[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118962-en":3,"doc-seo-118962-105":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},118962,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","The Landscape of Unfolding with Machine Learning - Paper","Machine-learning based techniques enable data unfolding without binning while retaining multi-dimensional correlations. The paper surveys a collection of established, upgraded, and newly proposed ML-unfolding approaches, and benchmarks them using identical datasets. Results show accurate reproduction of particle-level spectra across complex observables. The conceptual diversity provides a practical toolkit for future measurements aimed at probing the Standard Model with unprecedented detail and potential sensitivity to new phenomena.","UC Irvine  \nUC Irvine Previously Published Works  \nTitle  \nThe landscape of unfolding with machine learning  \nPermalink  \n[https://escholarship.org/uc/item/7gg776x9](https://escholarship.org/uc/item/7gg776x9)  \nJournal  \nSciPost Physics, 18(2)  \nISSN  \n2542-4653  \nAuthors  \nHuetsch, Nathan  \nMariño Villadamigo, Javier Shmakov, Alexander et al.  \nPublication Date  \n2025  \nDOI  \n10.21468/scipostphys.18.2.070  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons Attribution License, available at [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \narXiv :2404 . 18807v2 [hep-ph] 17 May 2024  \nThe Landscape of Unfolding with Machine Learning  \nNathan Huetsch1 , Javier Mariño Villadamigo1 , Alexander Shmakov2 , Sascha Diefenbacher3 , Vinicius Mikuni3 , Theo Heimel1 , Michael Fenton2 , Kevin Greif2 , Benjamin Nachman3,4 , Daniel Whiteson2 , Anja Butter1,5 , and Tilman Plehn1,6  \n1 Institut für Theoretische Physik, Universität Heidelberg, Germany  \n2 Department of Physics and Astronomy, University of California, Irvine, USA  \n3 Physics Division, Lawrence Berkeley National Laboratory, Berkeley, USA  \n4 Berkeley Institute for Data Science, University of California, Berkeley, USA  \n5 LPNHE, Sorbonne Université, Université Paris Cité, CNRS/IN2P3, Paris, France  \n6 Interdisciplinary Center for Scientific Computing (IWR), Universität Heidelberg, Germany  \nMay 20, 2024  \nAbstract  \nRecent innovations from machine learning allow for data unfolding, without binning and including correlations across many dimensions. We describe a set of known, upgraded, and new methods for ML-based unfolding. The performance of these approaches are evaluated on the same two datasets. We find that all techniques are capable of accurately reproducing the particle-level spectra across complex observables. Given that these approaches are conceptually diverse, they offer an exciting toolkit for a new class of measurements that can probe the Standard Model with an unprecedented level of detail and may enable sensitivity to new phenomena.  \n\n| Contents\u003Cbr>1 Introduction\u003Cbr>2 ML-Unfolding\u003Cbr>2.1 Reweighting: (b)OmniFold\u003Cbr>2.2 Mapping distributions: Schrödinger Bridge and Direct Diffusion\u003Cbr>2.3 Generative unfolding: cINN, Transfermer, CFM, TraCFM, Latent Diffusion\u003Cbr>3 Detector unfolding: Z+jets\u003Cbr>3.1 Data and preprocessing\u003Cbr>3.2 Reweighting\u003Cbr>3.3 Mapping distributions\u003Cbr>3.4 Generative unfolding\u003Cbr>3.5 Learned event migration\u003Cbr>4 Unfolding to parton level: top pairs\u003Cbr>4.1 Data\u003Cbr>4.2 Generative unfolding\u003Cbr>4.3 Generative unfolding using physics\u003Cbr>5 Outlook\u003Cbr>A Combined Z+jets results\u003Cbr>B Hyperparameters\u003Cbr>References | 3\u003Cbr>4 4\u003Cbr>5 8\u003Cbr>14\u003Cbr>14\u003Cbr>15\u003Cbr>17\u003Cbr>19\u003Cbr>19\u003Cbr>22\u003Cbr>22\u003Cbr>22\u003Cbr>24\u003Cbr>26\u003Cbr>28\u003Cbr>29\u003Cbr>30 |\n| --- | --- |\n\n1 Introduction  \nParticle physics experiments seek to reveal clues about the fundamental properties of particles and their interactions. A key challenge is that predictions from quantum field theory are atthe level of partons, while experiments observe the corresponding detector signatures. Precise and detailed simulations link these two levels [1] . They fold predictions for the hard process through QCD effects, hadronization, and the detector response to compare with data. This statistically powerful forward inferences approach has been widely used.  \nHowever, forward inference requires access to the data and the detector simulation. These conditions are rarely satisfied outside of a given experiment, severely limiting the ability of the broader community to study particle physics data. In addition, analysis of data from the highluminosity LHC with forward inference will require precise simulations for every hypothesis, challenging available computing resources.  \nAn alternative approach is unfolding. Rather than correcting pr","cbCaic2dyqs2kjXN","https://ap.wps.com/l/cbCaic2dyqs2kjXN","pdf",1773083,1,36,"English","en",105,"# Introduction\n# ML-Unfolding\n## Reweighting: (b)OmniFold\n## Mapping distributions: Schrödinger Bridge and Direct Diffusion\n## Generative unfolding: cINN, Transfermer, CFM, TraCFM, Latent Diffusion\n# Detector unfolding: Z+jets\n## Data and preprocessing\n## Reweighting\n## Mapping distributions\n## Generative unfolding\n## Learned event migration\n# Unfolding to parton level: top pairs\n## Data\n## Generative unfolding\n## Generative unfolding using physics\n# Outlook\n## A Combined Z+jets results\n## B Hyperparameters\n## References","[{\"question\":\"What problem does ML-based unfolding address compared with traditional unfolding?\",\"answer\":\"It enables unfolding of unbinned cross sections across many dimensions, avoiding the need to pre-select observables and binning required by traditional methods.\"},{\"question\":\"Which types of ML-unfolding methods are covered in the paper?\",\"answer\":\"The paper describes reweighting approaches such as OmniFold, distribution mapping methods like Schrödinger Bridge and Direct Diffusion, and generative unfolding methods including cINN and diffusion-based generative models.\"},{\"question\":\"How are the ML methods evaluated and what do the results show?\",\"answer\":\"The approaches are benchmarked on the same two datasets, demonstrating accurate reproduction of particle-level spectra over complex observables, and suggesting suitability for high-detail Standard Model measurements.\"}]","The Landscape of Unfolding with Machine Learning - Paper | PDF",1785721227,91,{"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},"the-landscape-of-unfolding-with-machine-learning-paper","",{"@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/the-landscape-of-unfolding-with-machine-learning-paper/118962/",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","2026-08-04","2026-08-03",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},"What problem does ML-based unfolding address compared with traditional unfolding?","Question",{"text":76,"@type":77},"It enables unfolding of unbinned cross sections across many dimensions, avoiding the need to pre-select observables and binning required by traditional methods.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which types of ML-unfolding methods are covered in the paper?",{"text":81,"@type":77},"The paper describes reweighting approaches such as OmniFold, distribution mapping methods like Schrödinger Bridge and Direct Diffusion, and generative unfolding methods including cINN and diffusion-based generative models.",{"name":83,"@type":74,"acceptedAnswer":84},"How are the ML methods evaluated and what do the results show?",{"text":85,"@type":77},"The approaches are benchmarked on the same two datasets, demonstrating accurate reproduction of particle-level spectra over complex observables, and suggesting suitability for high-detail Standard Model measurements.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]