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- Publisher Website: 10.1103/PhysRevLett.132.081801
- Scopus: eid_2-s2.0-85186742137
- WOS: WOS:001190886300010
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Article: Search for New Phenomena in Two-Body Invariant Mass Distributions Using Unsupervised Machine Learning for Anomaly Detection at p s=13 TeV ffi with the ATLAS Detector
Title | Search for New Phenomena in Two-Body Invariant Mass Distributions Using Unsupervised Machine Learning for Anomaly Detection at p s=13 TeV ffi with the ATLAS Detector |
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Authors | |
Issue Date | 20-Feb-2024 |
Publisher | American Physical Society |
Citation | Physical Review Letters, 2024, v. 132, n. 8, p. 1-23 How to Cite? |
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 √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. |
Persistent Identifier | http://hdl.handle.net/10722/343956 |
ISSN | 2023 Impact Factor: 8.1 2023 SCImago Journal Rankings: 3.040 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Tu, Yanjun | - |
dc.contributor.author | Huang, Shuhui | - |
dc.contributor.author | Paredes Hernandez, Daniela Katherinne | - |
dc.contributor.author | Pizzimento, Luca | - |
dc.contributor.author | Tam, Kai Chung | - |
dc.contributor.author | ATLAS Collaboration, | - |
dc.date.accessioned | 2024-06-19T05:20:59Z | - |
dc.date.available | 2024-06-19T05:20:59Z | - |
dc.date.issued | 2024-02-20 | - |
dc.identifier.citation | Physical Review Letters, 2024, v. 132, n. 8, p. 1-23 | - |
dc.identifier.issn | 0031-9007 | - |
dc.identifier.uri | http://hdl.handle.net/10722/343956 | - |
dc.description.abstract | <p>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=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.<span> </span></p> | - |
dc.language | eng | - |
dc.publisher | American Physical Society | - |
dc.relation.ispartof | Physical Review Letters | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.title | Search for New Phenomena in Two-Body Invariant Mass Distributions Using Unsupervised Machine Learning for Anomaly Detection at p s=13 TeV ffi with the ATLAS Detector | - |
dc.type | Article | - |
dc.identifier.doi | 10.1103/PhysRevLett.132.081801 | - |
dc.identifier.scopus | eid_2-s2.0-85186742137 | - |
dc.identifier.volume | 132 | - |
dc.identifier.issue | 8 | - |
dc.identifier.spage | 1 | - |
dc.identifier.epage | 23 | - |
dc.identifier.eissn | 1079-7114 | - |
dc.identifier.isi | WOS:001190886300010 | - |
dc.identifier.issnl | 0031-9007 | - |