a repo of all of the code associated with this project
The distribution of massive clusters of galaxies depends strongly on the total cosmic mass density, the mass variance, and the dark energy equation of state. As such, measures of galaxy clusters can provide constraints on these parameters and even test models of gravity, but only if observations of clusters can lead to accurate estimates of their total masses. Here, we carry out a study to investigate the ability of a blind spectroscopic survey to recover accurate galaxy cluster masses through their velocity dispersion using probability based and machine learning methods. We focus on the Hobby Eberly Telescope Dark Energy Experiment (HETDEX), which will employ new Visible Integral-Field Replicable Unit Spectrographs (VIRUS), over 420 degree2 on the sky with a 1/4.5 fill factor. VIRUS covers the blue/optical portion of the spectrum (3500-5500 Angstrom), allowing surveys to measure redshifts for a large sample of galaxies out to z < 0.5 based on their absorption features or [OII] 3727 emission (and Lyman-alpha over 1.9 < z < 3.5). We use a detailed mock galaxy catalog from a semi-analytic model to simulate surveys observed with VIRUS, including: (1) a blind, HETDEX-like survey with an incomplete but uniform spectroscopic selection function; and (2) IFU surveys that target clusters directly, obtaining spectra of all galaxies in a VIRUS-sized field. For both surveys, we include realistic uncertainties from galaxy magnitude and line-flux limits. We benchmark both surveys against spectroscopic observations with “perfect” knowledge of galaxy line-of-sight velocities. With HETDEX, we can recover cluster masses to ~ 0.1 dex which can be further improved to < 0.1 dex with targeted follow-up observations. This level of cluster mass recovery enables constraints on 8 to < 20%, and the unique properties of the observations will provide important calibrations for the optical richness-cluster mass relation.
- Steven Boada (Texas A&M University)
Copyright 2015 the authors.
Currently released under the MIT license.
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