Deducing neutron star equation of state parameters directly from telescope spectra with uncertainty-aware machine learning
Deducing neutron star equation of state parameters directly from telescope spectra with uncertainty-aware machine learning
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DOI：
10.1088/1475-7516/2023/02/016
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发表时间：
2022-09
影响因子：
6.4
通讯作者：
D. Farrell;Pierre Baldi;Jordan Ott;A. Ghosh;Andrew W. Steiner;Atharva M Kavitkar;Lee Lindblom;D. Whiteson;Fridolin Weber
中科院分区：
文献类型：
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作者：
D. Farrell;Pierre Baldi;Jordan Ott;A. Ghosh;Andrew W. Steiner;Atharva M Kavitkar;Lee Lindblom;D. Whiteson;Fridolin Weber
中子星为研究极端压力和密度下的物质提供了一个独特的实验室。虽然没有直接的方法来探索它们的内部结构，但从这些恒星发出的X射线可以通过推断星星的质量和半径，间接地为超致密核物质的状态方程（EOS）提供线索。然而，直接从星星的X射线光谱推断物态方程是极具挑战性的，并且由于系统的不确定性而变得复杂。目前的技术水平是使用基于模拟的似然性在一个分段的方法，依赖于某些理论假设和简化的不确定性。它首先推断出星星的质量和半径，以减少问题的维数，并从这些量推断出EOS。我们展示了一系列对最先进技术的增强，在现实的不确定性量化方面，以及规避理论假设的需要，用机器学习来推断物理特性的路径。我们还展示了直接从观测到的恒星的高维光谱的状态方程的新的推断，避免了中间质量半径步骤。我们的网络以每个星星的不确定性来源为条件，允许不确定性自然而完整地传播到EOS。
Neutron stars provide a unique laboratory for studying matter at extreme pressures and densities. While there is no direct way to explore their interior structure, X-rays emitted from these stars can indirectly provide clues to the equation of state (EOS) of the superdense nuclear matter through the inference of the star's mass and radius. However, inference of EOS directly from a star's X-ray spectra is extremely challenging and is complicated by systematic uncertainties. The current state of the art is to use simulation-based likelihoods in a piece-wise method which relies on certain theoretical assumptions and simplifications about the uncertainties. It first infers the star's mass and radius to reduce the dimensionality of the problem, and from those quantities infer the EOS. We demonstrate a series of enhancements to the state of the art, in terms of realistic uncertainty quantification and a path towards circumventing the need for theoretical assumptions to infer physical properties with machine learning. We also demonstrate novel inference of the EOS directly from the high-dimensional spectra of observed stars, avoiding the intermediate mass-radius step. Our network is conditioned on the sources of uncertainty of each star, allowing for natural and complete propagation of uncertainties to the EOS.