FPCA增强的基于模拟推断用于稳健的Ia型超新星宇宙学
FPCA-Enhanced Simulation-Based Inference for Robust Type Ia Supernova Cosmology
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中文总结 AI 辅助
本研究首次将FPCA光变曲线参数作为SBI汇总统计量，在非平坦ΛCDM模型下实现稳健的超新星宇宙学推断，其约束与SALT2及显式似然方法相当，且在域外模拟和真实DES数据上表现更稳健，为统一分类与推断流水线奠定基础。
中文摘要 AI 辅助
精密宇宙学是现代天文学的基石，即将开展的大视场测光巡天使得开发稳健的、数据驱动的推断方法变得至关重要，这些方法能够处理复杂的巡天系统效应，而无需依赖封闭形式的似然函数。基于模拟的推断（SBI）通过启用编码复杂巡天特征的前向模拟来满足这一需求。此前在超新星宇宙学中的SBI分析使用SALT2光变曲线参数作为汇总统计量；然而，SALT2的双基谱模板施加了刚性的建模假设，这促使需要更灵活的表征。在本工作中，我们首次将函数主成分分析（FPCA）光变曲线参数作为汇总统计量，应用于非平坦ΛCDM模型下的基于SBI的宇宙学推断。在相同生成的模拟上，FPCA+SBI产生的约束与基于SALT2的SBI和显式似然分析相当，同时在域外模拟上提供比SALT2更稳健的约束。将基于类LSST光变曲线训练的模型应用于光谱确认的DES第五年超新星样本，我们恢复的约束与DES合作组的约束在Ωm和ΩΛ方面分别一致在0.12σ和0.23σ以内，确立了该方法对真实巡天数据的泛化能力。通过引入依赖于宿主的质量相关尘埃消光定律来引入宿主依赖的系统效应，我们发现基于FPCA的汇总统计量隐式编码了宿主依赖的系统效应，表明在此框架内可能无需专门的宿主建模。结合其先前在测光分类中展示的有效性，FPCA为统一的、数据驱动的流水线提供了引人注目的基础，该流水线能够联合执行超新星分类和宇宙学推断，以服务于即将开展的大视场测光巡天。
英文摘要
Precision cosmology is a cornerstone of modern astronomy, and upcoming wide-field photometric surveys make it crucial to develop robust, data-driven inference methods that can handle complex survey systematics without relying on closed-form likelihoods. Simulation-Based Inference (SBI) meets this need by enabling forward simulations that encode complex survey characteristics. Previous SBI analyses in supernova cosmology have used SALT2 light-curve parameters as summary statistics; however, SALT2's two-basis spectral template imposes rigid modeling assumptions, motivating a more flexible representation. In this work, we present the first application of Functional Principal Component Analysis (FPCA) light-curve parameters as summary statistics for SBI-based cosmological inference under a non-flat ΛCDM model. On identically generated simulations, FPCA+SBI yields constraints comparable to both SALT2-based SBI and explicit-likelihood analyses, while providing more robust constraints on out-of-domain simulations than SALT2. Applying the model trained on LSST-like light curves to a spectroscopically confirmed DES Year 5 supernova sample, we recover constraints consistent with those of the DES collaboration to within 0.12 σ and 0.23 σ in Ωm and ΩΛ respectively, establishing the method's generalizability to real survey data. By introducing host-dependent systematics through a mass-dependent dust extinction law, we find FPCA-based summary statistics implicitly encode host-dependent systematics, suggesting dedicated host modeling may be unnecessary within this framework. Combined with its previously demonstrated effectiveness for photometric classification, FPCA offers a compelling foundation for unified, data-driven pipelines capable of jointly performing supernova classification and cosmological inference for upcoming wide-field photometric surveys.
发表机构
- Baylor University（贝勒大学）
- Department of Physics and Astronomy, Texas A&M University（德克萨斯农工大学物理与天文系）
- George P. and Cynthia Woods Mitchell Institute for Fundamental Physics and Astronomy, Texas A&M University（德克萨斯农工大学乔治·P·辛西娅·伍兹基础物理与天文学研究所）
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