Graph transformer and mixture of experts for rare signal detection in calorimeter data

This work investigates graph-based deep learning approaches for the identification of rare and non-standard particle signatures in the ATLAS experiment at CERN focusing on dark photons, new particles predicted by beyond standard model theories, as a representative case. Such signatures arise from displaced decays and produce highly heterogeneous and irregular energy deposition patterns in part of the ATLAS detector such as the calorimeter, that are difficult to model with conventional approaches. Graph neural networks provide a natural framework to model the sparse and relational structure of these data. We evaluate a range of architectures, including message-passing networks, attention-based models, and graph Transformers. Building on this, we introduce a graph Transformer augmented with a mixture of experts (MoE) mechanism, designed to better capture the intrinsic heterogeneity of these signatures through modular representations. The models are benchmarked on public, simulated ATLAS calorimeter data, and their behaviour is analysed using physics-motivated observables. The results show that graph-based approaches, and in particular the MoE-enhanced graph Transformer, improve sensitivity to rare and complex signal topologies, while providing insight into the learned representations, and as such offer useful guidance for future applications in particle physics and related domains.