Optical Spectra: Galaxy Properties

DR14 includes legacy sets of estimates for intrinsic properties of galaxies from DR12 (includes three different estimates for the stellar mass and velocity dispersion estimators for galaxies) plus a legacy set of estimates run on only the DR8 spectra. The four methods are described below. However, for eBOSS, luminous red galaxies (LRGs) and emission-line galaxies (ELGs) are selected to be at higher redshift and are considerably fainter and have much lower S/N both in photometry and spectroscopy than those in the legacy samples. This has posed a challenge for the derivation of the intrinsic properties of these targets. Currently the team is working on two approaches to circumvent this difficulty: (1) use deeper imaging data made available by the new DECaLS survey and derive properties for individual galaxies with the higher-quality photometry with techniques similar to the Portsmouth SED-fitting method (Maraston et al. 2013); (2) group sources into subsamples with similar properties, construct high S/N composite spectra, and derive average properties of each subsample of galaxies. As of DR13, the higher-redshift coverage enables access to the near-ultraviolet wavelength region, which includes a collection of atomic absorption and emission lines that are informative for the baryonic processes in galaxy formation (Zhu et al. 2015), included as a value-added catalog.

Greater detail about each method is available for:

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 12 includes these derived quantities based on three methods, plus a legacy set of determinations that was originally released in DR8 and is included here for completeness.

The Portsmouth SED-fit Stellar Masses, the Portsmouth Stellar Kinematics and Emission Line Fluxes and Wisconsin (PCA) Galaxy Properties have been available since DR9. The Granada SED-fit Stellar Masses based on FSPS have been available since DR10. Each of these products is currently available for BOSS and SDSS spectra from Data Release 8. Maraston 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.

The Portsmouth, Wisconsin and Granada 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). For further details of the galaxy selection see the section “Selection of Galaxies” below. 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.

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 12, 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. 2012), 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).

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 Ångstroms) 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.

Granada FSPS Stellar Masses

Granada FSPS stellar masses (Ahn et al. 2014, Montero-Dorta et al. in preparation) 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 models based on a Flexible Stellar Population Synthesis (FSPS, Conroy et al. 2009) grid of templates. The flexibility of the model templates allows for a variety of formation-time scenarios including early formation-time (with and without dust), and a wide range of formation times. Calculations have been carried out for both Salpeter (1955) and Kroupa (2001) initial mass functions.

MPA-JHU Stellar Masses (for DR8 spectra only)

For DR8 galaxy spectra (virtually all of which were in DR7 too) we provide the galSpec galaxy properties from MPA-JHU. These properties are deprecated in DR12 in favor of the Wisconsin, Portsmouth, and Granada team analyses of the same data, but are provided in DR12 for comparison with the other galaxy property measurements listed here.

Selection of Galaxies

The detailed selection of galaxies for processing by the galaxy pipeline is given by the algorithm below, written in the IDL language. For BOSS objects, the selection is fairly simple: galaxies that are not accidentally matched to a QSO, with positive definite redshifts. For SDSS/DR8 Legacy galaxies, extended objects that were accidentally mis-classified as stars are also included.

The selection also guarantees that spectroscopically classified QSOs will not be processed, no matter how interesting their emission lines might be.

IF KEYWORD_SET(noqso) THEN BEGIN
    ; BOSS objects
    galaxy_noqso = (STRMATCH(spZbest.objtype,'GALAXY*') AND STRMATCH(spZbest.class_noqso,'GALAXY*'))
    galaxy_class = (NOT STRMATCH(spZbest.objtype,'GALAXY*') AND STRMATCH(spZbest.class,'GALAXY*'))
    good_zBest = (spZbest.zwarning_noqso EQ 0 AND spZbest.z_err_noqso ge 0. AND spZbest.z_noqso GT spZbest.z_err_noqso)
    wh = WHERE((galaxy_noqso OR galaxy_class) AND good_zBest, nobj)
ENDIF ELSE BEGIN
    ; SDSS objects
    galaxy_class = STRMATCH(spZbest.class,'GALAXY*')
    star_class = STRMATCH(spZbest.class,'STAR*')
    good_zBest = (spZbest.zwarning EQ 0 AND spZbest.z_err GE 0. AND spZbest.z GT spZbest.z_err)

    sdss_flux2lups, photoPosPlateRow.modelflux, sdss_model, /noivar
    sdss_rmag= sdss_model[2,*]
    sdss_flux2lups, photoPosPlateRow.psfflux, sdss_psf, /noivar
    sdss_pmm= sdss_psf[2,*]-sdss_rmag

    ; if it is extended
    sdss_pmmlimit= 0.1+EXP((14.-sdss_rmag)*0.3)
    extended = photoPosPlateRow.objc_type EQ 3 AND sdss_pmm GT sdss_pmmlimit
    star_extended = star_class AND extended
        
    wh = WHERE((galaxy_class OR star_extended) AND good_zBest, nobj)
ENDELSE

Comparison

The different stellar mass estimates for BOSS galaxies encompass calculations based on different stellar population models (Portsmouth, Maraston 2005; Wisconsin, Bruzal & Charlot 2003; Granada FSPS, Conroy et al. 2009), 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 and the Granada FSPS 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.

The WMAP 7 flat ΛCDM cosmology with H0 = 70, Ωm = 0.274, and ΩΛ = 0.726. (White et al. 2011) is applied universally to each of the Portsmouth-Wisconsin-Granada computations by the BOSS Pipeline.

References

Ahn, C. et al. 2014, ApJS, 211 ,17, doi:10.1088/0067-0049/211/2/17/

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., Pâris, 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.

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.

Conroy, C.; Gunn, J. E. & White, M., 2009, APJ, 699, 486 doi:10.1088/0004-637X/699/1/486.

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

Montero-Dorta, A. D.; Prada, F. (in preparation)

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.

White, M.; Blanton, M.; Bolton, A.; Schlegel, D.; Tinker, J.; Berlind, A.; da Costa, L.; Kazin, E.; Lin, Y. T.; Maia, M.; McBride, C. K.; Padmanabhan, N.; Parejko, J.; Percival, W.; Prada, F.; Ramos, B.; Sheldon, E.; de Simoni, F.; Skibba, R.; Thomas, D.; Wake, D.; Zehavi, I.; Zheng, Z.; Nichol, R.; Schneider, D. P.; Strauss, M. A.; Weaver, B. A.; Weinberg, D. H. & White, M.; & 2011, APJ, 728, 126, doi:10.1088/0004-637X/728/2/126