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Search for New Phenomena in Two-Body Invariant Mass Distributions Using Unsupervised Machine Learning for Anomaly Detection at √s=13  TeV with the ATLAS Detector

ContributorsATLAS Collaboration
Published inPhysical review letters, vol. 132, no. 8
Publication date2024
First online date2024-02-20
Abstract

Searches for new resonances are performed using an unsupervised anomaly-detection technique. Events with at least one electron or muon are selected from 140  fb^{-1} of pp collisions at sqrt[s]=13  TeV recorded by ATLAS at the Large Hadron Collider. The approach involves training an autoencoder on data, and subsequently defining anomalous regions based on the reconstruction loss of the decoder. Studies focus on nine invariant mass spectra that contain pairs of objects consisting of one light jet or b jet and either one lepton (e,μ), photon, or second light jet or b jet in the anomalous regions. No significant deviations from the background hypotheses are observed. Limits on contributions from generic Gaussian signals with various widths of the resonance mass are obtained for nine invariant masses in the anomalous regions.

Keywords
  • P p: scattering
  • P p: colliding beams
  • Resonance: production
  • New physics: search for
  • Mass spectrum
  • ATLAS
  • Muon
  • Photon
  • Electron
  • Machine learning
  • Neural network
  • Final state: ((n)jet lepton)
  • Jet: bottom
  • CERN LHC Coll
  • Background
  • Anomaly
  • Channel cross section: upper limit
  • Data analysis method
  • Experimental results
  • 13000 GeV-cms
  • S015CS
Citation (ISO format)
ATLAS Collaboration. Search for New Phenomena in Two-Body Invariant Mass Distributions Using Unsupervised Machine Learning for Anomaly Detection at √s=13  TeV with the ATLAS Detector. In: Physical review letters, 2024, vol. 132, n° 8. doi: 10.1103/PhysRevLett.132.081801
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Article (Published version)
Identifiers
Journal ISSN0031-9007
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