Neural Simulation-Based Inference of the Neutron Star Equation of State directly from Telescope Spectra
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866912011847204864 |
|---|---|
| author | Brandes, Len Modi, Chirag Ghosh, Aishik Farrell, Delaney Lindblom, Lee Heinrich, Lukas Steiner, Andrew W. Weber, Fridolin Whiteson, Daniel |
| author_facet | Brandes, Len Modi, Chirag Ghosh, Aishik Farrell, Delaney Lindblom, Lee Heinrich, Lukas Steiner, Andrew W. Weber, Fridolin Whiteson, Daniel |
| contents | Neutron stars provide a unique opportunity to study strongly interacting matter under extreme density conditions. The intricacies of matter inside neutron stars and their equation of state are not directly visible, but determine bulk properties, such as mass and radius, which affect the star's thermal X-ray emissions. However, the telescope spectra of these emissions are also affected by the stellar distance, hydrogen column, and effective surface temperature, which are not always well-constrained. Uncertainties on these nuisance parameters must be accounted for when making a robust estimation of the equation of state. In this study, we develop a novel methodology that, for the first time, can infer the full posterior distribution of both the equation of state and nuisance parameters directly from telescope observations. This method relies on the use of neural likelihood estimation, in which normalizing flows use samples of simulated telescope data to learn the likelihood of the neutron star spectra as a function of these parameters, coupled with Hamiltonian Monte Carlo methods to efficiently sample from the corresponding posterior distribution. Our approach surpasses the accuracy of previous methods, improves the interpretability of the results by providing access to the full posterior distribution, and naturally scales to a growing number of neutron star observations expected in the coming years. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_00287 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Neural Simulation-Based Inference of the Neutron Star Equation of State directly from Telescope Spectra Brandes, Len Modi, Chirag Ghosh, Aishik Farrell, Delaney Lindblom, Lee Heinrich, Lukas Steiner, Andrew W. Weber, Fridolin Whiteson, Daniel High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics General Relativity and Quantum Cosmology High Energy Physics - Phenomenology Nuclear Theory Neutron stars provide a unique opportunity to study strongly interacting matter under extreme density conditions. The intricacies of matter inside neutron stars and their equation of state are not directly visible, but determine bulk properties, such as mass and radius, which affect the star's thermal X-ray emissions. However, the telescope spectra of these emissions are also affected by the stellar distance, hydrogen column, and effective surface temperature, which are not always well-constrained. Uncertainties on these nuisance parameters must be accounted for when making a robust estimation of the equation of state. In this study, we develop a novel methodology that, for the first time, can infer the full posterior distribution of both the equation of state and nuisance parameters directly from telescope observations. This method relies on the use of neural likelihood estimation, in which normalizing flows use samples of simulated telescope data to learn the likelihood of the neutron star spectra as a function of these parameters, coupled with Hamiltonian Monte Carlo methods to efficiently sample from the corresponding posterior distribution. Our approach surpasses the accuracy of previous methods, improves the interpretability of the results by providing access to the full posterior distribution, and naturally scales to a growing number of neutron star observations expected in the coming years. |
| title | Neural Simulation-Based Inference of the Neutron Star Equation of State directly from Telescope Spectra |
| topic | High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics General Relativity and Quantum Cosmology High Energy Physics - Phenomenology Nuclear Theory |
| url | https://arxiv.org/abs/2403.00287 |