Site Map

Galaxy Properties

Data Release 9 includes three different estimates for the stellar mass and velocity dispersion estimators for galaxies. The three methods are described in this section.

The array of choices allows consistent comparisons with the literature and future surveys. The proper method to use will depend on the scientific problem at hand, and should be chosen carefully.

The spectroscopic pipeline initially classifies all spectra without referring to any associated imaging data. That is, a spectrally-observed object is classified by testing its spectrum against templates for stars, galaxies, and quasars, regardless of why that object was targeted for spectroscopic follow-up. However, in BOSS, we found that galaxy targets were often incorrectly matched to quasar templates with unphysical fit parameters, (such as negative coefficients), resulting in genuine galaxy absorption features being incorrectly fit to quasar emission features. Thus, for galaxy targets in BOSS, the best classification and redshift are selected only from the fits to the galaxy and star templates. The resulting quantities are listed with the suffix _NOQSO in the pipeline outputs (Bolton et al. 2012). Results without this template restriction are also made available.

After the spectra are output from the spectroscopic pipeline, we additionally compute a variety of derived quantities by applying stellar population models to derive stellar masses, emission-line fluxes and equivalent widths, and gas kinematics and stellar velocity dispersions ( Chen et al. 2012, Maraston et al. 2012, Thomas et al. 2013).

Data Release 9 includes these derived quantities based on three stellar population models. The MPA-JHU Galspec stellar population models are available for SDSS-I/-II galaxies, but have not been run on BOSS galaxies.

The Portsmouth SED-fit Stellar Masses, the Portsmouth Stellar Kinematics and Emission Line Fluxes and Wisconsin (PCA) Galaxy Properties are new for Data Release 9, and are currently available only for BOSS spectra. However, Chen et al. (2012) and Thomas et al. (2013) each found that a comparison of their respective techniques to the MPA-JHU algorithm demonstrated consistent results for a set of SDSS galaxies from Data Release 7.

Both the Portsmouth and Wisconsin galaxy property computations have been applied to all objects that the spectroscopic pipeline classifies as a galaxy with a reliable and positive definite redshift (i.e. with CLASS_NOQSO='galaxy' and ZWARNING_NOQSO=0 and (Z_NOQSO > Z_ERR_NOQSO > 0) (Bolton et al. 2012). A detailed comparison between the Portsmouth SED-fit and the Wisconsin spectral PCA stellar masses is discussed in Appendix A of Maraston et al. (2012).

The three galaxy computations are described below. Click on their names for a page giving further information.


The Galspec product (Kauffmann et al. 2003, Brinchmann et al. 2004, Tremonti et al. 2004), provided by the Max Planck Institute for Astrophysics and the Johns Hopkins University (MPA-JHU), introduced in Data Release 8 and is maintained for SDSS-I/II galaxies, but is not available for SDSS-III BOSS spectra.

Portsmouth SED-fit Stellar Masses

Portsmouth SED-fit stellar masses Maraston et al. (2012) are calculated using the BOSS spectroscopic redshift, Z_NOQSO and u,g,r,i,z photometry by means of broad-band spectral energy distribution (SED) fitting of stellar population models. Separate calculations are carried out with a passive template and a star-forming template, and in each case for both Salpeter (1955) and Kroupa (2001) initial mass functions, and for stellar evolution with and without stellar mass loss.

Templates are based on Maraston (2005) and Maraston et al. (2009) for the star-forming and passive stellar population models, respectively, for the best-fit spectral energy distribution model of Maraston et al. (2006).

In Data Release 9, internal galaxy reddening is not included in the fitting procedures, in order not to underestimate stellar mass. Reddening for individual galaxies may, however, be obtained via the Portsmouth emission-line flux calculations Thomas et al. (2013).

Portsmouth Stellar Kinematics and Emission Line Fluxes

Portsmouth Stellar Kinematics and Emission Line Fluxes (Thomas et al. 2013) are based on the stellar population synthesis models of Maraston & Strömbäck (2011) applied to BOSS spectra using an adaptation of the publicly available Gas AND Absorption Line Fitting (GANDALF, Sarzi et al. 2006) and penalized PiXel Fitting (pPXF, Cappellari & Emsellem 2004).

Caveat: Please note that due to a bug in the DR9 version of the Portsmouth Stellar Kinematics and Emission Line Fluxes code, EW values need to be divided by a factor (1+z) and Continuum Flux measurements need to be multiplied by a factor (1+z) before being used. This will be corrected in the DR10 version.

Wisconsin PCA-based Stellar Masses and Velocity Dispersions

Wisconsin stellar masses and velocity dispersions are derived from the optical rest-frame spectral region (3700-5500 Å) using a principal component analysis (PCA) method (Chen et al. 2012). The estimation is based on a library of model spectra generated using the single stellar population models of Bruzal & Charlot (2003), assuming a Kroupa (2001) initial mass function, and with a broad range of star-formation histories, metallicities, dust extinctions, and stellar velocity dispersions.


