ANOMALY DETECTION FOR THE ATLAS TRIGGER SYSTEM

This work investigates the use of unsupervised anomaly detection for the ATLAS trigger system in the High-Luminosity LHC (HL-LHC) environment. To address the challenge of identifying non-standard events without relying on predefined signatures, a sparse convolutional autoencoder is trained on pile-up suppressed calorimeter tower images containing only QCD dijet background. The network learns to reconstruct typical energy deposition patterns, and the reconstruction error is used as an anomaly score. Submanifold sparse convolutions are employed to preserve the sparsity pattern and ensure accurate, localized reconstruction without introducing artificial energy deposits. Evaluation shows that the model can distinguish Higgs pair production events (HH → b¯bb¯ b) from background, with higher reconstruction errors corresponding to anomalous spatial features. The results demonstrate the viability of sparse autoencoders for model-independent event filtering at the trigger level and motivate further analysis into the features driving anomaly detection performance.