Deconvolving X-Ray Galaxy Cluster Spectra Using a Recurrent Inference Machine
Recent advances in machine learning algorithms have unlocked new insights in observational astronomy by allowing astronomers to probe new frontiers. In this article, we present a methodology to disentangle the intrinsic X-ray spectrum of galaxy clusters from the instrumental response function. Emplo...
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| Format: | Article |
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IOP Publishing
2025-01-01
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| Series: | The Astronomical Journal |
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| Online Access: | https://doi.org/10.3847/1538-3881/adbf7b |
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| author | Carter Lee Rhea Julie Hlavacek-Larrondo Alexandre Adam Ralph Kraft Ákos Bogdán Laurence Perreault-Levasseur Marine Prunier |
| author_facet | Carter Lee Rhea Julie Hlavacek-Larrondo Alexandre Adam Ralph Kraft Ákos Bogdán Laurence Perreault-Levasseur Marine Prunier |
| author_sort | Carter Lee Rhea |
| collection | DOAJ |
| description | Recent advances in machine learning algorithms have unlocked new insights in observational astronomy by allowing astronomers to probe new frontiers. In this article, we present a methodology to disentangle the intrinsic X-ray spectrum of galaxy clusters from the instrumental response function. Employing state-of-the-art modeling software and data mining techniques of the Chandra data archive, we construct a set of 100,000 mock Chandra spectra. We train a Recurrent Inference Machine (RIM) to take in the instrumental response and mock observation and output the intrinsic X-ray spectrum. The RIM can recover the mock intrinsic spectrum below the 1 σ error threshold; moreover, the RIM reconstruction of the mock observations is indistinguishable from the observations themselves. To further test the algorithm, we deconvolve extracted spectra from the central regions of the galaxy group NGC 1550, known to have a rich X-ray spectrum, and the massive galaxy clusters A1795. Despite the RIM reconstructions consistently remaining below the 1 σ noise level, the recovered intrinsic spectra did not align with modeled expectations. This discrepancy is likely attributable to the RIM’s method of implicitly encoding prior information within the neural network. This approach holds promise for unlocking new possibilities in accurate spectral reconstructions and advancing our understanding of complex X-ray cosmic phenomena. |
| format | Article |
| id | doaj-art-486e0b7179a446398331fd491a285f1e |
| institution | OA Journals |
| issn | 1538-3881 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astronomical Journal |
| spelling | doaj-art-486e0b7179a446398331fd491a285f1e2025-08-20T02:24:35ZengIOP PublishingThe Astronomical Journal1538-38812025-01-01169526810.3847/1538-3881/adbf7bDeconvolving X-Ray Galaxy Cluster Spectra Using a Recurrent Inference MachineCarter Lee Rhea0https://orcid.org/0000-0003-2001-1076Julie Hlavacek-Larrondo1https://orcid.org/0000-0001-7271-7340Alexandre Adam2https://orcid.org/0000-0001-8806-7936Ralph Kraft3https://orcid.org/0000-0002-0765-0511Ákos Bogdán4https://orcid.org/0000-0003-0573-7733Laurence Perreault-Levasseur5https://orcid.org/0000-0003-3544-3939Marine Prunier6https://orcid.org/0009-0003-0932-2487Département de Physique, Université de Montréal , Succ. Centre-Ville, Montréal, Québec, H3C 3J7, Canada ; carterrhea@astro.umontreal; Centre de recherche en astrophysique du Québec (CRAQ) , Canada Département de Physique, Université de Montréal , Succ. Centre-Ville, Montréal, Québec, H3C 3J7, Canada ; carterrhea@astro.umontrealDépartement de Physique, Université de Montréal , Succ. Centre-Ville, Montréal, Québec, H3C 3J7, Canada ; carterrhea@astro.umontreal; Mila—Quebec Artificial Intelligence Institute , Montreal, Québec, CanadaCenter for Astrophysics ∣ Harvard & Smithsonian , 60 Garden Street, Cambridge, MA 02138, USACenter for Astrophysics ∣ Harvard & Smithsonian , 60 Garden Street, Cambridge, MA 02138, USADépartement de Physique, Université de Montréal , Succ. Centre-Ville, Montréal, Québec, H3C 3J7, Canada ; carterrhea@astro.umontreal; Mila—Quebec Artificial Intelligence Institute , Montreal, Québec, Canada; Center for Computational Astrophysics, Flatiron Institute , NY, USADépartement de Physique, Université de Montréal , Succ. Centre-Ville, Montréal, Québec, H3C 3J7, Canada ; carterrhea@astro.umontreal; Centre de recherche en astrophysique du Québec (CRAQ) , Canada ; Max-Planck-Institut für Astronomie , Königstuhl 17, D-69117 Heidelberg, GermanyRecent advances in machine learning algorithms have unlocked new insights in observational astronomy by allowing astronomers to probe new frontiers. In this article, we present a methodology to disentangle the intrinsic X-ray spectrum of galaxy clusters from the instrumental response function. Employing state-of-the-art modeling software and data mining techniques of the Chandra data archive, we construct a set of 100,000 mock Chandra spectra. We train a Recurrent Inference Machine (RIM) to take in the instrumental response and mock observation and output the intrinsic X-ray spectrum. The RIM can recover the mock intrinsic spectrum below the 1 σ error threshold; moreover, the RIM reconstruction of the mock observations is indistinguishable from the observations themselves. To further test the algorithm, we deconvolve extracted spectra from the central regions of the galaxy group NGC 1550, known to have a rich X-ray spectrum, and the massive galaxy clusters A1795. Despite the RIM reconstructions consistently remaining below the 1 σ noise level, the recovered intrinsic spectra did not align with modeled expectations. This discrepancy is likely attributable to the RIM’s method of implicitly encoding prior information within the neural network. This approach holds promise for unlocking new possibilities in accurate spectral reconstructions and advancing our understanding of complex X-ray cosmic phenomena.https://doi.org/10.3847/1538-3881/adbf7bGalaxy clustersGalactic and extragalactic astronomy |
| spellingShingle | Carter Lee Rhea Julie Hlavacek-Larrondo Alexandre Adam Ralph Kraft Ákos Bogdán Laurence Perreault-Levasseur Marine Prunier Deconvolving X-Ray Galaxy Cluster Spectra Using a Recurrent Inference Machine The Astronomical Journal Galaxy clusters Galactic and extragalactic astronomy |
| title | Deconvolving X-Ray Galaxy Cluster Spectra Using a Recurrent Inference Machine |
| title_full | Deconvolving X-Ray Galaxy Cluster Spectra Using a Recurrent Inference Machine |
| title_fullStr | Deconvolving X-Ray Galaxy Cluster Spectra Using a Recurrent Inference Machine |
| title_full_unstemmed | Deconvolving X-Ray Galaxy Cluster Spectra Using a Recurrent Inference Machine |
| title_short | Deconvolving X-Ray Galaxy Cluster Spectra Using a Recurrent Inference Machine |
| title_sort | deconvolving x ray galaxy cluster spectra using a recurrent inference machine |
| topic | Galaxy clusters Galactic and extragalactic astronomy |
| url | https://doi.org/10.3847/1538-3881/adbf7b |
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