Grants for Visiting Postdoc, Advanced Doctoral Students or Master’s Students to AIMS South Africa 2025 Anomaly Detection Algorithms for the ATLAS Trigger Systems
Dr. Claire David
Proposed project:
Anomaly Detection Algorithms for the ATLAS Trigger Systems
Description:
The ATLAS Experiment is one of the detectors of the Large Hadron Collider (LHC), an underground particle accelerator located at CERN, the European Centre for Nuclear and Particle Physics. A recent trendy approach to find New Physics, i.e. processes departing from the current, incomplete theoretical framework, is to search for proton-proton collisions that are not generic (most collisions at the LHC are known physics and define the 'normal', aka the physics we already know). The project would revolve into developing a compact, fast Variational AutoEncoder (VAE) that would extract most information about anomalous events, such as a possible directionality (from the latent space) or multi-dimensional anomaly score. Relevant metrics will be defined to assess the performance. There are available datasets open to the public. Signal samples can be used as a proxy for an initial benchmarking, yet the ultimate goal is to create a model-independent metric where events with high anomaly scores are the one most likely to contain interesting new physics.
The topic demands a short introduction about the physics context, yet it should be doable by a student without any background in particle physics.