Conformal calibration and look-elsewhere effect in anomaly detection for new-physics searches
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 model that scans many regions inflates the look-elsewhere effect, and the asymptotic significances the field relies on are blind to the background mismodelling that anomaly detectors are especially prone to. We propose a calibration layer, built on conformal prediction, that turns any anomaly score into a defensible significance with distribution-free, finite-sample guarantees. Conformal prediction converts scores into valid local p-values, weighted and Mondrian variants repair the sideband-to-signal-region exchang...
| Authors | Jack Y. Araz, Michael Spannowsky |
|---|---|
| Primary category | hep-ph |
| Published | 11 June 2026 |
| Last updated at source | 11 June 2026 |
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