Set-Conditional Set Generation for Particle Physics

Set-Conditional Set Generation for Particle Physics

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 mod...

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Veröffentlicht in:arXiv.org 2023-11
Hauptverfasser: Di Bello, Francesco Armando, Dreyer, Etienne, Ganguly, Sanmay, Gross, Eilam, Heinrich, Lukas, Kado, Marumi, Kakati, Nilotpal, Shlomi, Jonathan, Soybelman, Nathalie
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Graph neural networks
Large Hadron Collider
Particle physics
Physics - High Energy Physics - Experiment
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container_title arXiv.org
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creator Di Bello, Francesco Armando
Dreyer, Etienne
Ganguly, Sanmay
Gross, Eilam
Heinrich, Lukas
Kado, Marumi
Kakati, Nilotpal
Shlomi, Jonathan
Soybelman, Nathalie
description 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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subjects Graph neural networks
Large Hadron Collider
Particle physics
Physics - High Energy Physics - Experiment
title Set-Conditional Set Generation for Particle Physics
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