Accelerating the BSM interpretation of LHC data with machine learning
File(s)
Working paper
Author(s)
Type
Journal Article
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.
Date Issued
2019-03-18
Date Acceptance
2019-03-18
Citation
PHYSICS OF THE DARK UNIVERSE, 2019, 24
ISSN
2212-6864
Publisher
ELSEVIER SCIENCE BV
Journal / Book Title
PHYSICS OF THE DARK UNIVERSE
Volume
24
Copyright Statement
© 2016 The Authors
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000465292500018&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
Science & Technology
Physical Sciences
Astronomy & Astrophysics
DARK-MATTER
SQUARK
MODELS
Notes
5 pages, 2 figures
Publication Status
Published
Article Number
ARTN 100293