Session 3I: Bayesian Statistics and Estimating Stellar Masses in the Blind Cosmology Challenge and the Dark Energy Survey
Session Number
Session 3I: 4th Presentation
Advisor(s)
James Annis, Fermilab
Location
Room B108
Start Date
28-4-2017 1:15 PM
End Date
28-4-2017 2:30 PM
Abstract
The purpose of this investigation was to update, refine, and apply the techniques and strategies developed over the course of the last two years to produce a new cluster richness proxy, called M*, based off of stellar masses derived through Bayesian statistical methods. Bayesian models derived from Simha et al (2014) and Conroy and Gunn's FSPS were updated to incorporate new versions of FSPS. Our analysis of data from the BCC mirrors the results of the Canada France Legacy Survey (CFLS) for high mass galaxy clusters (clusters of at least 10^13 stellar masses), and discrepancies at low mass can be explained due to inconsistencies in how BCC produces low-mass clusters. When applied to Stripe 82 of the Dark Energy Survey Data Release 8, our analysis mirrors the results of the CFLS. From this, we conclude that this Bayesian technique provides not the best fit value, but rather, the most likely value, our estimates for stellar mass could be useful as a proxy for the galaxy richness, a key variable when analyzing and modeling the cosmology of a cluster.
Session 3I: Bayesian Statistics and Estimating Stellar Masses in the Blind Cosmology Challenge and the Dark Energy Survey
Room B108
The purpose of this investigation was to update, refine, and apply the techniques and strategies developed over the course of the last two years to produce a new cluster richness proxy, called M*, based off of stellar masses derived through Bayesian statistical methods. Bayesian models derived from Simha et al (2014) and Conroy and Gunn's FSPS were updated to incorporate new versions of FSPS. Our analysis of data from the BCC mirrors the results of the Canada France Legacy Survey (CFLS) for high mass galaxy clusters (clusters of at least 10^13 stellar masses), and discrepancies at low mass can be explained due to inconsistencies in how BCC produces low-mass clusters. When applied to Stripe 82 of the Dark Energy Survey Data Release 8, our analysis mirrors the results of the CFLS. From this, we conclude that this Bayesian technique provides not the best fit value, but rather, the most likely value, our estimates for stellar mass could be useful as a proxy for the galaxy richness, a key variable when analyzing and modeling the cosmology of a cluster.