A strategy for a general search for new phenomena using data-derived signal regions and its application within the ATLAS experiment

2018/07/31 by ATLAS Collaboration, M. Aaboud, G. Aad +98 · 2 citations
Engineering · Mathematics · Physics and Astronomy · #Algorithm #Astrophysics #Atlas (anatomy) #Benchmark (surveying) #Cartography #Collision #Computer science #Data mining #Engineering #Event (particle physics) #Event data #Geography #High-Energy Particle Collisions Research #Large Hadron Collider #Mathematics #Monte Carlo method #Particle Detector Development and Performance #Particle physics #Particle physics theoretical and experimental studies #Physics #Physics beyond the Standard Model #SIGNAL (programming language) #Sensitivity (control systems) #Standard deviation #Statistical physics #Statistics #hep-ex

paper · pdf · doi:10.1140/epjc/s10052-019-6540-y

published as Eur. Phys. J. C 79 (2019) 120 · 63 pages in total, author list starting page 47, 23 figures, 5 tables, final version published in Eur. Phys. J. C. All figures including auxiliary figures are available at http://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PAPERS/EXOT-2016-38/

openalex created_date 2018/08/03 · openalex publication_date 2019/02/01 · arxiv created 2019/02/18 · arxiv updated 2019/02/19 · openalex updated_date 2026/08/06

Abstract

This paper describes a strategy for a general search used by the ATLAS Collaboration to find potential indications of new physics. Events are classified according to their final state into many event classes. For each event class an automated search algorithm tests whether the data are compatible with the Monte Carlo simulated expectation in several distributions sensitive to the effects of new physics. The significance of a deviation is quantified using pseudoexperiments. A data selection with a significant deviation defines a signal region for a dedicated follow-up analysis with an improved background expectation. The analysis of the data-derived signal regions on a new dataset allows a statistical interpretation without the large look-elsewhere effect. The sensitivity of the approach is discussed using Standard Model processes and benchmark signals of new physics. As an example, results are shown for 3.2 fb -1 of protonproton collision data at a centre-of-mass energy of 13 TeV collected with the ATLAS detector at the LHC in 2015, in which more than 700 event classes and more than 10 5 regions have been analysed. No significant deviations are found and consequently no data-derived signal regions for a follow-up analysis have been defined.

Citations

• Next-to-next-to-leading logarithm resummation fors-channel single top quark production
• Supergauge transformations in four dimensions
• Event generation with SHERPA 1.1
• Is the neutrino a goldstone particle?
• The anti-ktjet clustering algorithm
• HERWIG 6: an event generator for hadron emission reactions with interfering gluons (including supersymmetric processes)
• QCD Matrix Elements + Parton Showers
• Global search for new physics with2.0 fb−1at CDF
• Search for new phenomena in high-mass diphoton final states using 37 fb−1 of proton–proton collisions collected at s=13 TeV with the ATLAS detector
• Search for supersymmetry at √(s)=13 s = 13 TeV in final states with jets and two same-sign leptons or three leptons with the ATLAS detector

Cited by

• Model-Agnostic Signal Discovery with Machine Learning: Bridging the Gap Between Theory and Practice
• On the Statistical Interpretation of Discoveries in LHC Data

Related

• Expected Performance of the ATLAS Experiment - Detector, Trigger and Physics
• Statistical methods used in ATLAS for exclusion and discovery
• Searches for new phenomena at CMS and ATLAS
• Search for new physics in final states with semi-visible jets or anomalous signatures using the ATLAS detector
• Searches for new phenomena in leptonic final states using the ATLAS detector