A one-step generative model for calorimeter showers, using MeanFlow, a learned Gaussian-mixture prior, and a physics-constrained loss, matches diffusion-model quality at far fewer evaluations.
arXiv preprint arXiv:2508.11594 , year=
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Flow Mismatching detects anomalies via aggregated velocity mismatches along noise-to-image paths in a flow matching model trained only on normal data, yielding pixel heatmaps without reconstruction or test-time optimization.
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CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters
A one-step generative model for calorimeter showers, using MeanFlow, a learned Gaussian-mixture prior, and a physics-constrained loss, matches diffusion-model quality at far fewer evaluations.
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Flow Mismatching: Unsupervised Anomaly Detection via Velocity Discrepancies in Flow Matching Models
Flow Mismatching detects anomalies via aggregated velocity mismatches along noise-to-image paths in a flow matching model trained only on normal data, yielding pixel heatmaps without reconstruction or test-time optimization.