Begone, G;
Deisenroth, MP;
Kim, JS;
Liem, S;
de Austri, RR;
Welling, M;
(2019)
Accelerating the BSM interpretation of LHC data with machine learning.
Physics of the Dark Universe
, 24
, Article 100293. 10.1016/j.dark.2019.100293.
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Abstract
The interpretation of Large Hadron Collider (LHC) data in the framework of Beyond the Standard Model (BSM) theories is hampered by the need to run computationally expensive event generators and detector simulators. Performing statistically convergent scans of high-dimensional BSM theories is consequently challenging, and in practice unfeasible for very high-dimensional BSM theories. We present here a new machine learning method that accelerates the interpretation of LHC data, by learning the relationship between BSM theory parameters and data. As a proof-of-concept, we demonstrate that this technique accurately predicts natural SUSY signal events in two signal regions at the High Luminosity LHC, up to four orders of magnitude faster than standard techniques. The new approach makes it possible to rapidly and accurately reconstruct the theory parameters of complex BSM theories, should an excess in the data be discovered at the LHC.
| Type: | Article |
|---|---|
| Title: | Accelerating the BSM interpretation of LHC data with machine learning |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.dark.2019.100293 |
| Publisher version: | https://doi.org/10.1016/j.dark.2019.100293 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10083553 |
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