A machine learning method to infer clusters of galaxies mass radial profiles from mock Sunyaev-Zel’dovich maps with The Three Hundred clusters
Our study introduces a new machine learning algorithm for estimating 3D cumulative radial profiles of total and gas mass in galaxy clusters from thermal Sunyaev-Zel’dovich (SZ) effect maps. We generate mock images from 2522 simulated clusters, employing an autoencoder and random forest in our approa...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2024-01-01
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| Series: | EPJ Web of Conferences |
| Online Access: | https://www.epj-conferences.org/articles/epjconf/pdf/2024/03/epjconf_mmUniverse2023_00019.pdf |
| _version_ | 1797235238019006464 |
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| author | Ferragamo A. de Andres D. Sbriglio A. Cui W. De Petris M. Yepes G. Dupuis R. Jarraya M. Lahouli I. De Luca F. Gianfagna G. Rasia E. |
| author_facet | Ferragamo A. de Andres D. Sbriglio A. Cui W. De Petris M. Yepes G. Dupuis R. Jarraya M. Lahouli I. De Luca F. Gianfagna G. Rasia E. |
| author_sort | Ferragamo A. |
| collection | DOAJ |
| description | Our study introduces a new machine learning algorithm for estimating 3D cumulative radial profiles of total and gas mass in galaxy clusters from thermal Sunyaev-Zel’dovich (SZ) effect maps. We generate mock images from 2522 simulated clusters, employing an autoencoder and random forest in our approach. Notably, our model makes no prior assumptions about hydrostatic equilibrium. Our results indicate that the model successfully reconstructs unbiased total and gas mass profiles, with a scatter of approximately 10%. We analyse clusters in various dynamical states and mass ranges, finding that our method’s accuracy and precision are consistent. We verify the capabilities of our model by comparing it with the hydrostatic equilibrium technique, showing that it accurately recovers total mass profiles without any bias. |
| first_indexed | 2024-04-24T16:44:47Z |
| format | Article |
| id | doaj.art-292badd9b1ae450f8af7b1dd18a23d5e |
| institution | Directory Open Access Journal |
| issn | 2100-014X |
| language | English |
| last_indexed | 2024-04-24T16:44:47Z |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | EPJ Web of Conferences |
| spelling | doaj.art-292badd9b1ae450f8af7b1dd18a23d5e2024-03-29T08:31:00ZengEDP SciencesEPJ Web of Conferences2100-014X2024-01-012930001910.1051/epjconf/202429300019epjconf_mmUniverse2023_00019A machine learning method to infer clusters of galaxies mass radial profiles from mock Sunyaev-Zel’dovich maps with The Three Hundred clustersFerragamo A.0de Andres D.1Sbriglio A.2Cui W.3De Petris M.4Yepes G.5Dupuis R.6Jarraya M.7Lahouli I.8De Luca F.9Gianfagna G.10Rasia E.11Instituto de Astrofísica de Canarias (IAC)Departamento de Física Téorica, Módulo 15, Facultad de Ciencias, Universidad Autónoma de MadridDipartimento di Fisica, Sapienza Universitá di RomaDepartamento de Física Téorica, Módulo 15, Facultad de Ciencias, Universidad Autónoma de MadridDipartimento di Fisica, Sapienza Universitá di RomaDepartamento de Física Téorica, Módulo 15, Facultad de Ciencias, Universidad Autónoma de MadridEURANOVAEURANOVAEURANOVADipartimento di Fisica, Università di Roma Tor VergataDipartimento di Fisica, Sapienza Universitá di RomaIFPU - Institute for Fundamental Physics of the UniverseOur study introduces a new machine learning algorithm for estimating 3D cumulative radial profiles of total and gas mass in galaxy clusters from thermal Sunyaev-Zel’dovich (SZ) effect maps. We generate mock images from 2522 simulated clusters, employing an autoencoder and random forest in our approach. Notably, our model makes no prior assumptions about hydrostatic equilibrium. Our results indicate that the model successfully reconstructs unbiased total and gas mass profiles, with a scatter of approximately 10%. We analyse clusters in various dynamical states and mass ranges, finding that our method’s accuracy and precision are consistent. We verify the capabilities of our model by comparing it with the hydrostatic equilibrium technique, showing that it accurately recovers total mass profiles without any bias.https://www.epj-conferences.org/articles/epjconf/pdf/2024/03/epjconf_mmUniverse2023_00019.pdf |
| spellingShingle | Ferragamo A. de Andres D. Sbriglio A. Cui W. De Petris M. Yepes G. Dupuis R. Jarraya M. Lahouli I. De Luca F. Gianfagna G. Rasia E. A machine learning method to infer clusters of galaxies mass radial profiles from mock Sunyaev-Zel’dovich maps with The Three Hundred clusters EPJ Web of Conferences |
| title | A machine learning method to infer clusters of galaxies mass radial profiles from mock Sunyaev-Zel’dovich maps with The Three Hundred clusters |
| title_full | A machine learning method to infer clusters of galaxies mass radial profiles from mock Sunyaev-Zel’dovich maps with The Three Hundred clusters |
| title_fullStr | A machine learning method to infer clusters of galaxies mass radial profiles from mock Sunyaev-Zel’dovich maps with The Three Hundred clusters |
| title_full_unstemmed | A machine learning method to infer clusters of galaxies mass radial profiles from mock Sunyaev-Zel’dovich maps with The Three Hundred clusters |
| title_short | A machine learning method to infer clusters of galaxies mass radial profiles from mock Sunyaev-Zel’dovich maps with The Three Hundred clusters |
| title_sort | machine learning method to infer clusters of galaxies mass radial profiles from mock sunyaev zel dovich maps with the three hundred clusters |
| url | https://www.epj-conferences.org/articles/epjconf/pdf/2024/03/epjconf_mmUniverse2023_00019.pdf |
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