Just CaloCHILL: fast hadronic shower generation in a learned latent space

CaloCHILL, a new generative model, significantly accelerates the simulation of hadronic calorimeter showers by generating a compact summary in a learned latent space rather than individual energy deposits. This three-stage model, which includes flow-matching and a variational autoencoder, reproduces main shower observables and their time structure. CaloCHILL is 600 times faster than its predecessor while also improving accuracy.