Evolution of The Velocity-Dispersion Function of Luminous Red Galaxies: A Hierarchical Bayesian Measurement

TL;DR

This paper introduces a hierarchical Bayesian method to accurately measure the velocity-dispersion function of luminous red galaxies, revealing evolution in the distribution's width with redshift while accounting for observational uncertainties.

Contribution

The study develops a novel hierarchical Bayesian approach that corrects for measurement errors and biases, providing a self-consistent and unbiased estimation of galaxy velocity-dispersion distributions over redshift.

Findings

Detected significant evolution in velocity-dispersion distribution width with redshift.

Demonstrated unbiased parameter estimates using deep re-observations.

Showed the method's robustness against spectroscopic SNR variations.

Abstract

We present a hierarchical Bayesian determination of the velocity-dispersion function of approximately 430,000 massive luminous red galaxies(LRGs) observed at relatively low spectroscopic signal-to-noise ratio (SNR ~3-5 per 69 km s^(-1)) by the Baryon Oscillation Spectroscopic Survey (BOSS) of the Sloan Digital Sky Survey III (SDSS-III). We marginalize over spectroscopic redshift errors, and use the full velocity-dispersion likelihood function for each galaxy to make a self-consistent determination of the velocity-dispersion distribution parameters as a function of absolute magnitude and redshift, correcting as well for the effects of broadband magnitude errors on our binning. Parameterizing the distribution at each point in the luminosity-redshift plane with a log-normal form, we detect significant evolution in the width of the distribution toward higher intrinsic scatter at higher redshifts. Using a subset of deep re-observations of BOSS galaxies, we demonstrate that our distribution-parameter estimates are unbiased regardless of spectroscopic SNR. We also show through simulation that our method introduces no systematic parameter bias with redshift. We highlight the advantage of the hierarchical Bayesian method over frequentist "stacking" of spectra, and illustrate how our measured distribution parameters can be adopted as informative priors for velocity-dispersion measurements from individual noisy spectra.