用模拟引力透镜超新星和神经网络，实现高精度自动测哈勃常数。
Inferring the Hubble Constant Using Simulated Strongly Lensed Supernovae and Neural Network Ensembles
- 用5个卷积神经网络集成学习模拟透镜超新星图像
- 100个系统可得4.4%精度的哈勃常数估计
- 适合做宇宙学快速测量的自动化工具
强引力透镜超新星是独立测量哈勃常数（H₀）的有前景新方法。本文利用模拟的引力透镜型Ia超新星（glSNe Ia）训练机器学习流水线以约束H₀。模拟了未来南希·格雷斯·罗曼空间望远镜观测到的glSNe Ia图像时间序列，用于训练由五个卷积神经网络组成的集成模型。该集成网络输出结合基于模拟的推断（SBI）框架，量化预测不确定性并推导出完整的H₀后验分布。结果表明，多个glSN系统的组合显著提升约束精度，基于100个模拟系统可得4.4%精度的H₀估计，与真实值一致。研究展示了将机器学习与glSN系统结合，在实现快速、自动化H₀测量方面的潜力。
原文摘要 · Abstract (English)
Strongly lensed supernovae are a promising new probe to obtain independent measurements of the Hubble constant (${H_0}$). In this work, we employ simulated gravitationally lensed Type Ia supernovae (glSNe Ia) to train our machine learning (ML) pipeline to constrain $H_0$. We simulate image time-series of glSNIa, as observed with the upcoming Nancy Grace Roman Space Telescope, that we employ for training an ensemble of five convolutional neural networks (CNNs). The outputs of this ensemble network are combined with a simulation-based inference (SBI) framework to quantify the uncertainties on the network predictions and infer full posteriors for the $H_0$ estimates. We illustrate that the combination of multiple glSN systems enhances constraint precision, providing a $4.4\%$ estimate of $H_0$ based on 100 simulated systems, which is in agreement with the ground truth. This research highlights the potential of leveraging the capabilities of ML with glSNe systems to obtain a pipeline capable of fast and automated $H_0$ measurements.
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