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Isolating Unisolated Upsilons with Anomaly Detection in CMS Open Data

Rikab Gambhir^1,2,*, Radha Mastandrea ^3,4,†, Benjamin Nachman ^4,5,6,7,‡, and Jesse Thaler ^1,2,§

¹Center for Theoretical Physics -- a Leinweber Institute, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
²The NSF AI Institute for Artificial Intelligence and Fundamental Interactions, USA
³Department of Physics, University of California, Berkeley, California 94720, USA
⁴Physics Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
⁵Berkeley Institute for Data Science, University of California, Berkeley, California 94720, USA
⁶Department of Particle Physics and Astrophysics, Stanford University, Stanford, California 94305, USA
⁷Fundamental Physics Directorate, SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA

^*Contact author: rikab@mit.edu
^†Contact author: rmastand@berkeley.edu
^‡Contact author: nachman@stanford.edu
^§Contact author: jthaler@mit.edu

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Articles Citing

Isolating Unisolated Upsilons with Anomaly Detection in CMS Open Data

Foundation model framework for all tasks involving jet physics

Graph theory inspired anomaly detection at the LHC

Weakly supervised anomaly detection with event-level variables

Frequentist uncertainties on neural density ratios with wi fi ensembles

Pseudoscalar heavy-quarkonium hadroproduction from nonrelativistic fragmentation at NLL/NLO+

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