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Picking the low-hanging fruit: testing new physics at scale with active learning
Since the discovery of the Higgs boson, testing the many possible extensions to the Standard Model has become a key challenge in particle physics. This paper discusses a new method for predicting the compatibility of new physics theories with existing experimental data from particle colliders. By us...
| Autores principales: | , , , , |
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| Lenguaje: | eng |
| Publicado: |
2022
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| Materias: | |
| Acceso en línea: | https://dx.doi.org/10.21468/SciPostPhys.13.1.002 http://cds.cern.ch/record/2801666 |
| _version_ | 1780972712782462976 |
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| author | Rocamonde, Juan Corpe, Louie Zilgalvis, Gustavs Avramidou, Maria Butterworth, Jon |
| author_facet | Rocamonde, Juan Corpe, Louie Zilgalvis, Gustavs Avramidou, Maria Butterworth, Jon |
| author_sort | Rocamonde, Juan |
| collection | CERN |
| description | Since the discovery of the Higgs boson, testing the many possible extensions to the Standard Model has become a key challenge in particle physics. This paper discusses a new method for predicting the compatibility of new physics theories with existing experimental data from particle colliders. By using machine learning, the technique obtained comparable results to previous methods (>90% precision and recall) with only a fraction of their computing resources (<10%). This makes it possible to test models that were impossible to probe before, and allows for large-scale testing of new physics theories. |
| id | cern-2801666 |
| institution | Organización Europea para la Investigación Nuclear |
| language | eng |
| publishDate | 2022 |
| record_format | invenio |
| spelling | cern-28016662023-08-10T10:07:21Zdoi:10.21468/SciPostPhys.13.1.002http://cds.cern.ch/record/2801666engRocamonde, JuanCorpe, LouieZilgalvis, GustavsAvramidou, MariaButterworth, JonPicking the low-hanging fruit: testing new physics at scale with active learninghep-phParticle Physics - PhenomenologySince the discovery of the Higgs boson, testing the many possible extensions to the Standard Model has become a key challenge in particle physics. This paper discusses a new method for predicting the compatibility of new physics theories with existing experimental data from particle colliders. By using machine learning, the technique obtained comparable results to previous methods (>90% precision and recall) with only a fraction of their computing resources (<10%). This makes it possible to test models that were impossible to probe before, and allows for large-scale testing of new physics theories.Since the discovery of the Higgs boson, testing the many possible extensions to the Standard Model has become a key challenge in particle physics. This paper discusses a new method for predicting the compatibility of new physics theories with existing experimental data from particle colliders. Using machine learning, the technique obtained comparable results to previous methods (>90% precision and recall) with only a fraction of their computing resources (<10%). This makes it possible to test models that were impossible to probe before, and allows for large-scale testing of new physics theories.arXiv:2202.05882MCnet-21oai:cds.cern.ch:28016662022-02-11 |
| spellingShingle | hep-ph Particle Physics - Phenomenology Rocamonde, Juan Corpe, Louie Zilgalvis, Gustavs Avramidou, Maria Butterworth, Jon Picking the low-hanging fruit: testing new physics at scale with active learning |
| title | Picking the low-hanging fruit: testing new physics at scale with active learning |
| title_full | Picking the low-hanging fruit: testing new physics at scale with active learning |
| title_fullStr | Picking the low-hanging fruit: testing new physics at scale with active learning |
| title_full_unstemmed | Picking the low-hanging fruit: testing new physics at scale with active learning |
| title_short | Picking the low-hanging fruit: testing new physics at scale with active learning |
| title_sort | picking the low-hanging fruit: testing new physics at scale with active learning |
| topic | hep-ph Particle Physics - Phenomenology |
| url | https://dx.doi.org/10.21468/SciPostPhys.13.1.002 http://cds.cern.ch/record/2801666 |
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