Accounting for Selection Effects in Supernova Cosmology with Simulation-Based Inference and Hierarchical Bayesian Modelling

Autor:	Boyd, Benjamin M., Grayling, Matthew, Thorp, Stephen, Mandel, Kaisey S.
Rok vydání:	2024
Předmět:	Astrophysics - Cosmology and Nongalactic Astrophysics Astrophysics - Instrumentation and Methods for Astrophysics
Druh dokumentu:	Working Paper
Popis:	Type Ia supernovae (SNe Ia) are thermonuclear exploding stars that can be used to put constraints on the nature of our universe. One challenge with population analyses of SNe Ia is Malmquist bias, where we preferentially observe the brighter SNe due to limitations of our telescopes. If untreated, this bias can propagate through to our posteriors on cosmological parameters. In this paper, we develop a novel technique of using a normalising flow to learn the non-analytical likelihood of observing a SN Ia for a given survey from simulations, that is independent of any cosmological model. The learnt likelihood is then used in a hierarchical Bayesian model with Hamiltonian Monte Carlo sampling to put constraints on different sets of cosmological parameters conditioned on the observed data. We verify this technique on toy model simulations finding excellent agreement with analytically-derived posteriors to within $1 \sigma$. Comment: Accepted for the ICML AI for Science Workshop (2024). 13 pages, 5 figures, 2 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.15923 Zobrazit plný text záznamu View this record from Arxiv