2nd GSI/FAIR AI Workshop

We investigate fast methods for evaluating particle hypotheses by combining signals from multiple detectors during the early readout stage of the data-acquisition (DAQ) chain. As a first use case, we consider straight particle trajectories reconstructed from straw-tube drift-time measurements and time-of-flight (TOF) wall responses, initially neglecting energy loss. A compact multilayer perceptron implemented in PyTorch is trained on reconstructed spatial and timing observables to produce a muon-compatibility score. Its performance is compared with the classical chi2 selection in terms of muon efficiency, background rejection, and momentum dependence. This study provides a proof of concept for combining detector information with lightweight machine-learning models, with the longer-term goal of deployment in low-latency, real-time data-processing systems.