Set-Conditional Set Generation for Particle Physics
Sanmay Ganguly ⋅ Lukas Heinrich ⋅ Nilotpal Kakati ⋅ Nathalie Soybelman
Abstract
The simulation of particle physics data is a fundamental but computationally intensive ingredient for physics analysis at the Large Hadron Collider, where observational set-valued data is generated conditional on a set of incoming particles. To accelerate this task, we present an novel generative model based on graph neural network and slot-attention components, which exceeds the performance of pre-existing baselines.
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