发表机构
University of Southampton; University of Cambridge; Duke University; Johns Hopkins University（南安普顿大学; 剑桥大学; 杜克大学; 约翰斯·霍普金斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于模拟的Stjörnumál推断管道，结合神经后验估计与神经比率估计，拟合DES 5年SNe样本，为SNe Ia种群建模提供高效工具，发现高红端或存在两类SNe Ia。
AI 中文摘要
Ia型超新星（SNe Ia）是重要的宇宙学探针，通过标准化过程将观测散度降至约0.15星等。越来越多的模型试图解释这种剩余的本征散度，其依据是尘埃性质的多样性以及与前身星系统的可能联系。由于模拟成本高且伴随复杂性，新模型的推断受到限制。本文提出Stjörnumál，一种基于模拟的SNe Ia本征和外禀参数推断管道，是对之前基于SALT的SNe Ia建模尝试（如Dust2Dust）的升级。Stjörnumál通过神经后验估计提供快速准确的后验推断，通过神经比率估计实现集成模型比较，且在速度和使用便捷性上有显著升级。我们对暗能量巡天（DES）5年SNe样本进行拟合，发现与已发表的DES5YR尘埃模型参数吻合良好。我们测试了7种SNe Ia行为模型，发现需要更多数据来打破R_V模型之间的简并性，但已有足够证据（对数10贝叶斯因子=+1.9，混合分数f_mix=0.8）支持高红端存在两类SNe Ia。我们结合频率论χ²度量和贝叶斯模型比较进行模型判定，发现二者单独使用均不足以正确比较模型。对于我们的标称模型，其ΔR_V=0.8，小于之前基于SALT的尝试。我们测试模型与假设宇宙学的一致性，发现结果在|Δw|<0.10范围内是稳健的。该代码可在指定URL公开获取，为在统一框架中灵活快速测试SNe Ia散度的潜在模型提供了机会。
英文摘要
Type Ia Supernovae (SNe Ia) are prominent cosmological probes, utilising a standardisation process to reduce their observed scatter to $\sim0.15$ 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 ($\log(10)~\textrm{Bayes Factor} = +1.9$, $f_{\rm mix} = 0.8$) 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.
