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Weakly supervised anomaly detection in events with a Higgs boson and exotic physics

Chi Lung Cheng1,2,*, Sarah Demers3,†, Sascha Diefenbacher1,‡, Runze Li3,§, Benjamin Nachman4,5,∥, and Dennis Noll1,5,¶

  • *Contact author: ccheng84@wisc.edu
  • †Contact author: sarah.demers@yale.edu
  • ‡Contact author: sdiefenbacher@lbl.gov
  • §Contact author: runze.li@yale.edu
  • ∥Contact author: nachman@stanford.edu
  • Contact author: nollde@stanford.edu
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Phys. Rev. D 114, 012003 – Published 6 July, 2026

DOI: https://doi.org/10.1103/7582-jt9n

Abstract

We present a machine-learning-based anomaly detection strategy designed to identify anomalous physics in events containing resonant Standard Model physics and demonstrate this method on the final state of a Higgs boson decaying to two photons. The demonstration targets high-dimensional deviations in the region of phase space containing the Higgs mass peak in a fully signal-agnostic manner. A latent-space embedding, learned from event kinematics, enables the use of a large set of potentially sensitive features. Backgrounds are estimated using a hybrid approach that combines machine-learning-based generative modeling with traditional simulation, and a discriminator is trained in the latent space to distinguish data from background estimates. After applying a selection on the classifier output, the invariant mass distribution of the diphoton system is examined for localized excesses above the simulated Higgs peak. We benchmark the sensitivity of this strategy using simplified simulated proton-proton collisions corresponding to data recorded during Run 2 of the LHC and show that the method can provide significant improvements in sensitivity, even for small signal injections that could remain undetected in an inclusive analysis. These results demonstrate that the proposed strategy is a promising and viable approach for future searches and should be applied to recorded collider data.

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Phys. Rev. D 114, 012003– Published 6 July, 2026
  • Received 6 November 2025
  • Accepted 20 May 2026
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