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 : = Search for new phenomena in two-body invariant mass distributions using unsupervised machine learning for anomaly detection at [square root]s=13 TeV with the ATLAS detector

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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 ⁢ collisions at √ =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 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

Alternative title
Search for new phenomena in two-body invariant mass distributions using unsupervised machine learning for anomaly detection at [square root]s=13 TeV with the ATLAS detector
Location
Deutsche Nationalbibliothek Frankfurt am Main
Extent
Online-Ressource
Language
Englisch
Notes
Physical review letters. - 132, 8 (2024) , 081801, ISSN: 1079-7114

Event
Veröffentlichung
(where)
Freiburg
(who)
Universität
(when)
2024
Creator
Contributor
Experimentelle Teilchenphysik

DOI
10.1103/physrevlett.132.081801
URN
urn:nbn:de:bsz:25-freidok-2533344
Rights
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
Last update
25.03.2025, 1:51 PM CET

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Time of origin

  • 2024

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