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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
https://lup.lub.lu.se/search/publication/f0f650f1-831f-4456-adac-b898d2580c3b
Aad, G.
Åkesson, T.P.A.
Corrigan, E.E.
Doglioni, C.
Geisen, J.
Hansen, E.
Hedberg, V.
Herde, Hannah
Konya, B.
Lytken, E.
Poettgen, R.
Simpson, N.D.
Smirnova, O.
Zwalinski, L.
2024
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 √s ¼ 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. © 2024 CERN, for the ATLAS Collaboration.
https://lup.lub.lu.se/record/f0f650f1-831f-4456-adac-b898d2580c3b
http://dx.doi.org/10.1103/PhysRevLett.132.081801
scopus:85186742137
eng
Physical Review Letters; 132(8), no 081801 (2024)
ISSN: 0031-9007
Subatomic Physics
Anomaly detection
Machine learning
Tellurium compounds
Anomalous regions
ATLAS detectors
Auto encoders
Invariant mass distribution
Large Hadron Collider
Large-hadron colliders
Region-based
Unsupervised anomaly detection
Unsupervised machine learning
Mass spectrometry
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
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info:eu-repo/semantics/article
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