Accounting for Selection Bias Using Simulations: A General Method and an Application to Millimeter-wavelength Surveys

Abstract

We have developed a new Bayesian method to correct the flux densities of astronomical sources. The hybrid method combines a simulated likelihood to model survey selection together with an analytic source-count-based prior. The simulated likelihood captures the effect of complicated selection methods, such as multi-frequency filtering or imposed restrictions on recovered sample properties (e.g., color cuts). Simulations are also able to capture unanticipated sources of uncertainty. In this way, the method enables a broader application of Bayesian techniques. Use of an analytic prior allows variation of assumed source count models without re-simulating the likelihood. We present the method along with a detailed description of an application to real survey data from the Atacama Cosmology Telescope.