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

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

Weitere Titel
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
Standort
Deutsche Nationalbibliothek Frankfurt am Main
Umfang
Online-Ressource
Sprache
Englisch
Anmerkungen
Physical review letters. - 132, 8 (2024) , 081801, ISSN: 1079-7114

Ereignis
Veröffentlichung
(wo)
Freiburg
(wer)
Universität
(wann)
2024
Urheber
Beteiligte Personen und Organisationen
Experimentelle Teilchenphysik

DOI
10.1103/physrevlett.132.081801
URN
urn:nbn:de:bsz:25-freidok-2533344
Rechteinformation
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
Letzte Aktualisierung
25.03.2025, 13:51 MEZ

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Beteiligte

Entstanden

  • 2024

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