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Accelerating the BSM interpretation of LHC data with machine learning

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Title
Accelerating the BSM interpretation of LHC data with machine learning
Author(s)
Gianfranco Bertone; Marc Peter Deisenroth; Jong Soo Kim; Sebastian Liem; Roberto Ruiz de Austri; Max Welling
Publication Date
2019-03
Journal
PHYSICS OF THE DARK UNIVERSE, v.24, no., pp.100293 -
Publisher
ELSEVIER SCIENCE BV
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. © 2019 Elsevier B.V
URI
https://pr.ibs.re.kr/handle/8788114/6018
ISSN
2212-6864
Appears in Collections:
Center for Fundamental Theory(순수물리이론 연구단) > Journal Papers (저널논문)
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