2Flow2SN: Dusty Supernova Cosmology with Photometric Redshifts and Contamination using Neural Simulation-Based Inference
Abstract
Photometric Type Ia supernova (SN Ia) cosmology in the era of the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) demands statistical frameworks that simultaneously account for selection effects, photometric redshift uncertainties and non-Ia contamination at scale. We present FlowSN2, a modular statistical framework combining hierarchical Bayesian modelling and simulation-based inference (SBI), that reframes photometric SN cosmology from a regression task to a joint inference problem over light curve summaries and photometric redshifts. By employing neural likelihood estimation via normalising flows, FlowSN2 approximates the data distributions of selected objects in a form that can be reused across different cosmological models, achieving amortisation over the Hubble diagram. This amortisation enables inference across alternative cosmological parameterisations, such as w0waCDM, away from the fiducial training cosmology. The framework integrates a standardisation model that disentangles intrinsic SN variations from extrinsic host-galaxy dust effects and adopts a principled Bayesian resampling approach to propagate SN classification uncertainty, without requiring an explicit contaminant likelihood. Across simulated tests spanning five cosmological parameter sets, our framework recovers true cosmological and population parameters, with posteriors in close agreement with both tractable analytical solutions and pure-Ia baselines. Conveniently, the framework enables Bayesian model comparison at negligible additional computational cost, providing a valuable tool for addressing debates surrounding SN standardisation and evolving dark energy. Capable of scaling to over a hundred thousand SNe without retraining, FlowSN2 offers an interpretable and statistically principled solution for cosmological inference with upcoming wide-field photometric surveys.
Related
- FPCA-Enhanced Simulation-Based Inference for Robust Type Ia Supernova Cosmology
- FOMO-Z. Optimal galaxy classification, photometric redshifts, and reconstruction of population distributions
- Cosmic-neighbor-associated distances to VHE and candidate VHE BL Lacs via SDSS photometric redshifts
- Core-dominated non-Gaussian photometric redshifts in the COSMOS field: a normality test analysis
- Data-driven Galaxy Population Prior for Photometric Redshifts
BibTeX
@misc{boyd20262flow2sn,
title = {{2Flow2SN: Dusty Supernova Cosmology with Photometric Redshifts and Contamination using Neural Simulation-Based Inference}},
author = {Benjamin M. Boyd and Maximilian Autenrieth and Kaisey S. Mandel and Matthew Grayling and Madeleine Ginolin and Lisa Kelsey and Stephen Thorp},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.30244},
eprint = {2610.30244},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.30244},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - 2Flow2SN: Dusty Supernova Cosmology with Photometric Redshifts and Contamination using Neural Simulation-Based Inference
AU - Benjamin M. Boyd
AU - Maximilian Autenrieth
AU - Kaisey S. Mandel
AU - Matthew Grayling
AU - Madeleine Ginolin
AU - Lisa Kelsey
AU - Stephen Thorp
PY - 2026
DA - 2026/10/21
PB - arXiv
DO - 10.48550/arxiv.2610.30244
UR - https://arxiv.org/abs/2610.30244
AB - Photometric Type Ia supernova (SN Ia) cosmology in the era of the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) demands statistical frameworks that simultaneously account for selection effects, photometric redshift uncertainties and non-Ia contamination at scale. We present FlowSN2, a modular statistical framework combining hierarchical Bayesian modelling and simulation-based inference (SBI), that reframes photometric SN cosmology from a regression task to a joint inference problem over light curve summaries and photometric redshifts. By employing neural likelihood estimation via normalising flows, FlowSN2 approximates the data distributions of selected objects in a form that can be reused across different cosmological models, achieving amortisation over the Hubble diagram. This amortisation enables inference across alternative cosmological parameterisations, such as w_0w_aCDM, away from the fiducial training cosmology. The framework integrates a standardisation model that disentangles intrinsic SN variations from extrinsic host-galaxy dust effects and adopts a principled Bayesian resampling approach to propagate SN classification uncertainty, without requiring an explicit contaminant likelihood. Across simulated tests spanning five cosmological parameter sets, our framework recovers true cosmological and population parameters, with posteriors in close agreement with both tractable analytical solutions and pure-Ia baselines. Conveniently, the framework enables Bayesian model comparison at negligible additional computational cost, providing a valuable tool for addressing debates surrounding SN standardisation and evolving dark energy. Capable of scaling to over a hundred thousand SNe without retraining, FlowSN2 offers an interpretable and statistically principled solution for cosmological inference with upcoming wide-field photometric surveys.
ER -