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.
CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simula- tion
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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Nyström approximation of MMD enables scalable two-sample testing with permutation p-values and a finite-sample power bound matching the minimax optimal separation rate.
Compares ensemble, Bayesian, and evidential regression approaches for uncertainty quantification in amplitude surrogates and shows they detect localized training data issues.
citing papers explorer
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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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A Scalable Nystrom-Based Kernel Two-Sample Test with Permutations
Nyström approximation of MMD enables scalable two-sample testing with permutation p-values and a finite-sample power bound matching the minimax optimal separation rate.
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Amplitude Uncertainties Everywhere All at Once
Compares ensemble, Bayesian, and evidential regression approaches for uncertainty quantification in amplitude surrogates and shows they detect localized training data issues.
- An IQP Born Machine for Calorimeter Image Generation at 64 Qubits with Compiled-IQP Deployment