STAR NRE: solving supernova selection effects with set-based truncated auto-regressive neural ratio estimation
File(s)
Published version
Author(s)
Karchev, Konstantin
Trotta, Roberto
Type
Journal Article
Abstract
Accounting for selection effects in supernova type Ia (SN Ia) cosmology is crucial for unbiased cosmological parameter inference — even more so for the next generation of large, mostly photometric-only surveys. The conventional “bias correction” procedure has a built-in systematic bias towards the fiducial model used to derive it and fails to account for the additional Eddington bias that arises in the presence of significant redshift uncertainty. On the other hand, likelihood-based analyses within a Bayesian hierarchical model, e.g. using MCMC, scale poorly with the data set size and require explicit assumptions for the selection function that may be inaccurate or contrived. To address these limitations, we introduce STAR NRE, a simulation-based approach that makes use of a conditioned deep set neural network and combines efficient high-dimensional global inference with subsampling-based truncation in order to scale to very large survey sizes while training on sets with varying cardinality. Applying it to a simplified SN Ia model consisting of standardised brightnesses and redshifts with Gaussian uncertainties and a selection procedure based on the expected LSST sensitivity, we demonstrate precise and unbiased inference of cosmological parameters and the redshift evolution of the volumetric SN Ia rate from ≈ 100 000 mock SNæ Ia. Our inference procedure can incorporate arbitrarily complex selection criteria, including transient classification, in the forward simulator and be applied to complex data like light curves. We outline these and other steps aimed at integrating STAR NRE into an end-to-end simulation-based pipeline for the analysis of future photometric-only SN Ia data.
Date Issued
2025-07-01
Date Acceptance
2025-05-28
Citation
Journal of Cosmology and Astroparticle Physics, 2025, 2025 (7)
ISSN
1475-7516
Publisher
IOP Publishing
Journal / Book Title
Journal of Cosmology and Astroparticle Physics
Volume
2025
Issue
7
Copyright Statement
© 2025 The Author(s). Published by IOP Publishing Ltd on behalf of Sissa Medialab. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
License URL
Subjects
Astronomy & Astrophysics
Bayesian reasoning
IA SUPERNOVAE
INFERENCE
Machine learning
Physical Sciences
Physics
Physics, Particles & Fields
Science & Technology
supernova type Ia-standard candles
Publication Status
Published
Article Number
031
Date Publish Online
2025-07-10