Search for New Phenomena in Two-Body Invariant Mass Distributions Using Unsupervised Machine Learning for Anomaly Detection at with the ATLAS Detector
Collaboration
23 pages
Published in:
- Phys.Rev.Lett. 132 (2024) 8, 081801
- Published: Feb 20, 2024
e-Print:
- 2307.01612 [hep-ex]
DOI:
- 10.1103/PhysRevLett.132.081801 (publication)
Report number:
- CERN-EP-2023-112
Experiments:
Citations per year
Abstract: (APS)
Searches for new resonances are performed using an unsupervised anomaly-detection technique. Events with at least one electron or muon are selected from of collisions at 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 jet and either one lepton , photon, or second light jet or 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.Note:
- All figures including auxiliary figures are available at: http://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PAPERS/EXOT-2022-07
- p p: scattering
- p p: colliding beams
- resonance: production
- new physics: search for
- mass spectrum
- ATLAS
- muon
- photon
- electron
- machine learning
References(61)
Figures(4)
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- [3]
- [4]
- [5]
- [6]
- [7]
- [8]
- [10]
- [10]
- [11]
- [12]
- [13]
- [14]
- [15]
- [16]
- [17]
- [18]
- [19]
- [20]
- [22]
- [23]
- [24]
- [25]
- [26]