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Comparison of Point Cloud and Image-based Models for Calorimeter Fast Simulation
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Score based generative models are a new class of generative models that have been shown to accurately generate high dimensional calorimeter datasets. Recent advances in generative models have used images with 3D voxels to represent and model complex calorimeter showers. Point clouds, however, are likely a more natural representation of calorimeter showers, particularly in calorimeters with high granularity. Point clouds preserve all of the information of the original simulation, more naturally deal with sparse datasets, and can be implemented with more compact models and data files. In this work, two state-of-the-art score based models are trained on the same set of calorimeter simulation and directly compared.
Forward citations
Cited by 4 Pith papers
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Feasibility Study of Measuring $\Lambda^0\to n\pi^{0}$ Using a High-Granularity Zero-Degree Calorimeter at the Future Electron-Ion Collider
A Geant4 and graph-neural-network feasibility study shows the EIC Zero Degree Calorimeter can reconstruct Lambda0 -> n pi0 decays with resolutions that meet the EIC Yellow Report requirements.
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CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters
Presents CaloTrilogy, a unified one-step generative model for high-granularity calorimeter showers that combines velocity field integration, learned priors, and physics losses to match SOTA quality.
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GPT-like transformer model for silicon tracking detector simulation
A decoder-only transformer trained on tokenized Geant4 hit sequences generates silicon tracker hits that reconstruct to near-Geant4-quality tracks for single muons.
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First-Ever Deployment of a SiPM-on-Tile Calorimeter in a Collider: A Parasitic Test with 200 GeV $pp$ Collisions at RHIC
A SiPM-on-tile calorimeter prototype was deployed, calibrated, and operated for the first time in a collider environment, withstanding about 1e10 1-MeV neutron-equivalent fluence at room temperature.
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