Bayesian model reconstruction based on spectral line observations
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866910589609050112 |
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| author | De Ceuster, Frederik Ceulemans, Thomas Decin, Leen Danilovich, Taïssa Yates, Jeremy |
| author_facet | De Ceuster, Frederik Ceulemans, Thomas Decin, Leen Danilovich, Taïssa Yates, Jeremy |
| contents | Spectral line observations encode a wealth of information. A key challenge, therefore, lies in the interpretation of these observations in terms of models to derive the physical and chemical properties of the astronomical environments from which they arise. In this paper, we present pomme: an open-source Python package that allows users to retrieve 1D or 3D models of physical properties, such as chemical abundance, velocity, and temperature distributions of (optically thin) astrophysical media, based on spectral line observations. We discuss how prior knowledge, for instance, in the form of a steady-state hydrodynamics model, can be used to guide the retrieval process, and demonstrate our methods both on synthetic and real observations of cool stellar winds. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_18525 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Bayesian model reconstruction based on spectral line observations De Ceuster, Frederik Ceulemans, Thomas Decin, Leen Danilovich, Taïssa Yates, Jeremy Instrumentation and Methods for Astrophysics Solar and Stellar Astrophysics Spectral line observations encode a wealth of information. A key challenge, therefore, lies in the interpretation of these observations in terms of models to derive the physical and chemical properties of the astronomical environments from which they arise. In this paper, we present pomme: an open-source Python package that allows users to retrieve 1D or 3D models of physical properties, such as chemical abundance, velocity, and temperature distributions of (optically thin) astrophysical media, based on spectral line observations. We discuss how prior knowledge, for instance, in the form of a steady-state hydrodynamics model, can be used to guide the retrieval process, and demonstrate our methods both on synthetic and real observations of cool stellar winds. |
| title | Bayesian model reconstruction based on spectral line observations |
| topic | Instrumentation and Methods for Astrophysics Solar and Stellar Astrophysics |
| url | https://arxiv.org/abs/2402.18525 |