A Simulation Based Inference Approach to Modelling of Type Ia Supernova Populations
Abstract
Type Ia Supernovae (SNe Ia) are prominent cosmological probes, utilising a standardisation process to reduce their observed scatter to ∼0.15 mag. A growing number of models seek to explain this remaining intrinsic scatter, based on a diversity of dust properties and possible connections to the progenitor systems. Inference of new models has been limited due to the cost of simulations and attendant complexity. Here, we present Stjörnumál, a simulation based inference pipeline to infer intrinsic and extrinsic parameters of SNe Ia, an upgrade to previous SN Ia modelling attempts with SALT, e.g. Dust2Dust. Stjörnumál provides fast and accurate posterior inference via Neural Posterior Estimation, integrated model comparison with Neural Ratio Estimation, and overall significant speed and quality-of-life upgrades. We fit the Dark Energy Survey (DES) 5-year SN sample, finding good agreement with previously-published dust model parameters for DES5YR. We test 7 models of SN Ia behaviour, finding that more data is needed to break degeneracies between RV models, but sufficient to evidence (log(10) Bayes Factor = +1.9, fmix = 0.8) against two populations of SNe Ia at high-redshift. We employ a combination of frequentist χ2 metrics and Bayesian model comparison to make model determinations, finding neither are sufficient on their own to properly compare models. For our nominal model, we find a smaller ΔRV = 0.8 for our nominal model than previous SALT-based attempts. We test our model for consistency against our assumed cosmology, and find our results are robust to |Δw| < 0.10. The code is publicly available at https://github.com/bap37/Stjornumal, and presents an opportunity to flexibly and rapidly test potential models of SNe Ia scatter in a common framework.
Related
- FPCA-Enhanced Simulation-Based Inference for Robust Type Ia Supernova Cosmology
- Type Ia supernova population machine – I. A unified cosmological simulation–binary synthesis framework establishing non-universal delay-time distributions and cosmic progenitor-channel dominance crossover
- 2Flow2SN: Dusty Supernova Cosmology with Photometric Redshifts and Contamination using Neural Simulation-Based Inference
- Charge transfer of O ⁺ with Si, S, Mg, and Ne for supernova ejecta modelling
- Exploring the capabilities of combined modelling strategies for supernova with relativistic jets using Redback
BibTeX
@misc{popovic2026simulation,
title = {{A Simulation Based Inference Approach to Modelling of Type Ia Supernova Populations}},
author = {B. Popovic and M. Grayling and M. O'Callaghan and B. M. Boyd and K. Mandel and P. Wiseman and B. Carreres and N. Shiamtanis and D. Scolnic and E. Charleton and Y. Murakami},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2607.28725},
eprint = {2607.28725},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2607.28725},
note = {CC BY-SA 4.0},
}
RIS
TY - UNPB
TI - A Simulation Based Inference Approach to Modelling of Type Ia Supernova Populations
AU - B. Popovic
AU - M. Grayling
AU - M. O'Callaghan
AU - B. M. Boyd
AU - K. Mandel
AU - P. Wiseman
AU - B. Carreres
AU - N. Shiamtanis
AU - D. Scolnic
AU - E. Charleton
AU - Y. Murakami
PY - 2026
DA - 2026/09/17
PB - arXiv
DO - 10.48550/arxiv.2607.28725
UR - https://arxiv.org/abs/2607.28725
AB - Type Ia Supernovae (SNe Ia) are prominent cosmological probes, utilising a standardisation process to reduce their observed scatter to ∼0.15 mag. A growing number of models seek to explain this remaining intrinsic scatter, based on a diversity of dust properties and possible connections to the progenitor systems. Inference of new models has been limited due to the cost of simulations and attendant complexity. Here, we present Stjörnumál, a simulation based inference pipeline to infer intrinsic and extrinsic parameters of SNe Ia, an upgrade to previous SN Ia modelling attempts with SALT, e.g. Dust2Dust. Stjörnumál provides fast and accurate posterior inference via Neural Posterior Estimation, integrated model comparison with Neural Ratio Estimation, and overall significant speed and quality-of-life upgrades. We fit the Dark Energy Survey (DES) 5-year SN sample, finding good agreement with previously-published dust model parameters for DES5YR. We test 7 models of SN Ia behaviour, finding that more data is needed to break degeneracies between R_V models, but sufficient to evidence (log(10) Bayes Factor = +1.9, f_mix = 0.8) against two populations of SNe Ia at high-redshift. We employ a combination of frequentist χ^2 metrics and Bayesian model comparison to make model determinations, finding neither are sufficient on their own to properly compare models. For our nominal model, we find a smaller ΔR_V = 0.8 for our nominal model than previous SALT-based attempts. We test our model for consistency against our assumed cosmology, and find our results are robust to |Δw| < 0.10. The code is publicly available at https://github.com/bap37/Stjornumal, and presents an opportunity to flexibly and rapidly test potential models of SNe Ia scatter in a common framework.
ER -