Aug 20 – 25, 2023
Universität Hamburg
Europe/Berlin timezone

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

Not scheduled
5m
Mensa Blattwerk (Universität Hamburg)

Mensa Blattwerk

Universität Hamburg

Von-Melle-Park 5
Poster Searches for New Physics Poster session

Speaker

Alkaid Cheng

Description

Searches for new resonances in two-body invariant masses are performed using an unsupervised anomaly detection technique in events produced in $pp$ 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 and 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.

Collaboration / Activity ATLAS

Primary authors

Presentation materials