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Physical Review D

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Mach. Learn.: Sci. Technol. (1)Nuclear Physics B (1)Phys. Rev. D (1)

Articles Citing

Anomaly-preserving contrastive neural embeddings for end-to-end model-independent searches at the LHC

Kyle Metzger ^1,*, Lana Xu², Mia Sodini², Thea K. Årrestad ^1,†, Katya Govorkova ^2,‡, Gaia Grosso ^2,3,4,§, and Philip Harris^2,3,∥

¹ETH Zürich, Zürich, Switzerland
²Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
³NSF AI Institute for Artificial Intelligence and Fundamental Interactions, Cambridge, Massachusetts, USA
⁴School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA

^*Contact author: kyle.metzger@bluewin.ch
^†Contact author: thea.aarrestad@cern.ch
^‡Contact author: ekaterina.govorkova@cern.ch
^§Contact author: gaia.grosso@cern.ch
^∥Contact author: philip.coleman.harris@cern.ch

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)

Comparing generative models with the new physics learning machine

Samuele Grossi, Marco Letizia, and Riccardo Torre

Nuclear Physics B 1024 117349 (2026)

Learning to validate generative models: a goodness-of-fit approach

Pietro Cappelli, Gaia Grosso, Marco Letizia, Humberto Reyes-González, and Marco Zanetti

Mach. Learn.: Sci. Technol. 7 045011 (2026)

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ISSN 2470-0029 (online), 2470-0010 (print).

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