[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128466-en":3,"doc-seo-128466-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},128466,962085662650,"Jiven","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine Learning methods for simulating particle response in the Zero Degree Calorimeter at the ALICE experiment - fast simulation with neural networks","Machine learning methods are developed to simulate the neutron Zero Degree Calorimeter (ZDC) response at the ALICE experiment, CERN, addressing the heavy computational cost of traditional Monte Carlo simulation used across the LHC grid. The study applies neural network classifiers and generative models, focusing on variational autoencoders and generative adversarial networks. A GAN architecture is expanded with an additional regularisation network and an effective postprocessing step, while classification filters inputs with no calorimeter response. Results show a simulation speed-up of two orders of magnitude while preserving high fidelity.","arXiv :2306 . 13606v1 [ cs .CV] 23 Jun 2023  \nMachine Learning methods for simulating particle response in the Zero Degree Calorimeter at the ALICE experiment, CERN  \nJan Dubi´nski 1 , Kamil Deja 1 , Sandro Wenzel2 ,  \nPrzemyslaw Rokita 1 , and Tomasz Trzcinski 1 ,3 ,4 ,5  \n1Warsaw University of Technology 2 CERN  \n3 Jagiellonian University 4 Tooploox  \n5 IDEAS NCBR  \n[jan.dubinski.dokt@pw.edu.pl](jan.dubinski.dokt@pw.edu.pl)  \nAbstract. Currently, over 50% of the computing power at CERN’s GRID is used to run High Energy Physics simulations. The recent updates at the Large Hadron Collider (LHC) create the need for developing more efficient simulation methods. In particular, there exist a demand for a fast simulation of the neutron Zero Degree Calorimeter, where existing Monte Carlo-based methods impose a significant computational burden. We propose an alternative approach to the problem that leverages machine learning. Our solution utilises neural network classifiers and generative models to directly simulate the response of the calorimeter.  \nIn particular, we examine the performance of variational autoencoders and generative adversarial networks, expanding the GAN architecture by an additional regularisation network and a simple, yet effective postprocessing step. Our approach increases the simulation speed by 2 orders of magnitude while maintaining the high fidelity of the simulation.  \n1 Introduction  \nAt the European Organisation for Nuclear Research (CERN) located near Geneva, Swizterland physicists and engineers study the fundamental properties of matter through High Energy Physics (HEP) experiments. Inside the Large Hadron Collider (LHC), two particle beams are being accelerated nearly to the speed of light and brought to collide in order to recreate the extreme conditions of the early universe just after the Big Bang.  \nUnderstanding what happens during these collisions requires complex simulations that generate the expected response of the detectors inside the LHC. The currently used methods are based on statistical Monte Carlo simulations of physical interactions of particles. The high-fidelity results they provide come ata price of high computational cost. Currently, standard simulation procedures occupy the majority of CERN’s computing grid system (over 500 000 CPUs in 170 centres) . To address the shortcomings of this approach, an alternative solution for simulation in high-energy physics experiments that leverages generative machine learning techniques has been proposed recently. [4,6,13]  \n2 J. Dubi´nski et al.  \nIn this work, we examine the performance of machine learning models on the task of simulating the data from the neutron Zero Degree Calorimeter (ZDC) from the ALICE experiment, CERN. We apply a variational autoencoder and generative adversarial networks to the problem treating the results as baselines. Moreover, we expand the GAN architecture with an additional regularisation network and a simple, yet effective postprocessing step. Our solution uses a neural network classifier to filter inputs that do not cause any response of the calorimeter before passing the data to the generative model.  \nThe proposed models are able to generate the end data directly, without simulating the effect of every physical law and interaction between particles and the experiment’s matter separately. Therefore, this approach greatly reduces the demand for computational power. Our approach increases the simulation speed by 2 orders of magnitude while maintaining the high fidelity of the simulation.  \n2 Related work  \nThe need for simulating complex processes exists across many scientific domains. In recent years, solutions based on generative machine learning models have been proposed as an alternative to existing methods in cosmology [16] and genetics [15] . However, one of the most profound applications for generative simulations is in the field of High Energy Physics, where machine learning models can be used as a resourc","cbCaiiRwkXRzdSpQ","https://ap.wps.com/l/cbCaiiRwkXRzdSpQ","pdf",943160,1,11,"English","en",105,"# Introduction\n## Motivation and limitations of Monte Carlo simulation\n# Related work\n## Generative models in high energy physics\n# Zero Degree Calorimeter simulation\n## Detector principle and segmentation\n# Proposed method\n## Variational autoencoders and expanded GAN architecture\n## Classification filtering and postprocessing","[{\"question\":\"Why are faster ZDC simulation methods needed at CERN?\",\"answer\":\"Current Monte Carlo-based simulations achieve high fidelity but require substantial computational resources, consuming most of CERN’s grid capacity. LHC updates increase the demand for more efficient simulation approaches, especially for neutron ZDC.\"},{\"question\":\"Which machine learning models are used to simulate the calorimeter response?\",\"answer\":\"The approach evaluates variational autoencoders and generative adversarial networks (GANs). It further extends the GAN with an additional regularisation network and adds a simple postprocessing step.\"},{\"question\":\"How does the method maintain simulation quality while improving speed?\",\"answer\":\"A neural network classifier filters inputs that produce no calorimeter response before running the generative model, and the GAN includes regularisation plus postprocessing. This combination yields a two orders of magnitude speed increase while maintaining high-fidelity output.\"}]","Machine Learning methods for simulating particle response in the Zero Degree Calorimeter at the ALICE experiment - fast simulation with neural networks | PDF",1786001220,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-methods-for-simulating-particle-response-in-the-zero-degree-calorimeter-at-the-alice-experiment-fast-simulation-with-neural-networks","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-methods-for-simulating-particle-response-in-the-zero-degree-calorimeter-at-the-alice-experiment-fast-simulation-with-neural-networks/128466/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-06",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why are faster ZDC simulation methods needed at CERN?","Question",{"text":76,"@type":77},"Current Monte Carlo-based simulations achieve high fidelity but require substantial computational resources, consuming most of CERN’s grid capacity. LHC updates increase the demand for more efficient simulation approaches, especially for neutron ZDC.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning models are used to simulate the calorimeter response?",{"text":81,"@type":77},"The approach evaluates variational autoencoders and generative adversarial networks (GANs). It further extends the GAN with an additional regularisation network and adds a simple postprocessing step.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the method maintain simulation quality while improving speed?",{"text":85,"@type":77},"A neural network classifier filters inputs that produce no calorimeter response before running the generative model, and the GAN includes regularisation plus postprocessing. 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