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arXiv · 2211.06406

Set-Conditional Set Generation for Particle Physics

Francesco Armando Di Bello·Etienne Dreyer·Sanmay Ganguly·Eilam Gross·Lukas Heinrich·Marumi Kado·Nilotpal Kakati·Jonathan Shlomi·Nathalie Soybelman
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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 a novel generative model based on a graph neural network and slot-attention components, which exceeds the performance of pre-existing baselines.

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Francesco Armando Di Bello, Etienne Dreyer, Sanmay Ganguly, Eilam Gross, Lukas Heinrich, Marumi Kado, Nilotpal Kakati, Jonathan Shlomi, Nathalie Soybelman. 2022-11-11. Set-Conditional Set Generation for Particle Physics. https://doi.org/10.1088/2632-2153%2Fad035b

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