The BSM-AI project: SUSY-AI–generalizing LHC limits on supersymmetry with machine learning
Abstract A key research question at the Large Hadron Collider is the test of models of new physics. Testing if a particular parameter set of such a model is excluded by LHC data is a challenge: it requires time consuming generation of scattering events, simulation of the detector response, event rec...
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doaj-03828b46aaed494589350ae35dcdeda12020-11-25T01:41:37ZengSpringerOpenEuropean Physical Journal C: Particles and Fields1434-60441434-60522017-04-0177412510.1140/epjc/s10052-017-4814-9The BSM-AI project: SUSY-AI–generalizing LHC limits on supersymmetry with machine learningSascha Caron0Jong Soo Kim1Krzysztof Rolbiecki2Roberto Ruiz de Austri3Bob Stienen4Institute for Mathematics, Astro- and Particle Physics IMAPP, Radboud UniversiteitInstituto de Física Teórica, UAM/CSICInstituto de Física Teórica, UAM/CSICInstituto de Física Corpuscular, IFIC-UV/CSICInstitute for Mathematics, Astro- and Particle Physics IMAPP, Radboud UniversiteitAbstract A key research question at the Large Hadron Collider is the test of models of new physics. Testing if a particular parameter set of such a model is excluded by LHC data is a challenge: it requires time consuming generation of scattering events, simulation of the detector response, event reconstruction, cross section calculations and analysis code to test against several hundred signal regions defined by the ATLAS and CMS experiments. In the BSM-AI project we approach this challenge with a new idea. A machine learning tool is devised to predict within a fraction of a millisecond if a model is excluded or not directly from the model parameters. A first example is SUSY-AI, trained on the phenomenological supersymmetric standard model (pMSSM). About 300, 000 pMSSM model sets – each tested against 200 signal regions by ATLAS – have been used to train and validate SUSY-AI. The code is currently able to reproduce the ATLAS exclusion regions in 19 dimensions with an accuracy of at least $$93\%$$ 93 % . It has been validated further within the constrained MSSM and the minimal natural supersymmetric model, again showing high accuracy. SUSY-AI and its future BSM derivatives will help to solve the problem of recasting LHC results for any model of new physics. SUSY-AI can be downloaded from http://susyai.hepforge.org/ . An on-line interface to the program for quick testing purposes can be found at http://www.susy-ai.org/ .http://link.springer.com/article/10.1140/epjc/s10052-017-4814-9Dark MatterHiggs BosonLarge Hadron ColliderReceiver Operating Characteristic CurveMonte Carlo |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Sascha Caron Jong Soo Kim Krzysztof Rolbiecki Roberto Ruiz de Austri Bob Stienen |
spellingShingle |
Sascha Caron Jong Soo Kim Krzysztof Rolbiecki Roberto Ruiz de Austri Bob Stienen The BSM-AI project: SUSY-AI–generalizing LHC limits on supersymmetry with machine learning European Physical Journal C: Particles and Fields Dark Matter Higgs Boson Large Hadron Collider Receiver Operating Characteristic Curve Monte Carlo |
author_facet |
Sascha Caron Jong Soo Kim Krzysztof Rolbiecki Roberto Ruiz de Austri Bob Stienen |
author_sort |
Sascha Caron |
title |
The BSM-AI project: SUSY-AI–generalizing LHC limits on supersymmetry with machine learning |
title_short |
The BSM-AI project: SUSY-AI–generalizing LHC limits on supersymmetry with machine learning |
title_full |
The BSM-AI project: SUSY-AI–generalizing LHC limits on supersymmetry with machine learning |
title_fullStr |
The BSM-AI project: SUSY-AI–generalizing LHC limits on supersymmetry with machine learning |
title_full_unstemmed |
The BSM-AI project: SUSY-AI–generalizing LHC limits on supersymmetry with machine learning |
title_sort |
bsm-ai project: susy-ai–generalizing lhc limits on supersymmetry with machine learning |
publisher |
SpringerOpen |
series |
European Physical Journal C: Particles and Fields |
issn |
1434-6044 1434-6052 |
publishDate |
2017-04-01 |
description |
Abstract A key research question at the Large Hadron Collider is the test of models of new physics. Testing if a particular parameter set of such a model is excluded by LHC data is a challenge: it requires time consuming generation of scattering events, simulation of the detector response, event reconstruction, cross section calculations and analysis code to test against several hundred signal regions defined by the ATLAS and CMS experiments. In the BSM-AI project we approach this challenge with a new idea. A machine learning tool is devised to predict within a fraction of a millisecond if a model is excluded or not directly from the model parameters. A first example is SUSY-AI, trained on the phenomenological supersymmetric standard model (pMSSM). About 300, 000 pMSSM model sets – each tested against 200 signal regions by ATLAS – have been used to train and validate SUSY-AI. The code is currently able to reproduce the ATLAS exclusion regions in 19 dimensions with an accuracy of at least $$93\%$$ 93 % . It has been validated further within the constrained MSSM and the minimal natural supersymmetric model, again showing high accuracy. SUSY-AI and its future BSM derivatives will help to solve the problem of recasting LHC results for any model of new physics. SUSY-AI can be downloaded from http://susyai.hepforge.org/ . An on-line interface to the program for quick testing purposes can be found at http://www.susy-ai.org/ . |
topic |
Dark Matter Higgs Boson Large Hadron Collider Receiver Operating Characteristic Curve Monte Carlo |
url |
http://link.springer.com/article/10.1140/epjc/s10052-017-4814-9 |
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