[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119936-en":3,"doc-seo-119936-105":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":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},119936,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine Learning Techniques for Intermediate Mass Gap Lepton Partner Searches at the Large Hadron Collider - Abstract","The document studies boosted decision tree (BDT)–based machine learning methods for Large Hadron Collider searches targeting pair-produced lepton partners that decay into leptons plus invisible particles. The focus is on intermediate mass splittings (~30 GeV) where electroweak backgrounds limit progress beyond LEP. The analysis shows improved discovery reach for a benchmark lepton-partner mass (~110 GeV) at 300 fb−1, with signal-to-background around 0.3, and potential exclusion up to ~160 GeV. The work motivates ML as a decisive probe of MSSM parameter space and highlights techniques applicable to other LHC searches with complex backgrounds.","arXiv :2309 . 10197v2 [hep-ph] 17 Apr 2024  \nMI-HET-810, HRI-RECAPP-2023-08, UH511-1330-2023, CETUP-2023-007  \nMachine Learning Techniques for Intermediate Mass Gap Lepton Partner Searches at the Large Hadron Collider  \nBhaskar Dutta, 1 Tathagata Ghosh,2 Alyssa Horne,3, 4 Jason Kumar,5 Sean Palmer,3, 6 Pearl Sandick,7 Marcus Snedeker,3, 8 Patrick Stengel,9 and Joel W. Walker3  \n1 Department of Physics and Astronomy, Mitchell Institute for Fundamental Physics and Astronomy,  \nTexas A&M University, College Station, TX 77843, USA  \n2 Harish-Chandra Research Institute, A CI of Homi Bhabha National Institute,  \nChhatnag Road, Jhusi, Prayagraj 211019, India  \n3 Department of Physics, Sam Houston State University, Huntsville, TX 77341, USA  \n4 Department of Physics, Michigan Technological University, Houghton, MI 49931, USA  \n5 Department of Physics and Astronomy, University of Hawai’i, Honolulu, HI 96822, USA  \n6 Department of Physics, New Mexico State University, Las Cruces, NM 88003, USA  \n7 Department of Physics and Astronomy, University of Utah, Salt Lake City, UT 84112, USA  \n8 Department of Physics, East Carolina University, Greenville, NC 27858, USA  \n9 Istituto Nazionale di Fisica Nucleare, Sezione di Ferrara, via Giuseppe Saragat 1, I-44122 Ferrara, Italy  \nWe consider machine learning techniques associated with the application of a Boosted Decision Tree (BDT) to searches at the Large Hadron Collider (LHC) for pair-produced lepton partners which decay to leptons and invisible particles. This scenario can arise in the Minimal Supersymmetric Standard Model (MSSM), but can be realized in many other extensions ofthe Standard Model (SM) . We focus on the case of intermediate mass splitting ( ∼ 30GeV) between the dark matter (DM) and the scalar. For these mass splittings, the LHC has made little improvement over LEP due to large electroweak backgrounds. We find that the use of machine learning techniques can push the LHC well past discovery sensitivity for a benchmark model with a lepton partner mass of ∼ 110GeV, for an integrated luminosity of 300 fb −1, with a signal-to-background ratio of ∼ 0.3. The LHC could exclude models with a lepton partner mass as large as ∼ 160GeV with the same luminosity. The use of machine learning techniques in searches for scalar lepton partners at the LHC could thus definitively probe the parameter space of the MSSM in which scalar muon mediated interactions between SM muons and Majorana singlet DM can both deplete the relic density through dark matter annihilation and satisfy the recently measured anomalous magnetic moment of the muon. We identify several machine learning techniques which can be useful in other LHC searches involving large and complex backgrounds.  \nI. INTRODUCTION  \nA wide variety of scenarios for physics beyond the Standard Model (BSM) have been probed experimentally with the Large Hadron Collider (LHC) . However, as no conclusive evidence of BSM physics has been found yet, focus has turned to scenarios and signatures which are more diffi˜ cult to probe. One particularly difficult scenario is the pair  \nproduction of scalar lepton partners of SM fermions (ℓ± ), each of which decays to a lepton and an invisible particle (˜ℓ → ℓX) with a ∼ 30GeV mass splitting. This scenario can arise in the minimal supersymmetric standard model (MSSM) [1, 2], as well as in other BSM models [2, 3] often specifically motivated by the need for a viable dark matter candidate and by recent measurements of the anomalous magnetic moment of the muon. This scenario is difficult to probe at the LHC because there is a large SM background from the production of electroweak gauge bosons, decaying to leptons and neutrinos. As a result, current LHC constraints [4, 5] on this scenario show little improvement over LEP [6] . Although a variety of new analysis strategies have been proposed, there is no clear-cut strategy for effectively separating signal from background in models with these moderately compressed pa","cbCaihCS4aH2xBsp","https://ap.wps.com/l/cbCaihCS4aH2xBsp","pdf",2449448,1,22,"English","en",105,"# Introduction\n## Compressed scalar lepton partner signatures at the LHC\n## Limitations from electroweak backgrounds and prior strategies\n## Motivation for machine learning approaches\n## Role of kinematic variables and selection challenges","[{\"question\":\"What physics scenario does the document focus on?\",\"answer\":\"It focuses on searches at the LHC for pair-produced scalar lepton partners that decay into a lepton and an invisible particle, with an intermediate mass splitting of about 30 GeV.\"},{\"question\":\"Why is the intermediate-mass-splitting region difficult for conventional LHC analyses?\",\"answer\":\"Because electroweak backgrounds producing leptons and neutrinos are large, and the compressed spectra make the leptons and missing transverse energy less distinctive than in other regions.\"},{\"question\":\"What do the results indicate about using BDT-based machine learning?\",\"answer\":\"Using machine learning (boosted decision trees) can push the LHC discovery sensitivity well past discovery reach for a benchmark model with a lepton-partner mass around 110 GeV at 300 fb−1, and it can exclude masses up to about 160 GeV at the same luminosity.\"}]","Machine Learning Techniques for Intermediate Mass Gap Lepton Partner Searches at the Large Hadron Collider - Abstract | PDF",1785727079,55,{"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-techniques-for-intermediate-mass-gap-lepton-partner-searches-at-the-large-hadron-collider-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-techniques-for-intermediate-mass-gap-lepton-partner-searches-at-the-large-hadron-collider-abstract/119936/",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","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What physics scenario does the document focus on?","Question",{"text":75,"@type":76},"It focuses on searches at the LHC for pair-produced scalar lepton partners that decay into a lepton and an invisible particle, with an intermediate mass splitting of about 30 GeV.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is the intermediate-mass-splitting region difficult for conventional LHC analyses?",{"text":80,"@type":76},"Because electroweak backgrounds producing leptons and neutrinos are large, and the compressed spectra make the leptons and missing transverse energy less distinctive than in other regions.",{"name":82,"@type":73,"acceptedAnswer":83},"What do the results indicate about using BDT-based machine learning?",{"text":84,"@type":76},"Using machine learning (boosted decision trees) can push the LHC discovery sensitivity well past discovery reach for a benchmark model with a lepton-partner mass around 110 GeV at 300 fb−1, and it can exclude masses up to about 160 GeV at the same luminosity.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]