PoS - Proceedings of Science
Volume 449 - The European Physical Society Conference on High Energy Physics (EPS-HEP2023) - T10 Searches for New Physics
Search for new physics using unsupervised machine learning for anomaly detection in $\sqrt{s}$ = 13 TeV $pp$ collisions recorded by the ATLAS detector at the LHC
A. Cheng*  on behalf of the ATLAS Collaboration
Full text: pdf
Pre-published on: January 03, 2024
Published on: March 21, 2024
Searches for new resonances in two-body invariant masses are performed using an unsupervised anomaly detection technique in events produced in collisions at a center-of-mass energy of 13 TeV recorded by the ATLAS detector at the LHC. An autoencoder network is trained with 1% randomly selected collision events. Anomalous regions are then defined which contain events with high reconstruction losses. Studies are conducted in data containing at least one isolated lepton. Nine invariant masses ($m_{jX}$) are inspected which contain pairs of one jet ($b$-jet) and one lepton ($e$,$\mu$ ), photon, or a second jet ($b$-jet). No significant deviation from the background-only hypothesis is observed after applying the event-based anomaly detection technique.
The 95% confidence level upper limits on contributions from generic Gaussian signals are reported for the studied invariant masses. The widths of the signals range between 0% and 15% of the resonance mass, and masses range from 0.3 TeV to 7 TeV. The obtained model-independent limits are shown to have a strong potential to exclude generic heavy states with complex decays.
DOI: https://doi.org/10.22323/1.449.0481
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