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Set-conditional set generation for particle physics
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DOI：10.1088/2632-2153/ad035b.png)
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Abstract
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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 a novel generative model based on a graph neural network and slot-attention components, which exceeds the performance of pre-existing baselines.
Keywords:
fast simulation
transformer
graph networks
slot-attention
conditional generation
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