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FlowSN Normalising Flows for Simulation-Based Inference under Realistic Selection Effects applied to Supernova Cosmology

BM Boyd, KS Mandel, M Grayling, A Mitra… - arXiv preprint arXiv …, 2026 - arxiv.org
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… statistical framework using simulation-based inference with … Simulation-based inference enables us to implicitly … over other simulation-based inference approaches. We …

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@article{2603.11165v2,
Author        = {Benjamin M. Boyd and Kaisey S. Mandel and Matthew Grayling and Ayan Mitra and Richard Kessler and Maximilian Autenrieth and Aaron Do and Madeleine Ginolin and Lisa Kelsey and Gautham Narayan and Matthew O'Callaghan and Nikhil Sarin and Stephen Thorp},
Title         = {FlowSN: Neural Simulation-Based Inference under Realistic Selection Effects applied to Supernova Cosmology},
Eprint        = {2603.11165v2},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.CO},
Abstract      = {We present FlowSN, a statistical framework using simulation-based inference (SBI) with normalising flows to account for selection effects in observational astronomy. Failure to account for selection effects can lead to biased inference on global parameters. An example is Malmquist bias, where detection limits result in a sample skewed towards brighter objects. In Type Ia supernova (SN Ia) cosmology, these selection effects can systematically shift the inferred posterior distributions of cosmological parameters, necessitating the development of robust statistical frameworks to account for the biases. SBI enables us to implicitly learn probability distributions that are analytically intractable to calculate. In this work, we introduce a novel approach that employs a normalising flow to learn the non-analytic selected SN likelihood for a given survey from forward simulations, independent of the assumed cosmological model. The resulting likelihood approximation is incorporated into a hierarchical Bayesian framework and posterior sampling is performed using Hamiltonian Monte Carlo to obtain constraints on cosmological parameters conditioned on the observed data. The modular learnt likelihood approximation can be reused without retraining to evaluate different cosmological models, providing a key advantage over other SBI approaches. We demonstrate the performance of this methodology by training and testing the SBI technique using realistic LSST-like SNANA simulations for the first time. Our FlowSN approach yields accurate posterior estimates on cosmological parameters, including the dark energy equation of state $w_0$, that are an order of magnitude less biased than those obtained with conventional techniques and also exhibit improved frequentist calibration.},
Year          = {2026},
Month         = {Mar},
Url           = {http://arxiv.org/abs/2603.11165v2},
File          = {2603.11165v2.pdf}
}

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