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Area of Science:

  • High Energy Physics
  • Particle Physics
  • Quantum Chromodynamics

Background:

  • The study focuses on Upsilon decays (ϒ→μ⁺μ⁻) in proton-proton collisions.
  • Detecting these decays is challenging due to overwhelming anti-isolated backgrounds.

Purpose of the Study:

  • To present the first study of anti-isolated Upsilon decays.
  • To demonstrate the effectiveness of machine learning (ML)-based anomaly detection in particle physics.
  • To establish a benchmark dataset for future ML anomaly detection research.

Main Methods:

  • Utilized a machine learning (ML)-based anomaly detection strategy.
  • Analyzed 13 TeV CMS Open Data from 2016.
  • Employed an ML-based estimate of the multifeature likelihood.

Main Results:

  • Successfully "rediscovered" the Upsilon particle (ϒ) signal.
  • Elevated signal significance from 1.6σ to 6.4σ.
  • Achieved the first-ever detection of anti-isolated Upsilon decays.

Conclusions:

  • ML-based anomaly detection is practical for finding signals in experimental collider data.
  • This detection provides new opportunities for studying heavy-flavor fragmentation in quantum chromodynamics.
  • The developed benchmark dataset will aid future anomaly detection advancements.