Exploiting Machine Learning and Disequilibrium in Galaxy Clusters to Obtain a Mass Profile
We use 3D k -means clustering to characterize galaxy substructure in the A2146 cluster of galaxies ( z = 0.2343). This method objectively characterizes the cluster’s substructure using projected position and velocity data for 67 galaxies within a 2.305 Mpc circular region centered on the cluster...
| Asıl Yazarlar: | , |
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| Materyal Türü: | Makale |
| Dil: | English |
| Baskı/Yayın Bilgisi: |
IOP Publishing
2024-01-01
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| Seri Bilgileri: | The Astrophysical Journal Letters |
| Konular: | |
| Online Erişim: | https://doi.org/10.3847/2041-8213/ad1ede |
| _version_ | 1827370094504706048 |
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| author | Mark J. Henriksen Prajwal Panda |
| author_facet | Mark J. Henriksen Prajwal Panda |
| author_sort | Mark J. Henriksen |
| collection | DOAJ |
| description | We use 3D k -means clustering to characterize galaxy substructure in the A2146 cluster of galaxies ( z = 0.2343). This method objectively characterizes the cluster’s substructure using projected position and velocity data for 67 galaxies within a 2.305 Mpc circular region centered on the cluster's optical center. The optimal number of substructures is found to be four. Four distinct substructures with rms velocity typical of galaxy groups or low-mass subclusters, when compared to cosmological simulations of galaxy cluster formation, suggest that A2146 is in the early stages of formation. We utilize this disequilibrium, which is so prevalent in galaxy clusters at all redshifts, to construct a radial mass distribution. Substructures are bound but not virialized. This method is in contrast to previous kinematical analyses, which have assumed virialization, and ignored the ubiquitous clumping of galaxies. The best-fitting radial mass profile is much less centrally concentrated than the well-known Navarro–Frenk–White profile, indicating that the dark-matter-dominated mass distribution is flatter pre-equilibrium, becoming more centrally peaked in equilibrium through the merging of the substructure. |
| first_indexed | 2024-03-08T10:02:22Z |
| format | Article |
| id | doaj.art-fbcb5e0849e2482e8da1c03fa24f1b51 |
| institution | Directory Open Access Journal |
| issn | 2041-8205 |
| language | English |
| last_indexed | 2024-03-08T10:02:22Z |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal Letters |
| spelling | doaj.art-fbcb5e0849e2482e8da1c03fa24f1b512024-01-29T10:41:51ZengIOP PublishingThe Astrophysical Journal Letters2041-82052024-01-019612L3610.3847/2041-8213/ad1edeExploiting Machine Learning and Disequilibrium in Galaxy Clusters to Obtain a Mass ProfileMark J. Henriksen0https://orcid.org/0000-0003-0530-8736Prajwal Panda1University of Maryland , Baltimore County Physics Department, 1000 Hilltop Circle, Baltimore, MD 21250, USAUniversity of Maryland , Baltimore County Physics Department, 1000 Hilltop Circle, Baltimore, MD 21250, USAWe use 3D k -means clustering to characterize galaxy substructure in the A2146 cluster of galaxies ( z = 0.2343). This method objectively characterizes the cluster’s substructure using projected position and velocity data for 67 galaxies within a 2.305 Mpc circular region centered on the cluster's optical center. The optimal number of substructures is found to be four. Four distinct substructures with rms velocity typical of galaxy groups or low-mass subclusters, when compared to cosmological simulations of galaxy cluster formation, suggest that A2146 is in the early stages of formation. We utilize this disequilibrium, which is so prevalent in galaxy clusters at all redshifts, to construct a radial mass distribution. Substructures are bound but not virialized. This method is in contrast to previous kinematical analyses, which have assumed virialization, and ignored the ubiquitous clumping of galaxies. The best-fitting radial mass profile is much less centrally concentrated than the well-known Navarro–Frenk–White profile, indicating that the dark-matter-dominated mass distribution is flatter pre-equilibrium, becoming more centrally peaked in equilibrium through the merging of the substructure.https://doi.org/10.3847/2041-8213/ad1edeGalaxy clustersAstroinformaticsLarge-scale structure of the universeDark matter distribution |
| spellingShingle | Mark J. Henriksen Prajwal Panda Exploiting Machine Learning and Disequilibrium in Galaxy Clusters to Obtain a Mass Profile The Astrophysical Journal Letters Galaxy clusters Astroinformatics Large-scale structure of the universe Dark matter distribution |
| title | Exploiting Machine Learning and Disequilibrium in Galaxy Clusters to Obtain a Mass Profile |
| title_full | Exploiting Machine Learning and Disequilibrium in Galaxy Clusters to Obtain a Mass Profile |
| title_fullStr | Exploiting Machine Learning and Disequilibrium in Galaxy Clusters to Obtain a Mass Profile |
| title_full_unstemmed | Exploiting Machine Learning and Disequilibrium in Galaxy Clusters to Obtain a Mass Profile |
| title_short | Exploiting Machine Learning and Disequilibrium in Galaxy Clusters to Obtain a Mass Profile |
| title_sort | exploiting machine learning and disequilibrium in galaxy clusters to obtain a mass profile |
| topic | Galaxy clusters Astroinformatics Large-scale structure of the universe Dark matter distribution |
| url | https://doi.org/10.3847/2041-8213/ad1ede |
| work_keys_str_mv | AT markjhenriksen exploitingmachinelearninganddisequilibriumingalaxyclusterstoobtainamassprofile AT prajwalpanda exploitingmachinelearninganddisequilibriumingalaxyclusterstoobtainamassprofile |