Orateur
Description
CaloFound uses conditional shower generation to pretrain a reusable transformer representation in an unsupervised manner for calorimeter representation and reconstruction. Using GEANT4 simulations of a CMS-like electromagnetic calorimeter, it masks a ($9\times9$) crystal window around a photon impact, randomly placing its impact crystal within the central ($3\times3$) region. The encoder processes the visible detector, while a stochastic decoder generates the missing deposits conditioned on the incident energies and positions of all photons entering the window. Particle conditions enter only the decoder, allowing encoder reuse without generator-level information.
Distributional objectives constrain crystal energies, spatial energy profiles and hit occupancy across isolated, overlapping and spatially separated showers. The generated shower profiles agree with the reference simulation to within 2%.
We will present generation performance and transfer of the frozen and fine-tuned encoder to photon energy, position and seed-probability prediction. Comparisons with the state-of-the-art will assess energy and position resolution and the reconstruction of overlapping photons.