Speaker
Description
At ATLAS and CMS, the rate of proton collisions far exceeds the rate at which data can be recorded. A real-time event selection process, or trigger, is needed to ensure that the data recorded contains the highest possible discovery potential. In the absence of hoped-for anomalies such as SUSY, there is increasing motivation to develop dedicated anomaly detection triggers. A common approach is to use unsupervised machine learning to predict an event-by-event anomaly score based on the 4-momenta and multiplicity of reconstructed objects. We show that such anomaly scores often exhibit high mutual information with existing trigger variables, duplicating the acceptance of current triggers rather than accessing underexplored regions of phase space. We introduce DECorrelated Anomaly DEtection (DECADE), in which the resulting anomaly score is decorrelated from existing trigger variables. By minimising the mutual information between the anomaly score and the primary triggers, DECADE prioritises acceptance in regions of phase space not captured by existing trigger strategies. We benchmark two approaches to decorrelation, each suited to deployment in hardware (FPGA) and software (CPU/GPU), and compare physics performance.
