Rocamonde, Juan;
Corpe, Louie;
Zilgalvis, G;
Avramidou, Maria;
Butterworth, Jonathan;
(2022)
Picking the low-hanging fruit: testing new physics at scale with active learning.
SciPost Physics
, 13
(1)
, Article 002. 10.21468/scipostphys.13.1.002.
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | Picking the low-hanging fruit: testing new physics at scale with active learning |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.21468/scipostphys.13.1.002 |
| Publisher version: | http://dx.doi.org/10.21468/SciPostPhys.13.1.002 |
| Language: | English |
| Additional information: | © Published by the SciPost Foundation. J. Rocamonde et al. This work is licensed under the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10153281 |
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