论文标题
在Vera Rubin天文台的时代，使用标准化流量的摊销贝叶斯推断超新星的推断
Amortized Bayesian Inference for Supernovae in the Era of the Vera Rubin Observatory Using Normalizing Flows
论文作者
论文摘要
Vera Rubin天文台将于2024年中期开始观察，将使我们的超新星的发现率每年增加到超过一百万。有很大的推动力发展新方法，以识别，分类并最终了解鲁宾天文台发现的数百万个超新星。在这里，我们提出了使用标准化流量的第一个基于模拟的推理方法，该方法经过训练，可以快速推断出多元类似Rubin的数据源中玩具超新星模型的参数。我们发现，与传统推理方法（特别是MCMC）相比，我们的方法经过了良好的校准，在测试时间内仅需要千分之一的CPU小时。
The Vera Rubin Observatory, set to begin observations in mid-2024, will increase our discovery rate of supernovae to well over one million annually. There has been a significant push to develop new methodologies to identify, classify and ultimately understand the millions of supernovae discovered with the Rubin Observatory. Here, we present the first simulation-based inference method using normalizing flows, trained to rapidly infer the parameters of toy supernovae model in multivariate, Rubin-like datastreams. We find that our method is well-calibrated compared to traditional inference methodologies (specifically MCMC), requiring only one-ten-thousandth of the CPU hours during test time.
