Cargando…

The BSM-AI project: SUSY-AI–generalizing LHC limits on supersymmetry with machine learning

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 reconstructi...

Descripción completa

Detalles Bibliográficos
Autores principales: Caron, Sascha, Kim, Jong Soo, Rolbiecki, Krzysztof, de Austri, Roberto Ruiz, Stienen, Bob
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5586744/
https://www.ncbi.nlm.nih.gov/pubmed/28943782
http://dx.doi.org/10.1140/epjc/s10052-017-4814-9
_version_ 1783261865569157120
author Caron, Sascha
Kim, Jong Soo
Rolbiecki, Krzysztof
de Austri, Roberto Ruiz
Stienen, Bob
author_facet Caron, Sascha
Kim, Jong Soo
Rolbiecki, Krzysztof
de Austri, Roberto Ruiz
Stienen, Bob
author_sort Caron, Sascha
collection PubMed
description 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 [Formula: see text] . 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/.
format Online
Article
Text
id pubmed-5586744
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher Springer Berlin Heidelberg
record_format MEDLINE/PubMed
spelling pubmed-55867442017-09-22 The BSM-AI project: SUSY-AI–generalizing LHC limits on supersymmetry with machine learning Caron, Sascha Kim, Jong Soo Rolbiecki, Krzysztof de Austri, Roberto Ruiz Stienen, Bob Eur Phys J C Part Fields Regular Article - Theoretical Physics 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 [Formula: see text] . 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/. Springer Berlin Heidelberg 2017-04-24 2017 /pmc/articles/PMC5586744/ /pubmed/28943782 http://dx.doi.org/10.1140/epjc/s10052-017-4814-9 Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Funded by SCOAP3
spellingShingle Regular Article - Theoretical Physics
Caron, Sascha
Kim, Jong Soo
Rolbiecki, Krzysztof
de Austri, Roberto Ruiz
Stienen, Bob
The BSM-AI project: SUSY-AI–generalizing LHC limits on supersymmetry with machine learning
title 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_short 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
topic Regular Article - Theoretical Physics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5586744/
https://www.ncbi.nlm.nih.gov/pubmed/28943782
http://dx.doi.org/10.1140/epjc/s10052-017-4814-9
work_keys_str_mv AT caronsascha thebsmaiprojectsusyaigeneralizinglhclimitsonsupersymmetrywithmachinelearning
AT kimjongsoo thebsmaiprojectsusyaigeneralizinglhclimitsonsupersymmetrywithmachinelearning
AT rolbieckikrzysztof thebsmaiprojectsusyaigeneralizinglhclimitsonsupersymmetrywithmachinelearning
AT deaustrirobertoruiz thebsmaiprojectsusyaigeneralizinglhclimitsonsupersymmetrywithmachinelearning
AT stienenbob thebsmaiprojectsusyaigeneralizinglhclimitsonsupersymmetrywithmachinelearning
AT caronsascha bsmaiprojectsusyaigeneralizinglhclimitsonsupersymmetrywithmachinelearning
AT kimjongsoo bsmaiprojectsusyaigeneralizinglhclimitsonsupersymmetrywithmachinelearning
AT rolbieckikrzysztof bsmaiprojectsusyaigeneralizinglhclimitsonsupersymmetrywithmachinelearning
AT deaustrirobertoruiz bsmaiprojectsusyaigeneralizinglhclimitsonsupersymmetrywithmachinelearning
AT stienenbob bsmaiprojectsusyaigeneralizinglhclimitsonsupersymmetrywithmachinelearning