[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121101-en":3,"doc-seo-121101-105":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},121101,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Interplay of Traditional Methods and Machine Learning Algorithms for Tagging Boosted Objects - Review","Deep learning interest in collider physics is growing, especially for jet classification, anomaly detection, and particle identification. This review examines tagging frameworks for boosted objects at the LHC, with focus on boosted Higgs bosons and top quarks, aiming to connect established jet substructure techniques with state-of-the-art machine learning approaches. The work emphasizes interpretability and motivates hybrid taggers that combine traditional and ML methods to support searches for Standard Model and beyond-Standard-Model phenomena while enabling comparisons between data and theory.","arXiv :2408 .01138v1 [hep-ph] 2 Aug 2024  \nInterplay of Traditional Methods and Machine Learning Algorithms for Tagging Boosted Objects  \nCamellia Bose 1 , Amit Chakraborty2*, Shreecheta Chowdhury3 , Saunak Dutta4  \n1 Center for High Energy Physics, Indian Institute of Science, Bangalore, 560012, India.  \n2,3* Department of Physics, SRM University AP, Amaravati, 522240, India.  \n4 School of Science & Technology, Vijaybhoomi University, Greater Mumbai, 410201, India.  \n*Corresponding author(s). E-mail(s): [amit.c@srmap.edu.in](amit.c@srmap.edu.in) ;  \nAbstract  \nInterest in deep learning in collider physics has been growing in recent years, specifically in applying these methods in jet classification, anomaly detection, particle identification etc. Among those, jet classification using neural networks is one of the well-established areas. In this review, we discuss different tagging frameworks available to tag boosted objects, especially boosted Higgs boson and top quark, at the Large Hadron Collider (LHC) . Our aim is to study the interplay of traditional jet substructure based methods with the state-of-the-art machine learning ones. In this methodology, we would gain some interpretability of those machine learning methods, and which in turn helps to propose hybrid taggers relevant for tagging of those boosted objects belonging to both Standard Model (SM) and physics beyond the SM.  \n1 Introduction  \nDespite the commendable triumph of the Standard Model (SM) in unifying the fundamental interactions [1– 4] leading to the discovery of the Higgs boson at the Large Hadron Collider (LHC) experiment [5, 6], several observational discrepancies cumulated from different experiments and theoretical inconsistencies encountered within the SM framework indicate itself as a low-energy effective theory of a formalism with higher symmetries [2] . With many theories Beyond the Standard Model (BSM) proposed, and many more to be formulated, it is a challenging task to figure out which of these theories explains the nature. Observations made at particle colliders which recreate the early universe with high energy density are presently the only trails to trace back the framework, most favored by the nature.  \nThe BSM probes are, fundamentally, the classification problems: identification of the events that are governed by a BSM framework, among the ones accommodated in SM. Traditionally, the cut-based techniques have been the principal tool for the BSM probes. It identifies regions in the phase space with abundant (model dependent) BSM signatures over the SM backgrounds, and redirects the BSM search in those regions. With robust influx of collision data, constraining the phase space and pushing New Physics to higher energy frontier, implementation of advanced search strategies are inevitable.  \nThe focal point of attention in particle physics is presently the LHC [7] . It offers a distinctive chance to investigate the dynamics of the SM at the TeV scale and to explore potential new physics signatures. A collision event at the LHC encompassing various objects, namely hadrons, leptons (electrons/muons), photons, neutrinos, and also other stable exotic particles associated to BSM physics if any. After reconstructing the leptons and photons using tracker and calorimeter information, the hadrons are clustered into jets which are the collimated sprays of particles resulting from the hadronization of quarks and gluons, along with their  \nradiative effects. These are abundant in high-energy collisions, especially at the hadron colliders like LHC. Boosted objects, on the other hand, which are particles with high momentum relative to their mass, become more prevalent at higher energies. Therefore, analysis of jets and boosted objects at hadronic colliders provide great insights into the laws of nature. Over the recent past, an enormous amount of studies have been performed using the jet substructure method focusing on analyzing the physics with Higgs bosons ","cbCaiap3MgE2ikJ2","https://ap.wps.com/l/cbCaiap3MgE2ikJ2","pdf",2703551,1,35,"English","en",105,"# Introduction\n## Physics motivation for boosted-object tagging\n## Traditional cut-based strategies and jet substructure\n## Machine learning for feature discovery and classification","[{\"question\":\"Why are boosted objects important in collider physics?\",\"answer\":\"Boosted objects occur more frequently at higher energies and carry information about jet structure, enabling studies of Standard Model properties and potential signals of new physics.\"},{\"question\":\"What is the review’s main goal for tagging boosted Higgs bosons and top quarks?\",\"answer\":\"It aims to study the interplay between traditional jet substructure methods and modern machine learning algorithms, highlighting how hybrid approaches can improve boosted-object tagging.\"},{\"question\":\"How does the review connect machine learning with interpretability?\",\"answer\":\"It notes that interpretability of machine learning taggers can be gained, which in turn helps propose hybrid taggers relevant to both Standard Model and beyond-Standard-Model scenarios.\"}]","Interplay of Traditional Methods and Machine Learning Algorithms for Tagging Boosted Objects - 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