Speaker
Description
An unsupervised machine learning framework is developed for searching for anomalous events in proton-proton collisions at √s=13 TeV using ATLAS Open Data. The analysis utilizes an object-based Masked Transformer autoencoder trained exclusively on Standard Model Run 2 data to learn complex correlations among reconstructed physics objects and identify events that deviate from expected Standard Model behaviour without relying on signal labels or predefined signal hypotheses. The resulting anomaly scores provide clear separation between Standard Model background and MC generated Beyond-the-Standard-Model benchmark processes.
To further investigate the most anomalous events in the RUN 2 data, a multi-stage post-processing pipeline is employed to identify anomalous event clusters in the filtered anomalous event space, evaluate their statistical significance, and perform detailed physics characterization. This enables the separation of genuine anomalous event structures from statistical fluctuations and provides physically interpretable anomaly candidates. The proposed framework combines deep learning with unsupervised statistical analysis to provide a transparent, extensible, and data-driven workflow for anomaly discovery, offering a complementary approach to conventional targeted searches for new physics at the LHC.