Speaker
Description
Electromagnetic calorimeters provide essential information for reconstructing and selecting both Standard Model (SM) and potential beyond the SM physics events at high-energy particle colliders. The fine-grained segmentation of modern calorimeters captures rich information about the internal structure of particle showers, much of which is discarded by conventional high-level reconstruction methods. In this talk, we show how machine learning applied directly to calorimeter cells in an ATLAS-like calorimeter can recover this information for a challenging benchmark task: distinguishing highly collimated diphoton signatures from light axion-like particle decays against isolated single-photon showers. We present a systematic comparison of six machine-learning architectures, from shower-shape-based approaches to direct cell-level methods. Cell-level learning delivers substantially better classification, with a Transformer achieving the best overall performance and an MLP Mixer offering a lightweight alternative suited to real-time, trigger-level use. We also show that the Transformer enables invariant mass regression directly from cells, sharpening the characterization of light resonances and providing a new handle against $\pi^0$ and $\eta$ fake photon backgrounds. Together, these results point to cell-level machine learning as a way to push calorimeter-based particle identification well beyond current techniques.