LHC Hadronic Jet Generation Using Convolutional Variational Autoencoders with Normalizing Flows

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

Machine Learning Science and Technology Pub Date : 2023-10-19 DOI:10.1088/2632-2153/ad04ea

Breno Orzari, Nadezda Chernyavskaya, Raphael Cobe, Javier Mauricio Duarte, Jefferson Fialho, Dimitrios Gunopulos, Raghav Kansal, Maurizio Pierini, Thiago Tomei, Mary Touranakou

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引用次数: 0

Abstract

Abstract In high energy physics, one of the most important processes for collider data analysis is the comparison of collected and simulated data. Nowadays the state-of-the-art for data generation is in the form of Monte Carlo (MC) generators. However, because of the upcoming high-luminosity upgrade of the LHC, there will not be enough computational power or time to match the amount of needed simulated data using MC methods. An alternative approach under study is the usage of machine learning generative methods to fulfill that task. Since the most common final-state objects of high-energy proton collisions are hadronic jets, which are collections of particles collimated in a given region of space, this work aims to develop a convolutional variational autoencoder (ConVAE) for the generation of particle-based LHC hadronic jets. Given the ConVAE's limitations, a normalizing flow (NF) network is coupled to it in a two-step training process, which shows improvements on the results for the generated jets. The ConVAE+NF network is capable of generating a jet in 18.30 ± 0.04 μs, making it one of the fastest methods for this task up to now.

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使用归一化流的卷积变分自编码器生成LHC强子射流

在高能物理中，对撞机数据分析最重要的过程之一是对采集数据和模拟数据进行比较。如今，最先进的数据生成技术是蒙特卡洛(MC)生成器。然而，由于即将到来的大型强子对撞机的高亮度升级，将没有足够的计算能力或时间来匹配使用MC方法所需的模拟数据量。正在研究的另一种方法是使用机器学习生成方法来完成该任务。由于高能质子碰撞最常见的最终状态对象是强子射流，它是在给定空间区域中准直的粒子集合，因此本工作旨在开发一种卷积变分自编码器(ConVAE)，用于生成基于粒子的LHC强子射流。考虑到ConVAE的局限性，在两步训练过程中，将一个归一化流(NF)网络与之耦合，这表明生成的射流结果有所改善。ConVAE+NF网络能够在18.30±0.04 μs内产生射流，是目前最快的方法之一。

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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.