Set-Conditional Set Generation for Particle Physics

IF 6.3 2区物理与天体物理 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Machine Learning Science and Technology Pub Date : 2023-10-13 DOI:10.1088/2632-2153/ad035b

Nathalie Soybelman, Nilotpal Kakati, Lukas Heinrich, Francesco Armando Di Bello, Etienne Dreyer, Sanmay Ganguly, Eilam Gross, Marumi Kado, Jonathan Shlomi

引用次数: 3

Abstract

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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粒子物理的条件集生成

摘要:粒子物理数据的模拟是大型强子对撞机物理分析的一个基本但计算密集的组成部分，在大型强子对撞机中，观测集值数据是在一组入射粒子的条件下生成的。为了加速这一任务，我们提出了一种基于图神经网络和插槽注意力组件的新型生成模型，其性能超过了现有基线。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Machine Learning Science and Technology Computer Science-Artificial Intelligence

CiteScore

9.10

自引率

4.40%

发文量

审稿时长

5 weeks

期刊介绍： Machine Learning Science and Technology is a multidisciplinary open access journal that bridges the application of machine learning across the sciences with advances in machine learning methods and theory as motivated by physical insights. Specifically, articles must fall into one of the following categories: advance the state of machine learning-driven applications in the sciences or make conceptual, methodological or theoretical advances in machine learning with applications to, inspiration from, or motivated by scientific problems.