Speaker
Description
The ATLAS experiment at the CERN Large Hadron Collider (LHC) records and processes vast amounts of data from proton-proton collisions. With the High-Luminosity LHC (HL-LHC), the expected increase in data volume by more than an order of magnitude will place unprecedented demands on storage, data throughput, and analysis. In this contribution, we will start with the comparison of two novel ML-based compression methods (using Transformers and Mamba networks) in terms of throughput and compression rate.
We then move on to investigate the interplay between data compression and anomaly detection. Traditional lossless compression algorithms exploit statistical redundancies in the data and become less efficient when encountering previously unseen patterns. This observation motivates the hypothesis that the inability to compress an event efficiently may indicate that it deviates from the distribution on which the compressor was optimized.
We investigate whether ML-based compression can be used as an anomaly detection technique for high-energy physics. We employ the Byte-Oriented Autoregressive (BOA) compression model using Mamba networks, which learns the probability distribution of Standard Model background events and assigns likelihood-based compression scores to unseen data. Events that compress poorly are interpreted as candidates for anomalous behaviour. The approach is evaluated using LHC open data.