The UV-Optical Galaxy Color-Magnitude Diagram. I. Basic Properties

By T. Wyder, D. C. Martin, David Schiminovich, M. Seibert, Tamás Budavári, M. Treyer, T. Barlow, K. Forster, P. Friedman, P. Morrissey and 20 more. 2007

346 citations recorded by the source corpus.

We have analyzed the bivariate distribution of galaxies as a function of ultraviolet-optical colors and absolute magnitudes in the local universe. The sample consists of galaxies with redshifts and optical photometry from the Sloan Digital Sky Survey (SDSS) main galaxy sample matched with detections in the near-ultraviolet (NUV) and far-ultraviolet (FUV) bands in the Medium Imaging Survey being carried out by the Galaxy Evolution Explorer (GALEX) satellite. In the (NUV − r)0.1 versus Mr,0.1 galaxy color-magnitude diagram, the galaxies separate into two well-defined blue and red sequences. The (NUV − r)0.1 color distribution at each Mr,0.1 is not well fit by the sum of two Gaussians due to an excess of galaxies in between the two sequences. The peaks of both sequences become redder with increasing luminosity, with a distinct blue peak visible up to Mr,0.1 ∼ − 23. The r0.1-band luminosity functions vary systematically with color, with the faint-end slope and characteristic luminosity gradually increasing with color. After correcting for attenuation due to dust, we find that approximately one-quarter of the color variation along the blue sequence is due to dust, with the remainder due to star formation history and metallicity. Finally, we present the distribution of galaxies as a function of specific star formation rate and stellar mass. The specific star formation rates imply that galaxies along the blue sequence progress from low-mass galaxies with star formation rates that increase somewhat with time to more massive galaxies with a more or less constant star formation rate. Above a stellar mass of ~1010.5 M☉, galaxies with low ratios of current to past averaged star formation rate begin to dominate.

Subfields: Astronomy and Astrophysics, Computer Vision and Pattern Recognition, Ecology

View Paper: https://www.semanticscholar.org/paper/dc091149eeb1034cd15a53d4b375e7887b0ae5e2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Astronomy and Astrophysics

  1. P. Hopkins
  2. C. Conroy
  3. L. Hernquist
  4. P. V. van Dokkum
  5. R. Dav'e
  6. R. Somerville
  7. Simon D. M. White
  8. M. Franx

In Computer Vision and Pattern Recognition

  1. Christopher J. Conselice
  2. Kevin Schawinski
  3. M. Sarzi
  4. J. Lotz
  5. Hans-Walter Rix
  6. Jenny E. Greene
  7. D. Thomas
  8. C. Maraston

In Ecology

  1. F. V. D. Bosch
  2. M. Blanton
  3. H. Mo
  4. Xiaohu Yang
  5. J. Brinkmann
  6. D. Schneider
  7. Rachel Mandelbaum
  8. G. Kauffmann

Citations in the corpus