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Model-Agnostic Search for New Physics in the Presence of Imperfect Background by Smearing the Data — arXiv:2610.40734

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.40734
License (as archived): CC0-1.0
Archived: 2026-11-03T08:12:45.970Z
SHA-256 of the archived text: a7260ce7b200c954d78a8ae20398e2d445e4529300b09497695c31dcddf90671
Stable URL of this page: https://metraipolis.com/documento/dc_20fc7eeeaacd

How to cite

Model-Agnostic Search for New Physics in the Presence of Imperfect Background by Smearing the Data — arXiv:2610.40734 — arXiv (en), https://arxiv.org/abs/2610.40734 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_20fc7eeeaacd (Metraipolis archive dc_20fc7eeeaacd, sha256 a7260ce7b200c954d78a8ae20398e2d445e4529300b09497695c31dcddf90671)

BibTeX

@misc{dc20fc7eeeaacd,
  title = {Model-Agnostic Search for New Physics in the Presence of Imperfect Background by Smearing the Data — arXiv:2610.40734},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.40734},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_20fc7eeeaacd; sha256 a7260ce7b200c954d78a8ae20398e2d445e4529300b09497695c31dcddf90671},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.40734. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

In the search for new particles in high-energy physics, it is necessary to first define the signal region (SR) enriched in signal events, and then compare events in this region with the expected background distribution. While most existing search methods rely on prior domain knowledge of either the signal or the background, there has been less effort to relax both assumptions simultaneously, as methods that weaken one assumption typically rely on strong knowledge of the other. We propose Model-Agnostic Search with Smearing and Imperfect Background (MASSIB), a method that addresses this challenge by learning the density ratio of events relative to a misspecified background distribution using modern tools from machine learning, and defining the SR as the region with high density ratio. Recognizing that a naive application of this idea introduces significant bias in background estimation when interpolating from the control region to the SR, MASSIB mitigates this issue by introducing a "smeared" density ratio, which can be learned efficiently through data-level manipulation and used to define an SR that allows for accurate background estimation. By applying it to various types of simulated Large Hadron Collider (LHC) events with realistic background mismodeling, we demonstrate that MASSIB identifies a data-driven SR that achieves high signal concentration consistently across different types of signals while avoiding bias in background estimation. The method does this without making strong assumptions about either the signal or background distribution. We then show that this robustness extends to the validity and strong statistical power of the resulting hypothesis tests for signal detection, demonstrating the potential of this approach for future model-agnostic searches at the LHC.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Applications (stat.AP) ; Machine Learning (cs.LG); Data Analysis, Statistics and Probability (physics.data-an).
Pagina del lavoro: https://arxiv.org/abs/2610.40734 — 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 2026-11-03T08:02:38.966Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).