Active learning BSM parameter spaces
Abstract Active learning (AL) has interesting features for parameter scans of new models. We show on a variety of models that AL scans bring large efficiency gains to the traditionally tedious work of finding boundaries for BSM models. In the MSSM, this approach produces more accurate bounds. In lig...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2023-04-01
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| Series: | European Physical Journal C: Particles and Fields |
| Online Access: | https://doi.org/10.1140/epjc/s10052-023-11368-3 |
| _version_ | 1797822646320103424 |
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| author | Mark D. Goodsell Ari Joury |
| author_facet | Mark D. Goodsell Ari Joury |
| author_sort | Mark D. Goodsell |
| collection | DOAJ |
| description | Abstract Active learning (AL) has interesting features for parameter scans of new models. We show on a variety of models that AL scans bring large efficiency gains to the traditionally tedious work of finding boundaries for BSM models. In the MSSM, this approach produces more accurate bounds. In light of our prior publication, we further refine the exploration of the parameter space of the SMSQQ model, and update the maximum mass of a dark matter singlet to 48.4 TeV. Finally we show that this technique is especially useful in more complex models like the MDGSSM. |
| first_indexed | 2024-03-13T10:12:11Z |
| format | Article |
| id | doaj.art-03b1d58deca5444eac15af43621072eb |
| institution | Directory Open Access Journal |
| issn | 1434-6052 |
| language | English |
| last_indexed | 2024-03-13T10:12:11Z |
| publishDate | 2023-04-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | European Physical Journal C: Particles and Fields |
| spelling | doaj.art-03b1d58deca5444eac15af43621072eb2023-05-21T11:24:50ZengSpringerOpenEuropean Physical Journal C: Particles and Fields1434-60522023-04-0183412210.1140/epjc/s10052-023-11368-3Active learning BSM parameter spacesMark D. Goodsell0Ari Joury1Laboratoire de Physique Théorique et Hautes Energies (LPTHE), UMR 7589, Sorbonne Université et CNRSLaboratoire de Physique Théorique et Hautes Energies (LPTHE), UMR 7589, Sorbonne Université et CNRSAbstract Active learning (AL) has interesting features for parameter scans of new models. We show on a variety of models that AL scans bring large efficiency gains to the traditionally tedious work of finding boundaries for BSM models. In the MSSM, this approach produces more accurate bounds. In light of our prior publication, we further refine the exploration of the parameter space of the SMSQQ model, and update the maximum mass of a dark matter singlet to 48.4 TeV. Finally we show that this technique is especially useful in more complex models like the MDGSSM.https://doi.org/10.1140/epjc/s10052-023-11368-3 |
| spellingShingle | Mark D. Goodsell Ari Joury Active learning BSM parameter spaces European Physical Journal C: Particles and Fields |
| title | Active learning BSM parameter spaces |
| title_full | Active learning BSM parameter spaces |
| title_fullStr | Active learning BSM parameter spaces |
| title_full_unstemmed | Active learning BSM parameter spaces |
| title_short | Active learning BSM parameter spaces |
| title_sort | active learning bsm parameter spaces |
| url | https://doi.org/10.1140/epjc/s10052-023-11368-3 |
| work_keys_str_mv | AT markdgoodsell activelearningbsmparameterspaces AT arijoury activelearningbsmparameterspaces |