|   | 
Details
   web
Record
Author (up) Caron, S.; Kim, J.S.; Rolbiecki, K.; Ruiz de Austri, R.; Stienen, B.
Title The BSM-AI project: SUSY-AI-generalizing LHC limits on supersymmetry with machine learning Type Journal Article
Year 2017 Publication European Physical Journal C Abbreviated Journal Eur. Phys. J. C
Volume 77 Issue 4 Pages 257 - 25pp
Keywords
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 theATLAS exclusion regions in 19 dimensions with an accuracy of at least 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/.
Address [Caron, Sascha; Stienen, Bob] Radboud Univ Nijmegen, IMAPP, Nijmegen, Netherlands, Email: krolb@fuw.edu.pl
Corporate Author Thesis
Publisher Springer Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1434-6044 ISBN Medium
Area Expedition Conference
Notes WOS:000400079300001 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 3097
Permanent link to this record