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Incorporating Physical Priors into Weakly Supervised Anomaly Detection

Chi Lung Cheng1,2,*, Gup Singh2,†, and Benjamin Nachman2,3,4,5,‡

  • *Contact author: ccheng84@wisc.edu
  • †Contact author: gupsingh@berkeley.edu
  • ‡Contact author: bpnachman@lbl.gov

Foundation model framework for all tasks involving jet physics

Wahid Bhimji, Chris Harris, Vinicius Mikuni, and Benjamin Nachman

Phys. Rev. D 113 032020 (2026)

Sensitivity to new physics phenomena in anomaly detection: A study of untunable hyperparameters

Fernando Abreu de Souza, Maura Barros, Nuno F. Castro, Miguel Crispim Romão, Céu Neiva, and Rute Pedro

Phys. Rev. D 113 035003 (2026)

Graph theory inspired anomaly detection at the LHC

Jack Y. Araz, Dimitrios Athanasakos, Mateusz Ploskon, and Felix Ringer

J. High Energ. Phys. 2026 254 (2026)

Unbinned inclusive cross-section measurements with machine-learned systematic uncertainties

Lisa Benato, Cristina Giordano, Claudius Krause, Ang Li, Robert Schöfbeck, Dennis Schwarz, Maryam Shooshtari, and Daohan Wang

Phys. Rev. D 112 052006 (2025)

Single interpolated generative model for anomalies

Ranit Das and David Shih

Phys. Rev. D 112 074040 (2025)

Data-driven high-dimensional statistical inference with generative models

Oz Amram and Manuel Szewc

J. High Energ. Phys. 2025 129 (2025)

Improving the performance of weak supervision searches using data augmentation

Zong-En Chen, Cheng-Wei Chiang, and Feng-Yang Hsieh

J. High Energ. Phys. 2025 169 (2025)

D3MV: Detecting Deficient Data in Intelligent Software Systems via Model Verification

Xinyang Ding, Dongming Xiang, and Wang Lin

Electronics 14 4687 (2025)

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