Model-independent searches and anomaly detection at the CMS experiment
Abstract
The absence of a clear signal of physics beyond the standard model at the CERN LHC motivates search strategies that do not presuppose a specific signal hypothesis. This proceedings present machine-learning-based anomaly detection to search for new physics in a model-agnostic way. The first such search by CMS looks for dijet resonances whose jets have substructure atypical of jets initiated by light quarks or gluons, using five complementary anomaly detection methods applied to 138 fb−1 of proton-proton collision data at √s = 13 TeV. The technique has been validated directly on data by recovering the top quark without the use of labels. The program has also moved into real time: two unsupervised algorithms, AXOL1TL and CICADA, now run on field-programmable gate arrays inside the CMS level-1 trigger and selected more than four billion collision events during 2024 data taking. Finally, the extension of resonant anomaly detection to event-level observables is outlined.
Related
- Full-event anomaly detection for new physics searches at the Electron-Ion Collider
- Towards anomaly detection searches for new physics signatures including Higgs bosons with weakly supervised machine learning
- MADGRAV: a multilevel anomaly-detection pipeline for gravitational-wave searches applied to LIGO data
- Dark sector searches with the CMS experiment
- Searches for Light Dark Matter and Evidence of Coherent Elastic Neutrino-Nucleus Scattering of Solar Neutrinos with the LUX-ZEPLIN (LZ) Experiment
BibTeX
@misc{vami2026modelindependent,
title = {{Model-independent searches and anomaly detection at the CMS experiment}},
author = {Tamas Almos Vami and for the CMS Collaboration},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.31739},
eprint = {2609.31739},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.31739},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Model-independent searches and anomaly detection at the CMS experiment
AU - Tamas Almos Vami
AU - for the CMS Collaboration
PY - 2026
DA - 2026/09/22
PB - arXiv
DO - 10.48550/arxiv.2609.31739
UR - https://arxiv.org/abs/2609.31739
AB - The absence of a clear signal of physics beyond the standard model at the CERN LHC motivates search strategies that do not presuppose a specific signal hypothesis. This proceedings present machine-learning-based anomaly detection to search for new physics in a model-agnostic way. The first such search by CMS looks for dijet resonances whose jets have substructure atypical of jets initiated by light quarks or gluons, using five complementary anomaly detection methods applied to 138 fb^−1 of proton-proton collision data at √s = 13 TeV. The technique has been validated directly on data by recovering the top quark without the use of labels. The program has also moved into real time: two unsupervised algorithms, AXOL1TL and CICADA, now run on field-programmable gate arrays inside the CMS level-1 trigger and selected more than four billion collision events during 2024 data taking. Finally, the extension of resonant anomaly detection to event-level observables is outlined.
ER -