Particle Astrophysics Seminar: Redshift Inference from the Combination of Galaxy Colors and Clustering in a Hierarchical Bayesian Model

  • Dec. 2, 2019, 2:00 pm
  • Curia II

Speaker: Alex Alarcon, Argonne National Laboratory
Abstract: Photometric galaxy surveys constitute a powerful cosmological probe but rely on the accurate characterization of their redshift distributions using only broadband imaging, and can be very sensitive to incomplete or biased priors used for redshift calibration. Several techniques for estimating the redshift distributions of imaging surveys have been developed in the last decades, which can be broadly separated in three categories: 1) direct calibration through spectroscopic redshifts, 2) mapping the relation between photometry and redshift with a mix of theoretical models of galaxy spectra and empirical knowledge from direct spectroscopy, and 3) comparing the sky positions of galaxies to the positions of a tracer population with secure redshifts. In this work we extend a hierarchical Bayesian model which combines these three main sources of information so it can be applied to real data. We test the method in N-body simulations and find the incorporation of clustering information on top of photometry to tighten the redshift posteriors and overcome biases in the prior that mimic those happening in spectroscopic samples. This robustness to flaws in the redshift prior or training samples would constitute a milestone for the control of redshift systematic uncertainties in future weak lensing analyses.