Speaker
Description
Accurate knowledge of tracker material is essential for particle reconstruction and detector simulation in high energy physics. Photon conversions and secondary nuclear interactions provide localized probes of detector structures, but inferring a material map from their sparse and noisy vertex distributions is an ill-posed inverse problem. We present a Bayesian field-inference approach in which the spatial material distribution is modeled as a continuous latent field and reconstructed using Information Field Theory. A probabilistic forward model relates this field to the observed conversion and interaction vertices, naturally incorporating spatial correlations and enabling uncertainty quantification. The method is currently being developed and validated using configurable Geant4-based simulations, for which the inferred material maps can be compared directly with simulation truth.