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Found 7 papers, 6 papers with code
Flow Matching Beyond Kinematics: Generating Jets with Particle-ID and Trajectory Displacement Information
We introduce the first generative model trained on the JetClass dataset.
EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusion
In addition, we introduce \epcfm, the first permutation equivariant continuous normalizing flow (CNF) for particle cloud generation.
CaloClouds II: Ultra-Fast Geometry-Independent Highly-Granular Calorimeter Simulation
We further distill the diffusion model into a consistency model allowing for accurate sampling in a single step and resulting in a $46\times$ ($37\times$ over CaloClouds) speed-up.
CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulation
Simulating showers of particles in highly-granular detectors is a key frontier in the application of machine learning to particle physics.
EPiC-GAN: Equivariant Point Cloud Generation for Particle Jets
With the vast data-collecting capabilities of current and future high-energy collider experiments, there is an increasing demand for computationally efficient simulations.
Shared Data and Algorithms for Deep Learning in Fundamental Physics
We introduce a Python package that provides simply and unified access to a collection of datasets from fundamental physics research - including particle physics, astroparticle physics, and hadron- and nuclear physics - for supervised machine learning studies.
Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed
Accurate simulation of physical processes is crucial for the success of modern particle physics.
Instrumentation and Detectors High Energy Physics - Experiment High Energy Physics - Phenomenology Data Analysis, Statistics and Probability
