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Localized anomaly detection with a calorimeter normalizing flow — arXiv:2611.00234

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.00234
License (as archived): CC0-1.0
Archived: 2026-11-03T18:39:22.246Z
SHA-256 of the archived text: 438914f2d4381f7af9ada3bc8f4cf50ef94baeac0d1b80e52196adbb84a9213e
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How to cite

Localized anomaly detection with a calorimeter normalizing flow — arXiv:2611.00234 — arXiv (en), https://arxiv.org/abs/2611.00234 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_dcd8850e005e (Metraipolis archive dc_dcd8850e005e, sha256 438914f2d4381f7af9ada3bc8f4cf50ef94baeac0d1b80e52196adbb84a9213e)

BibTeX

@misc{dcdcd8850e005e,
  title = {Localized anomaly detection with a calorimeter normalizing flow — arXiv:2611.00234},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.00234},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_dcd8850e005e; sha256 438914f2d4381f7af9ada3bc8f4cf50ef94baeac0d1b80e52196adbb84a9213e},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.00234. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Normalizing flows trained as fast calorimeter simulators also give the likelihood of each shower, which can serve as an anomaly score. Because this likelihood sums over every voxel, a signal confined to a few voxels can be lost in the fluctuations of the rest of the shower. For example, in a public benchmark dataset in which a long-lived neutral particle decays to two photons, the CaloFlow likelihood used in previous work selects less than 5% of the signal at a background efficiency of $10^{-3}$ when the particle has the largest simulated mass and decays at or just behind the front face of the calorimeter. In this work, we define a regional anomaly score by summing the squared latent coordinates of the same flow within small detector regions around the point where the particle enters the calorimeter, with the threshold set using background showers alone. At the same background efficiency, this regional anomaly score selects more than 95% of the signal. The per-coordinate likelihood terms, summed over the same regions, perform equally well, but the squared latent coordinates have a known distribution for background showers and clearly illustrate when a shower departs from it. Therefore, normalizing flows may be sensitive to new physics, even if the global likelihood is not.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Instrumentation and Detectors (physics.ins-det) ; Machine Learning (cs.LG); High Energy Physics - Experiment (hep-ex); High Energy Physics - Phenomenology (hep-ph); Data Analysis, Statistics and Probability (physics.data-an).
Pagina del lavoro: https://arxiv.org/abs/2611.00234 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T18:12:54.684Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).