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

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 (𝑚𝑗𝑋) are inspected which contain pairs of one jet (𝑏-jet) and one lepton (𝑒, 𝜇), 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 obtained model-independent limits are shown to have a strong potential to exclude generic heavy states with complex decays.