhou et al. (2023) realization of the Gaia DR3 data. Also shown are the number of stars per bin from Gaia (right axis).

From 8 to 19 kpc, the Gaia data as realized by Zhao et al. fall bang on the model. They evince exactly the slowly declining rotation curve that was predicted. That’s pretty good for an extrapolation from R < 8 kpc. I’m not aware of any other model that did this well in advance of the observation. Indeed, I can’t think of a way to even make a prediction with a dark matter model. I’ve tried this – a lot – and it is as easy to come up with a model whose rotation curve is rising as one that is falling. There’s nothing in the dark matter paradigm that is predictive at this level of detail.

Beyond R > 19 kpc, the match of the model and Zhou et al. realization of the data is not perfect. It is still pretty damn good by astronomical standards, and better than the Keplerian dotted line. Cosmologists would be wetting themselves with excitement if they could come this close to predicting anything. Heck, they’re known to do that even when they’re obviously wrong*.

If the difference between the outermost data and the blue line is correct, then all it means is that we have to tweak the model to have a bit less mass than assumed in the extrapolation. I call it a tweak because it would be exactly that: a small change to an assumption I was obliged to make in order to do the calculation. I could have assumed something else, and almost did: there is discussion in the literature that the disk of the Milky Way is truncated at 20 kpc. I considered using a mass model with such a feature, but one can’t make it a sharp edge as that introduces numerical artifacts when solving the Poisson equation numerically, as this procedure depends on derivatives that blow up when they encounter sharp features. Presumably the physical truncation isn’t unphysically sharp anyway, rather being a transition to a steeper exponential decline as we sometimes see in other galaxies. However, despite indications of such an effect, there wasn’t enough data to constrain it in a way useful for my model. So rather than introduce a bunch of extra, unconstrained freedom into the model, I made a straight extrapolation from what I had all the way to infinity in the full knowledge that this had to be wrong at some level. Perhaps we’ve found that level.

That said, I’m happy with the agreement of the data with the model as is. The data become very sparse where there is even a hint of disagreement. Where there are thousands of stars per bin in the well-fit portion of the rotation curve, there are only tens per bin outside 20 kpc. When the numbers get that small, one has to start to worry that there are not enough independent samples of phase space. A sizeable fraction of those tens of stars could be part of the same stellar stream, which would bias the results to that particular unrepresentative orbit. I don’t know if that’s the case, which is the point: it is just one of the many potential systematic uncertainties that are not represented in the formal error bars. Missing those last five points by two sigma is as likely to be an indication that the error bars have been underestimated as it is to be an indication that the model is inadequate. Trying to account for this sort of thing is why the error bars of Jiao et al. are so much bigger than the formal uncertainties in the three realization papers.

That’s the outer regions. The place where the RAR model disagrees the most with the Gaia data is from 5 < R < 8 kpc, which is in the range where it was fit! So what’s going on there?

Again, the data disagree with the data. The stellar data from Gaia disagree with the terminal velocity data from interstellar gas at high significance. The RAR model was fit to the latter, so it must per force disagree with the former. It is tempting to dismiss one or the other as wrong, but do they really disagree?

Adapted from Fig. 4 of McGaugh (2019). Grey points are the first and fourth quadrant terminal velocity data to which the model (blue line) was matched. The red squares are the stellar rotation curve estimated with Gaia DR2 (DR3 is indistinguishable). The black squares are the stellar rotation curve after adjustment to be consistent with a mass profile that includes spiral arms. This adjustment for self-consistency remedies the apparent discrepancy between gas and stellar data.

In order to build the model depicted above, I chose to split the difference between the first and fourth quadrant terminal velocity data. I fit them separately in McGaugh (2016) where I made the additional point that the apparent difference between the two quadrants is what we expect from an m=2 mode – i.e., a galaxy with spiral arms. That means these velocities are not exactly circular as commonly assumed, and as I must per force assume to build the model. So I split the difference above in the full knowledge that this is not the exact circular velocity curve of the Galaxy, it’s just the best I can do at present. This is another example of the systematic uncertainties we encounter: the difference between the first and fourth quadrant is real and is telling us that the galaxy is not azimuthally symmetric – as anyone can tell by looking at any spiral galaxy, but is a detail we’d like to ignore so we can talk about disk+dark matter halo models in the convenient limit of axisymmetry.