The different stellar mass estimates for BOSS galaxies encompass calculations based on different stellar population models (Portsmouth, Maraston 2005; Wisconsin, Bruzal & Charlot 2003), different assumptions regarding galaxy star formation histories and reddening, as well as multiple choices for the initial mass function and stellar-mass loss rates.

In addition, each method focuses on a different aspect of the available imaging and spectroscopic data. The Portsmouth SED fitting focuses on broad-band colors and BOSS redshifts, the Portsmouth emission-line fitting focuses on specific regions of the spectrum that contain specific information on gas and stellar kinematics, and the Wisconsin PCA analysis uses the rest-frame 3700-5500 Å stellar continuum.


Bolton, A. S., Schlegel, D. J., Aubourg, É., Bailey, S., Bizyaev D., Bhardwaj, V., Brewington, H., Brownstein, J. R., Burles, S., Chen, Y., Dawson, K., Ebelke G., Eisenstein, D. J., Malanushenko, E., Malanushenko, V., Maraston, C., Myers, A. D., Olmstead, M. D., Oravetz, D., Padmanabhan N., Pan, K., P├óris, I., Percival, W. J., Petitjean, P., Ross, N. P., Schneider, D. P., Shelden A., Shu, Y., Simmons, A., Snedden, S., Strauss, M. A., Thomas. D., Tremonti, C. A., Wake, D. A., Weaver, B. A., Wood-Vasey, W. M., 2012, AJ, 144, 144, doi:10.1088/0004-6256/144/5/144.

Brinchmann, J., Charlot, S., White, S.D.M., Tremonti, C.A., Kauffmann, G., Heckman, T.M., & Brinkmann, J., 2004, MNRAS, doi:10.1111/j.1365-2966.2004.07881.x.

Bruzal, G. & Charlot, S., 2003, MNRAS, 344(4), 1000, doi:10.1046/j.1365-8711.2003.06897.x.

Chen, Y.-M., et al. 2012, MNRAS, 421, 314, doi:10.1111/j.1365-2966.2011.20306.x.

Kauffmann, G., Heckman, T.M., White, S.D.M., Charlot, S., Tremonti, C.A., Brinchmann, J., Bruzal, G., Peng, E.W., Seibert, M., Bernardi, M., Blanton, M., Brikmann, J., Castander, F., Csábai, I., Fukugita, M., Ivezić, Ž., Munn, J.A., Nichol, R.C., Padmanabhan, N., Thakar, A.R., Weinberg, D.H., & York, D., 2003, MNRAS, 341(1), 33, doi:10.1046/j.1365-8711.2003.06291.x.

Kroupa, P., 2001, MNRAS, 322(2), 231, doi:10.1046/j.1365-8711.2001.04022.x.

Maraston, C., 2005, MNRAS, 362(3), 799, doi:10.1111/j.1365-2966.2005.09270.x.

Maraston, C., Daddi, E., Renzini, A., Cimatti, A., Dickinson, M., Papovich, C., Pasquali, A., & Pirzkal, N., 2006, ApJ, 652, 85, doi:10.1086/508143.

Maraston, C., Strömbäck, G., Thomas, D., Wake, D.A., Nichol, R.C., 2009, MNRAS Letters, 394(1), L107, doi:10.1111/j.1745-3933.2009.00621.x.

Maraston, C., Strömbäck, G., 2011, MNRAS Letters, 394(1), L107, doi:10.1111/j.1365-2966.2011.19738.x.

Maraston, C., Pforr, J., Henriques, B., Thomas, D., Wake, D., Bundy, K., Skibba, R., Beifiori, A., Brownstein, J., Capozzi, D., Edmondson, E., & Ross, N., 2012, arXiv:1207.6114, Submitted to MNRAS

Salpeter, E.E., 1955, ApJ, 121, 161, doi:10.1086/145971

Thomas, D., Steele, O., Maraston, C., Johansson, J., Beifiori, A., Pforr, J., Strömbäck, G., Tremonti, C. A., Wake, D., Bizyaev, D., Bolton, A., Brewington, H., Brownstein, J. R., Comparat, J., Kneib, J.-P., Malanushenko, E., Malanushenko, V. , Oravetz, D., Pan, K., Parejko, J. K., Schneider, D. P., Shelden, A.,Simmons, A., Snedden, S., Tanaka, M., Weaver, B. A.,Yan, R., 2013, MNRAS, 431(2), 1383.

Tremonti, C.A., Heckman, T.M., Kauffmann, G., Brinchmann, J., Charlot, S., White, S.D.M., Seibert, M., Peng, E.W., Schlegel, D.J., Uomoto, A., Fukugita, M., & Brinkmann, J., 2004, ApJ, 613, 898, doi:10.1086/423264.