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CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulation
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abstract
Simulating showers of particles in highly-granular detectors is a key frontier in the application of machine learning to particle physics. Achieving high accuracy and speed with generative machine learning models would enable them to augment traditional simulations and alleviate a major computing constraint. This work achieves a major breakthrough in this task by, for the first time, directly generating a point cloud of a few thousand space points with energy depositions in the detector in 3D space without relying on a fixed-grid structure. This is made possible by two key innovations: i) Using recent improvements in generative modeling we apply a diffusion model to generate photon showers as high-cardinality point clouds. ii) These point clouds of up to $6,000$ space points are largely geometry-independent as they are down-sampled from initial even higher-resolution point clouds of up to $40,000$ so-called Geant4 steps. We showcase the performance of this approach using the specific example of simulating photon showers in the planned electromagnetic calorimeter of the International Large Detector (ILD) and achieve overall good modeling of physically relevant distributions.
Forward citations
Cited by 6 Pith papers
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Antineutron reconstruction in electromagnetic calorimeters with mixed-representation learning
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An amplitude surrogate trained on a few thousand exact LHC amplitude points statistically outperforms the training data, with largest amplification in sparsely populated kinematic tails of Z+g and Z+4g production.
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D$e^+e^-$ffusion: Capturing the Beam-Beam Physics of $e^+e^-$ Collisions with Diffusion Models
A diffusion model trained on GuineaPig++ reproduces FCC-ee beam-induced pair-production distributions at particle and detector level, about 10^4 times faster.
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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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HEPTAPOD: Orchestrating High Energy Physics Workflows Towards Autonomous Agency
HEPTAPOD uses LLM agents to drive FeynRules, MadGraph, Pythia, and analysis tools through schema-validated tool calls and run-card templates, demonstrated on a leptoquark signal scan.
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