Though not perfect – no model is – the RAR model Milky Way is a lot better than models that ignore spiral structure entirely, which is basically all of them. The standard procedure assumes an exponential disk and some form of dark matter halo. Allowance is usually made for a central bulge component, but it is relatively rare to bother to include the interstellar gas, much less consider deviations from a pure exponential disk. Having adopted the approximation of an exponential disk, one inevitably get a smooth rotation curve like the dashed line below:

Fig. 1 from McGaugh (2019). Red points are the binned fourth quadrant molecular hydrogen terminal velocities to which the model (blue line) has been fit. The dotted lines shows the corresponding Newtonian rotation curve of the baryons. The dashed line is the model of Bovy & Rix (2013) built assuming an exponential disk. The inset shows residuals of the models from the data. The exponential model does not and cannot fit these data.

The common assumption of exponential disk precludes the possibility of fitting the bumps and wiggles observed in the terminal velocities. These occur because of deviations from a pure exponential profile caused by features like spiral arms. By making this assumption, the variations in mass due to spiral arms is artificially smoothed over. They are not there by assumption, and there is no way to recover them in a dark matter fit that doesn’t know about the RAR.

Depending on what one is trying to accomplish, an exponential model may suffice. The Bovy & Rix model shown above is perfectly reasonable for what they were trying to do, which involved the vertical motions of stars, not the bumps and wiggles in the rotation curve. I would say that the result they obtain is in reasonable agreement with the rotation curve, given what they were doing and in full knowledge that we can’t expect to hit every error bar of every datum of every sort. But for the benefit of the chi-square enthusiasts who are concerned about missing a few data points at large radii, the reduced chi-squared of the Bovy & Rix model is 14.35 while that of the RAR model is 0.6. A good fit is around 1, so the RAR model is a good fit while the smooth exponential is terrible – as one can see by eye in the residual inset: the smooth exponential model gets the overall amplitude about right, but hits none of the data. That’s the starting point for every dark matter model that assumes an exponential disk; even if they do a marginally better job of fitting the alleged Keplerian downturn, they’re still a lot worse if we consider the terminal velocity data, the details of which are usually ignored.

If instead we pay attention the details of the terminal velocity data, we discover that the broad features seen there in are pretty much what we expect for the kinematic signatures of photometrically known spiral arms. That is, the mass density variations inferred by fitting the RAR correspond to spiral arms that are independently known from star counts. We’ve discussed this before.

Spiral structure in the Milky Way (left) as traced by HII regions and Giant Molecular Clouds (GMCs). These correspond to bumps in the surface density profile inferred from kinematics with the RAR (right).

If we accept that the bumps and wiggles in the terminal velocities are tracers of bumps and wiggles in the stellar mass profiles, as seen in external galaxies, then we can return to examining the apparent discrepancy between them and the stellar rotation curve from Gaia. The latter follow from an application of the Jeans equation, which helps us sort out the circular motion from the mildly eccentric orbits of many stars. It includes a term that depends on the gradient of the density profile of the stars that trace the gravitational potential. If we assume an exponential disk, then that term is easily calculated. It is slowly and smoothly varying, and has little impact on the outcome. One can explore variations of the assumed scale length of the disk, and these likewise have little impact, leading us to infer that we don’t need to worry about it. The trouble with this inference is that it is predicated on the assumption of a smooth exponential disk. We are implicitly assuming that there are no bumps and wiggles.

The bumps and wiggles are explicitly part of the RAR model. Consequently, the gradient term in the Jeans equation has a modest but important impact on the result. Applying it to the Gaia data, I get the black points:

The red squares are the Gaia DR2 data. The black squares are the same data after including in the Jeans equation the effect of variations in the tracer gradient. This term dominates the uncertainties.

The velocities of the Gaia data in the range illustrated all go up. This systematic effect reconciles the apparent discrepancy between the stellar and gas rotation curves. The red points are highly discrepant from the gray points, but the black points are not. All it took was to drop the assumption of a smooth exponential profile and calculate the density gradient numerically from the data. This difference has a more pronounced impact on rotation curve fits than any of the differences between the various realizations of the Gaia DR3 data – hence my cavalier attitude towards their error bars. Those are not the important uncertainties.

