[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122415-en":3,"doc-seo-122415-105":30,"detail-sidebar-cat-0-en-105":91},{"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":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},122415,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Machine Learning Methods for Cross Section Measurements - Thesis abstract","Precise differential cross section measurements enable stringent tests of Standard Model predictions at the energy frontier and support searches for new physics, but extracting them from collider data is an ill-posed inverse problem. Unfolding (deconvolution) removes detector distortions to reconstruct particle-level truth, while conventional binned techniques can introduce artifacts, arbitrary binning choices, and high-dimensional computational costs. This dissertation develops a unified machine-learning framework: Neural Posterior Unfolding with differentiable conditional normalising flows, Moment Unfolding without binning, and Reweighting Adversarial Networks for full spectral unfolding, plus a statistical treatment of event correlations to ensure correct uncertainty coverage.","UC Berkeley  \nUC Berkeley Electronic Theses and Dissertations  \nTitle  \nMachine Learning Methods for Cross Section Measurements  \nPermalink  \n[https://escholarship.org/uc/item/6qk049pf](https://escholarship.org/uc/item/6qk049pf)  \nISBN  \n9798293893348  \nAuthor  \nDesai, Krish  \nPublication Date  \n2025-05-01  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nMachine Learning Methods for Cross Section Measurements  \nBy  \nKrish Desai  \nA dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in  \nPhysics  \nin the  \nGraduate Division  \nof the  \nUniversity of California, Berkeley  \nCommittee in charge:  \nProfessor Benjamin Nachman, Co-Chair Professor Uros Seljak, Co-Chair Professor Joshua Bloom  \nProfessor Saul Perlmutter  \nSummer 2025  \nMachine Learning Methods for Cross Section Measurements  \nCopyright 2025  \nBy  \nKrish Desai  \n1  \nAbstract  \nMachine Learning Methods for Cross Section Measurements  \nby  \nKrish Desai  \nDoctor of Philosophy in Physics  \nUniversity of California, Berkeley  \nProfessor Benjamin Nachman, Co-Chair  \nProfessor Uros Seljak, Co-Chair  \nPrecise differential cross section measurements are indispensable for tests of Standard Model predictions at the energy frontier and for searches for new physics, yet their extraction from collider data is an ill posed inverse problem. Unfolding, also known as deconvolution, is the process of removing detector distortions to reconstruct particle level truth from detector level data. Conventional, histogram based, binned unfolding techniques introduce artifacts, impose arbitrary bin edges, and become  \n2  \ncomputationally prohibitive in high dimensional phase spaces, potentially obscuring underlying physics.  \nThis dissertation develops a unified framework that leverages modern machine learning techniques to surmount these limitations. First, the Neural Posterior Unfolding (NPU) method demonstrates how conditional normalising flows can serve as differentiable surrogates of detector response, enabling likelihood based unfolding through implicit regularisation. Building on this foundation, the Moment Unfolding algorithm directly extracts distribution moments without binning, providing precise experimental predictions for effective field theories and phenomenological models. The framework is further advanced by development of Reweighting Adversarial Networks (RANs), which perform full spectral unfolding using adversarial training to implement particle level reweighting guided by a detector level classifier, offering theoretical and computational advantages over extant methods.  \nA critical statistical analysis of event correlations in unfolded data reveals systematic misestimation of uncertainties when these correlations are ignored, leading to methodological recommendations that ensure correct coverage for all derived observables. The methods presented in this dissertation are validated using both idealised Gaussian distributions and proton–proton collision simulations of the CMS experiment as realistic particle physics examples, specifically simulations of Z+jets events, demonstrating significant improvements in precision, accuracy, and computational efficiency, reducing computational time by orders of magnitude while maintaining  \n3  \nor exceeding the precision of existing methods for the unbiased recovery of spectral features.  \nBy marrying statistical rigour with powerful machine learning methods, this work establishes a scalable blueprint for precision measurements at current and future high energy physics experiments. The resulting open source software enables more reliable extraction of fundamental physics parameters from complex detector data, advancing the ability to test theoretical models and potentially discover new phenomena.  \ni  \nPublications Resulting from This Dissertation  \n[1] K. Desai, O. Long, and B. Nachman. “Unbi","cbCaivjaEYJhkAV5","https://ap.wps.com/l/cbCaivjaEYJhkAV5","pdf",12358735,1,828,"English","en",105,"# Abstract\n# Publications Resulting from This Dissertation\n# Contents\n## Introduction and physics background\n## Theoretical foundations","[{\"question\":\"Why are unfolding methods important for cross section measurements?\",\"answer\":\"Unfolding removes detector distortions so particle-level truths can be reconstructed from detector-level collider data. It addresses the ill-posed inverse nature of the extraction problem.\"},{\"question\":\"What are the main machine learning approaches proposed in the dissertation?\",\"answer\":\"The dissertation introduces Neural Posterior Unfolding using conditional normalising flows, Moment Unfolding that extracts distribution moments without binning, and Reweighting Adversarial Networks that perform full spectral unfolding via adversarial training.\"},{\"question\":\"How does the dissertation handle statistical uncertainties in unfolded data?\",\"answer\":\"It performs a critical statistical analysis of event correlations and identifies systematic misestimation of uncertainties when correlations are ignored. It then provides recommendations to guarantee correct coverage for derived observables.\"}]","Machine Learning Methods for Cross Section Measurements - Thesis abstract | PDF",1785810519,2087,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-methods-for-cross-section-measurements-thesis-abstract","",{"@graph":36,"@context":85},[37,54,68],{"@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-cross-section-measurements-thesis-abstract/122415/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are unfolding methods important for cross section measurements?","Question",{"text":75,"@type":76},"Unfolding removes detector distortions so particle-level truths can be reconstructed from detector-level collider data. It addresses the ill-posed inverse nature of the extraction problem.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What are the main machine learning approaches proposed in the dissertation?",{"text":80,"@type":76},"The dissertation introduces Neural Posterior Unfolding using conditional normalising flows, Moment Unfolding that extracts distribution moments without binning, and Reweighting Adversarial Networks that perform full spectral unfolding via adversarial training.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the dissertation handle statistical uncertainties in unfolded data?",{"text":84,"@type":76},"It performs a critical statistical analysis of event correlations and identifies systematic misestimation of uncertainties when correlations are ignored. 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