We have a new paper that introduces SPARC: Spitzer Photometry & Accurate Rotation Curves. SPARC is a database of 175 galaxies with measured HI rotation curves and homogeneous near-infrared [3.6 micron] surface photometry obtained with the Spitzer Space Telescope. It provides the largest cohesive dataset currently available of disk galaxy mass models.

SPARC represents all known types of rotating galaxies. It spans a broad range in morphologies (S0 to Irr), luminosities (L[3.6] ~ 107 to ~1012 L, effective radii (~0.3 to ~15 kpc), effective surface brightnesses (~5 to ~5000 L pc-2), rotation velocities (~20 to ~300 km/s), and gas content (0.01 < M(HI)/L[3.6] < 10). This samples the full range of physical properties known for rotating disk galaxies. It is vastly superior to most “complete” samples in that it provides a much better representation of low mass and low surface brightness galaxies.

Let me emphasize that last point. Traditional galaxy surveys are great at finding bright objects. They are lousy at finding low luminosity and low surface brightness galaxies. For example, most studies based on the gold-standard Sloan Digital Sky Survey are restricted to massive galaxies with M* > 109 M☉. SPARC extends two decades lower in mass. Sloan misses low surface brightness galaxies entirely. SPARC includes many such objects. Ideally, a sample like this would provide a thorough sampling of all possible disk galaxy properties. We come as close to that ideal as is currently possible, without the usual bias against the faint and the dim.

The rotation curves of SPARC galaxies have been collected from the literature. While we have obtained some of these ourselves, the vast majority come from the hard work of many others. All SPARC galaxies have been observed in the 21cm line of atomic hydrogen with radio interferometers like the VLA or WSRT. These data represent the fruits of the labors of a whole community of radio astronomers spanning decades.

The surface photometry we have done ourselves. This represents the cumulative results of a decade of work. The near-IR images from Spitzer have been analyzed with the ARCHANGEL software to determine the surface brightness profiles of all sample galaxies. These have been used to construct mass models representing the gravitational potential generated by the observed distribution of stellar mass. The 21cm data provide the same information for the gas.


Optical (BVI), near-IR (JHK), and 21 cm images of the spiral galaxy NGC 6946. The images are shown on the same scale. So yes, the gas extends that much further out. This is typical, and emphasizes the importance of combining multiwavelength observations.

We now have three measured properties for all SPARC galaxies that are hard to find simultaneously in the literature. These are the rotation curve V(R), the portion of the rotation due to stars V*(R), and that due to gas Vg(R). These are what you need to study the missing mass problem in galaxies, as

V2(R) = V*2(R)+Vg2(R)+VDM2(R)

The mysterious “other” represented by VDM(R) is dark matter (whatever that means). It is now completely specified by the observations.

Of course, this has been true for a while, but with one important exception. Mass models for V*(R) have been constructed with the available data, which are usually in the optical. When we construct a mass model, we have to convert the observed light to a stellar mass by assuming some mass-to-light ratio for the stars, M*/L. Optical M*/L vary with age and metallicity in a way that precludes clarity in the correct stellar mass model. Near-IR data (the 2.2 micron K-band or [3.6] of Spitzer) are much, much, much better for this.

I don’t think I emphasized that enough. The near-IR image of a galaxy is as close as we’re likely to ever get to a map of the stellar mass. It isn’t perfect of course – nothing in astronomy ever is – but it is a sufficient improvement that all the freedom and uncertainty that we had in VDM(R) before basically goes away.

We’ll have a lot more to say about that. Look for big announcements, coming soon.

8 thoughts on “SPARC

  1. How fitting for a database named SPARC to overcome “the bias against the faint and the dim”.
    Here’s hoping for a post explaining the big paper to come !


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