Mixture-of-Theories training: can we find new physics and anomalies better by mixing physical theories?
Abstract Model-independent search strategies have been increasingly proposed in recent years because on the one hand there has been no clear signal for new physics and on the other hand there is a lack of a highly probable and parameter-free extension of the standard model. For these reasons, there...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-03-01
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| Series: | Journal of High Energy Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/JHEP03(2023)004 |
| _version_ | 1827917164691062784 |
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| author | Sascha Caron Roberto Ruiz de Austri Zhongyi Zhang |
| author_facet | Sascha Caron Roberto Ruiz de Austri Zhongyi Zhang |
| author_sort | Sascha Caron |
| collection | DOAJ |
| description | Abstract Model-independent search strategies have been increasingly proposed in recent years because on the one hand there has been no clear signal for new physics and on the other hand there is a lack of a highly probable and parameter-free extension of the standard model. For these reasons, there is no simple search target so far. In this work, we try to take a new direction and ask the question: bearing in mind that we have a large number of new physics theories that go beyond the Standard Model and may contain a grain of truth, can we improve our search strategy for unknown signals by using them “in combination”? In particular, we show that a signal hypothesis based on a large, intermingled set of many different theoretical signal models can be a superior approach to find an unknown BSM signal. Applied to a recent data challenge, we show that “mixture-of-theories training” outperforms strategies that optimize signal regions with a single BSM model as well as most unsupervised strategies. Applications of this work include anomaly detection and the definition of signal regions in the search for signals of new physics. |
| first_indexed | 2024-03-13T03:24:51Z |
| format | Article |
| id | doaj.art-09b627c2cd454bc9845ec816fecd364f |
| institution | Directory Open Access Journal |
| issn | 1029-8479 |
| language | English |
| last_indexed | 2024-03-13T03:24:51Z |
| publishDate | 2023-03-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of High Energy Physics |
| spelling | doaj.art-09b627c2cd454bc9845ec816fecd364f2023-06-25T11:05:36ZengSpringerOpenJournal of High Energy Physics1029-84792023-03-012023313710.1007/JHEP03(2023)004Mixture-of-Theories training: can we find new physics and anomalies better by mixing physical theories?Sascha Caron0Roberto Ruiz de Austri1Zhongyi Zhang2High Energy Physics, Radboud University NijmegenInstituto de Física Corpuscular, IFIC-UV/CSICHigh Energy Physics, Radboud University NijmegenAbstract Model-independent search strategies have been increasingly proposed in recent years because on the one hand there has been no clear signal for new physics and on the other hand there is a lack of a highly probable and parameter-free extension of the standard model. For these reasons, there is no simple search target so far. In this work, we try to take a new direction and ask the question: bearing in mind that we have a large number of new physics theories that go beyond the Standard Model and may contain a grain of truth, can we improve our search strategy for unknown signals by using them “in combination”? In particular, we show that a signal hypothesis based on a large, intermingled set of many different theoretical signal models can be a superior approach to find an unknown BSM signal. Applied to a recent data challenge, we show that “mixture-of-theories training” outperforms strategies that optimize signal regions with a single BSM model as well as most unsupervised strategies. Applications of this work include anomaly detection and the definition of signal regions in the search for signals of new physics.https://doi.org/10.1007/JHEP03(2023)004Specific BSM PhenomenologySupersymmetry |
| spellingShingle | Sascha Caron Roberto Ruiz de Austri Zhongyi Zhang Mixture-of-Theories training: can we find new physics and anomalies better by mixing physical theories? Journal of High Energy Physics Specific BSM Phenomenology Supersymmetry |
| title | Mixture-of-Theories training: can we find new physics and anomalies better by mixing physical theories? |
| title_full | Mixture-of-Theories training: can we find new physics and anomalies better by mixing physical theories? |
| title_fullStr | Mixture-of-Theories training: can we find new physics and anomalies better by mixing physical theories? |
| title_full_unstemmed | Mixture-of-Theories training: can we find new physics and anomalies better by mixing physical theories? |
| title_short | Mixture-of-Theories training: can we find new physics and anomalies better by mixing physical theories? |
| title_sort | mixture of theories training can we find new physics and anomalies better by mixing physical theories |
| topic | Specific BSM Phenomenology Supersymmetry |
| url | https://doi.org/10.1007/JHEP03(2023)004 |
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