{"ID":3053216,"CreatedAt":"2026-06-04T04:41:36.695875263Z","UpdatedAt":"2026-06-05T19:19:17.853951865Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.04165","arxiv_id":"2606.04165","title":"CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters","abstract":"High-precision calorimeter simulation at current and future colliders imposes rapidly growing computational demands, motivating the development of machine-learning surrogates for traditional Monte Carlo tools such as Geant4. Flow matching and diffusion-based generative models have become leading approaches for high-dimensional fast simulation because of their sample quality, but typically require ${\\cal O}(100)$ function evaluations at inference and often rely on auxiliary networks to constrain global observables, compromising streamlined end-to-end generation. We introduce a unified framework that improves the balance between speed, shower quality, and physics fidelity. The method combines: (i) an average velocity field integrator that enables sampling in one or a few evaluations; (ii) a learned generative prior in shower space, constructed from data rather than random noise; and (iii) physics-guided loss terms that impose inductive biases on key observables during training. These elements are training time regularizers, preserving end-to-end inference with no additional cost. With only one or a few evaluation steps, the model achieves shower quality competitive with state-of-the-art flow and diffusion approaches, tested on several public high granularity calorimeter datasets. The results demonstrate inter-layer shower structure consistent with the underlying physics, providing a strong candidate for future fast simulation workflows.","short_abstract":"High-precision calorimeter simulation at current and future colliders imposes rapidly growing computational demands, motivating the development of machine-learning surrogates for traditional Monte Carlo tools such as Geant4. Flow matching and diffusion-based generative models have become leading approaches for high-dim...","url_abs":"https://arxiv.org/abs/2606.04165","url_pdf":"https://arxiv.org/pdf/2606.04165v1","authors":"[\"Cheng Jiang\",\"Sitian Qian\",\"Kevin Pedro\",\"Oz Amram\",\"Huilin Qu\",\"Maggie Voetberg\"]","published":"2026-06-02T19:27:19Z","proceeding":"hep-ex","tasks":"[\"hep-ex\",\"cs.LG\",\"hep-ph\",\"physics.ins-det\"]","methods":"[\"Diffusion Model\"]","has_code":false}