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hep-ph2026
Conformal calibration and look-elsewhere effect in anomaly detection for new-physics searches
Jack Y. Araz, Michael Spannowsky
Machine-learned anomaly detection is reshaping searches for new physics, but it has outrun the statistics used to interpret it. A raw anomaly score has no calibrated meaning, a mod…
hep-ph2025
Reinterpretation and preservation of data and analyses in HEP
Jon Butterworth, Sabine Kraml, Harrison Prosper +145
Data from particle physics experiments are unique and are often the result of a very large investment of resources. Given the potential scientific impact of these data, which goes…
hep-ph2025
Communicating Likelihoods with Normalising Flows
Jack Y. Araz, Anja Beck, Méril Reboud +2
We present a machine-learning-based workflow to model an unbinned likelihood from its samples. A key advancement over existing approaches is the validation of the learned likelihoo…