[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125533-en":3,"doc-seo-125533-105":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},125533,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Applying Machine Learning Techniques To Intermediate-Length Cascade Decays - Abstract","Cascade decays arise generically in collider phenomenology of Standard Model extensions with partner particles, but discovery becomes difficult when new-particle spectra are compressed and signal cross sections are small. Achieving discovery-level significance and extracting intermediate-state properties is a long-standing challenge, while some optimized methods exist for specific topologies. This work studies a benchmark four-final-state decay topology and uses machine learning to determine optimal kinematic observables for discovery, spin identification, and mass measurement. The analysis confirms the strong effectiveness of observable κ4 and quantifies spin and mass precision versus signal size.","arXiv :2210 .0 1 178v 1 [hep-ph] 3 Oct 2022  \nOctober 5, 2022 UTTG 08-2022  \nApplying Machine Learning Techniques To Intermediate-Length Cascade  \nDecays  \nMaaz Ul Haq,a Can Kilic,a Benjamin Lawrence-Sanderson,a and Ram Purandhar  \nReddy Sudhaa  \na Theory Group, Department of Physics  \nUniversity of Texas at Austin, Austin, TX 78712, USA  \nAbstract: In the collider phenomenology of extensions of the Standard Model with partner particles, cascade decays occur generically, and they can be challenging to discover when the spectrum of new particles is compressed and the signal cross section is low. Achieving discovery-level signi􀀌cance and measuring the properties of the new particles appearing as intermediate states in the cascade decays is alongstanding problem, with analysis techniques for some decay topologies already optimized. We focus our attention on a benchmark decay topology with four 􀀌nal state particles where there is room for improvement, and where multidimensional analysis techniques have been shown to be e􀀋ective in the past. Using machine learning techniques, we identify the optimal kinematic observables for discovery, spin determination and mass measurement. In agreement with past work, we con􀀌rm that the kinematic observable 􀀁 4 is highly e􀀋ective. We quantify the achievable accuracy for spin determination and for the precision for mass measurements as a function of the signal size.  \n\n| Contents\u003Cbr>1 Introduction\u003Cbr>2 Review of Phase Space and Kinematic Observables\u003Cbr>3 Review of Machine Learning Tools\u003Cbr>4 Analysis Based Solely on Final State Momenta\u003Cbr>4.1 Monte Carlo methods and selection cuts\u003Cbr>4.2 Measurement of mass di􀀋erences\u003Cbr>5 Identi􀀌cation of Optimized Variables\u003Cbr>6 Spin Determination\u003Cbr>6.1 Ensemble-based method\u003Cbr>6.2 Performance with DL variables\u003Cbr>6.3 Performance with HL variables\u003Cbr>6.4 Optimizing the cut thresholds\u003Cbr>7 Mass Scale Determination\u003Cbr>8 Conclusions | 1\u003Cbr>6\u003Cbr>8\u003Cbr>9\u003Cbr>10\u003Cbr>11\u003Cbr>14\u003Cbr>19\u003Cbr>19\u003Cbr>19\u003Cbr>20\u003Cbr>20\u003Cbr>21\u003Cbr>23 |\n| --- | --- |\n\n1 Introduction  \nWhile the Standard Model (SM) of particle physics is extremely successful in describing the known particles and their interactions, it is also known to be an incomplete description of fundamental physics. Some of the best studied extensions of the Standard Model that aim to stabilize the electroweak scale or explain the observed dark matter (DM) relic abundance hint at the existence of new degrees of freedom at roughly the TeV scale. Unfortunately, collider searches for such new particles have yielded only null results until now. Therefore, as the Large Hadron Collider (LHC) is getting ready to start its third run, even if new physics is 􀀌nally discovered, the signal will in all likelihood either have low statistics, or be di􀀎cult to distinguish from backgrounds, or possibly both. This makes it all the more crucial that LHC searches be optimized for maximal e􀀎ciency with these challenges in mind. Similarly, post-discovery, the measurement of the properties of the new particles, such as their masses, will be challenging for the same reasons.  \n\n|  | \u003Cbr>p 1 | \u003Cbr>p2 | \u003Cbr>p3 |\n| --- | --- | --- | --- |\n| X | Y | Z |  |\n\nFigure 1: Feynman diagram for our benchmark decay chain. X , Y , Z, and are all new particles, while p 1 ;2 ;3 are SM particles.  \nAmong possible 􀀌nal states for new physics at the LHC, supersymmetry (SUSY)-like production and decay channels of color-neutral particles, especially with a compressed spectrum for the new particles, o􀀋er good examples for the type of signatures mentioned above, as electroweak-only charged particles have low production cross sections, and compressed spectra result in soft momenta in the 􀀌nal state, so backgrounds cannot be reduced by using hard cuts. Our de􀀌nition of a SUSY-like channel is that new particles can only be produced in pairs due to a Z2 symmetry, and that each one decays to a lighter new particle plus SM particles, until the lightest new particle is reached, which is ","cbCaicpUM2ObTSX9","https://ap.wps.com/l/cbCaicpUM2ObTSX9","pdf",3031774,1,29,"English","en",105,"# 1 Introduction\n## 2 Review of Phase Space and Kinematic Observables\n## 3 Review of Machine Learning Tools\n## 4 Analysis Based Solely on Final State Momenta\n## 4.1 Monte Carlo methods and selection cuts\n## 4.2 Measurement of mass di􀀋erences\n## 5 Identi􀀌cation of Optimized Variables\n## 6 Spin Determination\n## 7 Mass Scale Determination\n## 8 Conclusions","[{\"question\":\"Why are intermediate-length cascade decays challenging to study at colliders?\",\"answer\":\"When the new-particle spectrum is compressed and the signal cross section is low, events resemble backgrounds and the available information is not sufficient to rely on simple one-dimensional kinematic features. This makes optimized, information-rich analysis necessary.\"},{\"question\":\"What does the study optimize using machine learning?\",\"answer\":\"It identifies optimal kinematic observables for three tasks: improving discovery prospects, determining particle spin, and measuring masses of the intermediate or new particles in the cascade.\"},{\"question\":\"What key observable is highlighted, and how is its performance characterized?\",\"answer\":\"The study confirms that the kinematic observable κ4 is highly effective, and it evaluates achievable accuracies for spin determination as well as mass-measurement precision as a function of signal size.\"}]","Applying Machine Learning Techniques To Intermediate-Length Cascade Decays - Abstract | PDF",1785899704,73,{"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},"applying-machine-learning-techniques-to-intermediate-length-cascade-decays-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/applying-machine-learning-techniques-to-intermediate-length-cascade-decays-abstract/125533/",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","2026-08-05",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 intermediate-length cascade decays challenging to study at colliders?","Question",{"text":75,"@type":76},"When the new-particle spectrum is compressed and the signal cross section is low, events resemble backgrounds and the available information is not sufficient to rely on simple one-dimensional kinematic features. This makes optimized, information-rich analysis necessary.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What does the study optimize using machine learning?",{"text":80,"@type":76},"It identifies optimal kinematic observables for three tasks: improving discovery prospects, determining particle spin, and measuring masses of the intermediate or new particles in the cascade.",{"name":82,"@type":73,"acceptedAnswer":83},"What key observable is highlighted, and how is its performance characterized?",{"text":84,"@type":76},"The study confirms that the kinematic observable κ4 is highly effective, and it evaluates achievable accuracies for spin determination as well as mass-measurement precision as a function of signal size.","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"]