Full-event anomaly detection for new physics searches at the Electron-Ion Collider
Abstract
The future Electron-Ion Collider (EIC) will offer new opportunities to search for physics beyond the Standard Model. We study resonant anomaly detection using machine learning and the full particle content of an event. We consider a new neutral gauge boson and a heavy neutral lepton as illustrative signals with different electron-jet topologies. Weakly supervised learning uses this event information to enhance the sensitivity of an invariant-mass search without identifying individual signal events during training. Comparisons with high-level observables show that the electron kinematics account for an important part of the separation, while correlations among the particles provide further sensitivity. We also construct a conditional full-event generator by adapting a model pretrained on LHC jets to produce EIC background events. A scan over signal fractions demonstrates substantial signal enhancement using the learned background reference, providing a proof of concept for this approach at the EIC. Finally, we consider a fully unsupervised graph autoencoder that provides modest signal enhancement but retains sensitivity at signal fractions too small for effective weak supervision. Our results motivate full-event anomaly detection as part of the EIC physics program and identify directions for developing methods suited to its distinct kinematics.
Related
- Towards anomaly detection searches for new physics signatures including Higgs bosons with weakly supervised machine learning
- Machine Learning for Invisible Dark Boson Searches at the Electron-Ion Collider
- Model-independent searches and anomaly detection at the CMS experiment
- MADGRAV: a multilevel anomaly-detection pipeline for gravitational-wave searches applied to LIGO data
- The Intrinsic Dimension of Collider Events and Model-Independent Searches in 100 Dimensions
BibTeX
@misc{athanasakos2026fullevent,
title = {{Full-event anomaly detection for new physics searches at the Electron-Ion Collider}},
author = {Dimitrios Athanasakos and Sebastian Grieninger and Hongkai Liu and Tymothy Mangan and Felix Ringer and Robert Szafron},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.00308},
eprint = {2610.00308},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.00308},
}
RIS
TY - UNPB
TI - Full-event anomaly detection for new physics searches at the Electron-Ion Collider
AU - Dimitrios Athanasakos
AU - Sebastian Grieninger
AU - Hongkai Liu
AU - Tymothy Mangan
AU - Felix Ringer
AU - Robert Szafron
PY - 2026
DA - 2026/10/08
PB - arXiv
DO - 10.48550/arxiv.2610.00308
UR - https://arxiv.org/abs/2610.00308
AB - The future Electron-Ion Collider (EIC) will offer new opportunities to search for physics beyond the Standard Model. We study resonant anomaly detection using machine learning and the full particle content of an event. We consider a new neutral gauge boson and a heavy neutral lepton as illustrative signals with different electron-jet topologies. Weakly supervised learning uses this event information to enhance the sensitivity of an invariant-mass search without identifying individual signal events during training. Comparisons with high-level observables show that the electron kinematics account for an important part of the separation, while correlations among the particles provide further sensitivity. We also construct a conditional full-event generator by adapting a model pretrained on LHC jets to produce EIC background events. A scan over signal fractions demonstrates substantial signal enhancement using the learned background reference, providing a proof of concept for this approach at the EIC. Finally, we consider a fully unsupervised graph autoencoder that provides modest signal enhancement but retains sensitivity at signal fractions too small for effective weak supervision. Our results motivate full-event anomaly detection as part of the EIC physics program and identify directions for developing methods suited to its distinct kinematics.
ER -