[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119781-en":3,"doc-seo-119781-105":29,"detail-sidebar-cat-0-en-105":90},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},119781,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Refining fast simulation using machine learning","At the CMS experiment, fast Monte Carlo simulation (FastSim) accelerates event production by about a factor of 10 compared with Geant4-based FullSim, but it reduces accuracy for some final analysis observables. This work presents a machine learning refinement strategy that applies post-hoc corrections to FastSim-produced samples. A regression neural network is trained with a sophisticated combination of multiple loss functions, yielding substantially improved agreement with FullSim outputs and stronger correlations between observables and external parameters. The method offers a promising replacement for conventional correction factors, enabling more accurate and broader FastSim usage.","arXiv :2309 . 12919v1 [physics .ins-det] 22 Sep 2023  \nRefining fast simulation using machine learning  \nSamuel Bein1 , Patrick Connor1,2 , Kevin Pedro3 , Peter Schleper1 , and Moritz Wolf1 , ∗(on behalf of the CMS Collaboration)  \n1University of Hamburg, Institut für Experimentalphysik, Germany  \n2Center for Data and Computing in Natural Sciences, Hamburg, Germany  \n3Fermi National Accelerator Laboratory, Batavia, IL, USA  \nAbstract. At the CMS experiment, a growing reliance on the fast Monte Carlo application (FastSim) will accompany the high luminosity and detector granularity expected in Phase 2 . The FastSim chain is roughly 10 times faster than the application based on the Geant4 detector simulation and full reconstruction referred to as FullSim. However, this advantage comes at the price of decreased accuracy in some of the final analysis observables. In this contribution, a machine learning-based technique to refine those observables is presented. We employ a regression neural network trained with a sophisticated combination of multiple loss functions to provide post-hoc corrections to samples produced by the FastSim chain. The results show considerably improved agreement with the FullSim output and an improvement in correlations among output observables  \nand external parameters. This technique is a promising replacement for existing correction factors, providing higher accuracy and thus contributing to the wider usage of FastSim.  \n1 Introduction  \nSimulating particle collisions, the subsequent detector response, and the reconstruction of the final state are crucial for modern high energy physics. For the purpose of simulating events in the CMS detector [1], the collaboration largely relies on a simulation chain based on Geant4 [2, 3], referred to as FullSim, which gives an accurate representation of the truth [4, 5] . However, this requires a considerable amount of computing power. Therefore, another simulation chain has been established, which uses approximations to speed up the process by roughly a factor of 10 [6–8] . This application, known as FastSim, provides output with the same format and structure as FullSim. Looking towards the future with higher LHC luminosity and increased CMS detector granularity [9], FastSim will only gain in importance as the collaboration strives to keep the computing needs within budget in Phase 2 [10, 11] .  \nThe output of the FastSim chain is generally in good agreement with the FullSim chain, but discrepancies on the order of up to 20% are observed in some analysis observables. Traditionally, differences between simulation samples (or differences between simulation and data) are treated with dedicated correction factors or weights that are derived either by physics object groups or by individual users carrying out analyses. These corrections or weights are applied to events or physics objects to correct certain biases, for example in the transverse  \n∗[e-mail: moritz](e-mail: moritz.wolf@cern.ch)[.](e-mail: moritz.wolf@cern.ch)[wolf@cern](e-mail: moritz.wolf@cern.ch)[.](e-mail: moritz.wolf@cern.ch)[ch](e-mail: moritz.wolf@cern.ch)  \nmomentum or selection efficiency of jets, photons, or leptons. One approach that goes beyond these traditional corrections in terms of sophistication and accuracy is the application of weights that are derived using machine learning-based methods, such as the DCTR (deep neural networks using classification for tuning and reweighting) approach [12] . While the accuracy of simulated variables, as well as correlations among the variables, is improved compared to the unweighted sample, the use of weights reduces the statistical power, undermining the advantage of fast simulation applications. In contrast to reweighting, the aim of our method is to change the values of the sample to reach better agreement with the target sample, without the need for weights. Such a refinement has been studied and tested in air shower images using a Wasserstein GAN [13], de","cbCaieuJt9fJQB8N","https://ap.wps.com/l/cbCaieuJt9fJQB8N","pdf",383506,1,"English","en",105,"# Abstract\n# Introduction\n# Data sample\n# Method","[{\"question\":\"What is FastSim and why is it used in the CMS experiment?\",\"answer\":\"FastSim is a faster Monte Carlo simulation chain for CMS detector events. It provides outputs similar in format to FullSim while reducing computational cost by roughly an order of magnitude.\"},{\"question\":\"What problem does the paper address with FastSim results?\",\"answer\":\"Although FastSim generally agrees with FullSim, it can show discrepancies up to around 20% in some analysis observables. These differences typically require correction factors or weights.\"},{\"question\":\"How does the proposed method improve FastSim accuracy?\",\"answer\":\"The approach trains a regression neural network to produce refined post-hoc corrections for FastSim outputs. It uses multiple loss functions to improve agreement with FullSim and to enhance correlations among output observables and external parameters.\"}]","Refining fast simulation using machine learning | PDF",1785726281,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"refining-fast-simulation-using-machine-learning","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/refining-fast-simulation-using-machine-learning/119781/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What is FastSim and why is it used in the CMS experiment?","Question",{"text":74,"@type":75},"FastSim is a faster Monte Carlo simulation chain for CMS detector events. It provides outputs similar in format to FullSim while reducing computational cost by roughly an order of magnitude.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What problem does the paper address with FastSim results?",{"text":79,"@type":75},"Although FastSim generally agrees with FullSim, it can show discrepancies up to around 20% in some analysis observables. These differences typically require correction factors or weights.",{"name":81,"@type":72,"acceptedAnswer":82},"How does the proposed method improve FastSim accuracy?",{"text":83,"@type":75},"The approach trains a regression neural network to produce refined post-hoc corrections for FastSim outputs. It uses multiple loss functions to improve agreement with FullSim and to enhance correlations among output observables and external parameters.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":28,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":28,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]