Active learning BSM parameter spaces
| Autor: | Goodsell, Mark D., Joury, Ari |
|---|---|
| Přispěvatelé: | HEP, INSPIRE |
| Rok vydání: | 2023 |
| Předmět: |
Physics and Astronomy (miscellaneous)
new physics neural network dark matter: mass FOS: Physical sciences minimal supersymmetric standard model [PHYS.HPHE] Physics [physics]/High Energy Physics - Phenomenology [hep-ph] High Energy Physics - Phenomenology High Energy Physics - Phenomenology (hep-ph) dark matter: singlet statistical analysis supersymmetry numerical calculations Engineering (miscellaneous) |
| Zdroj: | European Physical Journal |
| ISSN: | 1434-6052 |
| Popis: | 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. 29 pages, 9 figures, 9 tables |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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