Indeed, I caution that we still don’t know what the effective circular velocity of the potential is. I’ve made my best guess by splitting the difference between the first and fourth quadrant terminal velocity data, but I’ve surely not got it perfectly right. One might view the difference between the quadrants as the level at which the perfect quantity is practically unknowable. I don’t think it is quite that bad, but I hope I have at least given the reader some flavor for some of the hidden systematic uncertainties that we struggle with in astronomy.

It gets worse! At small radii, there is good reason to be wary of the extent to which terminal velocities represent circular motion. Our Galaxy hosts a strong bar, as artistically depicted here:

Artist’s rendition of the Milky Way. Image credit: NASA/JPL-Caltech.

Bars are a rich topic in their own right. They are supported by non-circular orbits that maintain their pattern. Consequently, one does not expect gas in the region where the bar is to be on circular orbits. It is not entirely clear how long the bar in our Galaxy is, but it is at least 3 kpc – which is why I have not attempted to fit data interior to that. I do, however, have to account for the mass in that region. So I built a model based on the observed light distribution. It’s a nifty bit of math to work out the equivalent circular velocity corresponding to a triaxial bar structure, so having done it once I’ve not been keen to do it again. This fixes the shape of the rotation curve in the inner region, though the amplitude may shift up and down with the mass-to-light ratio of the stars, which dominate the gravitational potential at small radii. This deserves its own close up:

Colored points are terminal velocities from Marasco et al. (2017), from both molecular (red) and atomic (green) gas. Light gray circles are from Sofue (2020). These are plotted assuming they represent circular motions, which they do not. Dark grey squares are the equivalent circular velocity inferred from stars in the VVV survey. The black line is the Newtonian mass model for the central bar and disk, and the blue line is the corresponding RAR model as seen above.

Here is another place where the terminal velocities disagree with the stellar data. This time, it is because the terminal velocities do not trace circular motion. If we assume they do, then we get what is depicted above, and for many years, that was thought to be the Galactic rotation curve, complete with a pronounced classical bulge. Many decades later, we know the center of the Galaxy is not dominated by a bulge but rather a bar, with concominant non-circular motions – motions that have been observed in the stars and carefully used to reconstruct the equivalent circular velocity curve by Portail et al. (2017). This is exactly what we need to compare to the RAR model.

Note that 2008, when the bar model was constructed, predates 2017 (or the 2016 appearance of the preprint). While it would have been fair to tweak the model as the data improved, this did not prove necessary. The RAR model effectively predicted the inner rotation curve a priori. That’s a considerably more impressive feat than getting the outer slope right, but the model manages both sans effort.

No dark matter model can make an equivalent boast. Indeed, it is not obvious how to do this at all; usually people just make a crude assumption with some convenient approximation like the Hernquist potential and call it a day without bothering to fit the inner data. The obvious prediction for a dark matter model overshoots the inner rotation curve, as there is no room for the cusp predicted in cold dark matter halos – stars dominate the central potential. One can of course invoke feedback to fix this, but it is a post hoc kludge rather than a prediction, and one that isn’t supposed to apply in galaxies as massive as the Milky Way. Unless it needs to, of course.

So, lets’s see – the RAR model Milky Way reconciles the tension between stellar and interstellar velocity data, indicates density bumps that are in the right location to correspond to actual spiral arms, matches the effective circular velocity curve determined for stars in the Galactic bar, correctly predicted the slope of the rotation curve outside the solar circle out to at least 19 kpc, and is consistent with the bulk of the data at much larger radii. That’s a pretty successful model. Some realizations of the Gaia DR3 data are a bit lower than predicted, but others are not. Hopefully our knowledge of the outer rotation curve will continue to improve. Maybe the day will come when the data have improved to the point where the model needs to be tweaked a little bit, but it is not this day.

*To give one example, the BICEP II experiment infamously claimed in March of 2014 to have detected the Inflationary signal of primordial gravitational waves in their polarization data. They held a huge press conference to announce the result in clear anticipation of earning a Nobel prize. They did this before releasing the science paper, much less hearing back from a referee. When they did release the science paper, it was immediately obvious on inspection that they had incorrectly estimated the dust foreground. Their signal was just that – excess foreground emission. I could see that in a quick glance at the relevant figure as soon as the paper was made available. Literally – I picked it up, scanned through it, saw the relevant figure, and could immediately spot where they had gone wrong. And yet this huge group of scientists all signed their name to the submitted paper and hyped it as the cosmic “discovery of the century”. Pfft.