Picking the low-hanging fruit: testing new physics at scale with active learning

Rocamonde, Juan; Corpe, Louie; Zilgalvis, G.; Avramidou, Maria; Butterworth, Jonathan Mark

doi:10.21468/SciPostPhys.13.1.002

SciPost Physics

Picking the low-hanging fruit: testing new physics at scale with active learning

Juan Rocamonde, Louie Corpe, Gustavs Zilgalvis, Maria Avramidou, Jon Butterworth

SciPost Phys. 13, 002 (2022) · published 15 July 2022

doi: 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. 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.

TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhys.13.1.002
TI  - Picking the low-hanging fruit: testing new physics at scale with active learning
PY  - 2022/07/15
UR  - https://scipost.org/SciPostPhys.13.1.002
JF  - SciPost Physics
JA  - SciPost Phys.
VL  - 13
IS  - 1
SP  - 002
A1  - Rocamonde, Juan
AU  - Corpe, Louie
AU  - Zilgalvis, G.
AU  - Avramidou, Maria
AU  - Butterworth, Jonathan Mark
AB  - 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.
ER  -

@Article{10.21468/SciPostPhys.13.1.002,
	title={{Picking the low-hanging fruit: testing new physics at scale with active learning}},
	author={Juan Rocamonde and Louie Corpe and Gustavs Zilgalvis and Maria Avramidou and Jon Butterworth},
	journal={SciPost Phys.},
	volume={13},
	pages={002},
	year={2022},
	publisher={SciPost},
	doi={10.21468/SciPostPhys.13.1.002},
	url={https://scipost.org/10.21468/SciPostPhys.13.1.002},
}

Cited by 4

Authors / Affiliations: mappings to Contributors and Organizations

See all Organizations.

¹ Juan Rocamonde,
² Louie Corpe,
¹ G. Zilgalvis,
³ Maria Avramidou,
³ Jonathan Mark Butterworth

Funder for the research work leading to this publication

Horizon 2020 (through Organization: European Commission [EC])