BRICKS creates compositional neural Markov kernels via hybrid transformers and Riemannian Flow Matching on product manifolds to enable zero-shot simulation of radiation-matter interactions across arbitrary material distributions.
CaloChallenge 2022: a community challenge for fast calorimeter simulation
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Parnassus faithfully reproduces the ALEPH detector response at event, jet, and particle levels for clean e+e- to Z to qqbar events.
The paper defines interpretability as model structural transparency and explainability as scientific content mapping, discusses their trade-offs, and frames both as deliberate modeling choices for ML in physics.
A review of initiatives to make LHC Monte Carlo event generations available as open data to minimize redundant simulations and resource use.
A primer by six leading developers maps the full Monte Carlo chain (matrix elements, parton showers, hadronisation, detector simulation, tuning, analysis) and the computing and reproducibility issues that come with it.
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BRICKS: Compositional Neural Markov Kernels for Zero-Shot Radiation-Matter Simulation
BRICKS creates compositional neural Markov kernels via hybrid transformers and Riemannian Flow Matching on product manifolds to enable zero-shot simulation of radiation-matter interactions across arbitrary material distributions.
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An AI-based Detector Simulation and Reconstruction Model for the ALEPH Experiment at LEP
Parnassus faithfully reproduces the ALEPH detector response at event, jet, and particle levels for clean e+e- to Z to qqbar events.
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Interpreting "Interpretability" and Explaining "Explainability" in Machine Learning in Physics
The paper defines interpretability as model structural transparency and explainability as scientific content mapping, discusses their trade-offs, and frames both as deliberate modeling choices for ML in physics.
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Open LHC Monte Carlo Event Generation
A review of initiatives to make LHC Monte Carlo event generations available as open data to minimize redundant simulations and resource use.
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The Monte Carlo Ecosystem in High-Energy Physics: A Primer
A primer by six leading developers maps the full Monte Carlo chain (matrix elements, parton showers, hadronisation, detector simulation, tuning, analysis) and the computing and reproducibility issues that come with it.