PMID- 28943782
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220219
IS  - 1434-6044 (Print)
IS  - 1434-6052 (Electronic)
IS  - 1434-6044 (Linking)
VI  - 77
IP  - 4
DP  - 2017
TI  - The BSM-AI project: SUSY-AI-generalizing LHC limits on supersymmetry with machine 
      learning.
PG  - 257
LID - 10.1140/epjc/s10052-017-4814-9 [doi]
LID - 257
AB  - 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/.
FAU - Caron, Sascha
AU  - Caron S
AD  - Institute for Mathematics, Astro- and Particle Physics IMAPP, Radboud 
      Universiteit, Nijmegen, The Netherlands. ISNI: 0000000122931605. GRID: 
      grid.5590.9
AD  - Nikhef, Amsterdam, The Netherlands. ISNI: 0000 0004 0646 2193. GRID: 
      grid.420012.5
FAU - Kim, Jong Soo
AU  - Kim JS
AD  - Instituto de Fisica Teorica, UAM/CSIC, Madrid, Spain.
FAU - Rolbiecki, Krzysztof
AU  - Rolbiecki K
AD  - Instituto de Fisica Teorica, UAM/CSIC, Madrid, Spain.
AD  - Faculty of Physics, University of Warsaw, Warsaw, Poland. ISNI: 0000 0004 1937 
      1290. GRID: grid.12847.38
FAU - de Austri, Roberto Ruiz
AU  - de Austri RR
AD  - Instituto de Fisica Corpuscular, IFIC-UV/CSIC, Valencia, Spain.
FAU - Stienen, Bob
AU  - Stienen B
AD  - Institute for Mathematics, Astro- and Particle Physics IMAPP, Radboud 
      Universiteit, Nijmegen, The Netherlands. ISNI: 0000000122931605. GRID: 
      grid.5590.9
LA  - eng
PT  - Journal Article
DEP - 20170424
PL  - France
TA  - Eur Phys J C Part Fields
JT  - The European physical journal. C, Particles and fields
JID - 101622319
PMC - PMC5586744
EDAT- 2017/09/26 06:00
MHDA- 2017/09/26 06:01
PMCR- 2017/04/24
CRDT- 2017/09/26 06:00
PHST- 2016/06/22 00:00 [received]
PHST- 2017/04/07 00:00 [accepted]
PHST- 2017/09/26 06:00 [entrez]
PHST- 2017/09/26 06:00 [pubmed]
PHST- 2017/09/26 06:01 [medline]
PHST- 2017/04/24 00:00 [pmc-release]
AID - 4814 [pii]
AID - 10.1140/epjc/s10052-017-4814-9 [doi]
PST - ppublish
SO  - Eur Phys J C Part Fields. 2017;77(4):257. doi: 10.1140/epjc/s10052-017-4814-9. 
      Epub 2017 Apr 24.