Artificial proto-modelling: building precursors of a next standard model from simplified model results

Author(s)
Wolfgang Waltenberger, Andre Lessa, Sabine Kraml
Abstract

We present a novel algorithm to identify potential dispersed signals of new physics in the slew of published LHC results. It employs a random walk algorithm to introduce sets of new particles, dubbed "proto-models", which are tested against simplified-model results from ATLAS and CMS (exploiting the SModelS software framework). A combinatorial algorithm identifies the set of analyses and/or signal regions that maximally violates the SM hypothesis, while remaining compatible with the entirety of LHC constraints in our database. Demonstrating our method by running over the experimental results in the SModelS database, we find as currently best-performing proto-model a top partner, a light-flavor quark partner, and a lightest neutral new particle with masses of the order of 1.2 TeV, 700 GeV and 160 GeV, respectively. The corresponding global p-value for the SM hypothesis is p(global)approximate to 0.19; by construction no look-elsewhere effect applies.

Organisation(s)
Particle Physics
External organisation(s)
Österreichische Akademie der Wissenschaften (ÖAW), Universidade Federal do ABC, University of Grenoble
Journal
Journal of High Energy Physics
Volume
2021
No. of pages
41
ISSN
1029-8479
DOI
https://doi.org/10.1007/JHEP03(2021)207
Publication date
03-2021
Peer reviewed
Yes
Austrian Fields of Science 2012
103012 High energy physics
Keywords
Portal url
https://ucris.univie.ac.at/portal/en/publications/artificial-protomodelling-building-precursors-of-a-next-standard-model-from-simplified-model-results(62387424-e2c7-44ef-ad3b-ae5ec04eb35f).html