{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/65536"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/65536","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Identifying boosted objects with N-subjettiness and linear k-means clustering","abstract":"In this thesis, I explore aspects of a new jet shape - N-subjettiness - designed to identify boosted hadronically-decaying objects (with a particular focus on tagging top quarks) at particle accelerators such as the Large Hadron Collider. Combined with an invariant mass cut on jets, N-subjettiness is a powerful discriminating variable for tagging boosted objects such as top quarks and rejecting the fake background of QCD jets with large invariant mass. In a crossover analysis, the N-subjettiness method is found to outperform the common top tagging methods of the BOOST2010 conference, with top tagging efficiencies of 50% and 20% against mistag rates of 4.0% and 0.19%, respectively. The N-subjettiness values are calculated using a new infrared- and collinear-safe minimization procedure which I call the linear k-means clustering algorithm. As a true jet shape with highly effective tagging performances, N-subjettiness has many advantages on the experimental as well as on the theoretical side.","abstract_html":"In this thesis, I explore aspects of a new jet shape - N-subjettiness - designed to identify boosted hadronically-decaying objects (with a particular focus on tagging top quarks) at particle accelerators such as the Large Hadron Collider. Combined with an invariant mass cut on jets, N-subjettiness is a powerful discriminating variable for tagging boosted objects such as top quarks and rejecting the fake background of QCD jets with large invariant mass. In a crossover analysis, the N-subjettiness method is found to outperform the common top tagging methods of the BOOST2010 conference, with top tagging efficiencies of 50% and 20% against mistag rates of 4.0% and 0.19%, respectively. The N-subjettiness values are calculated using a new infrared- and collinear-safe minimization procedure which I call the linear k-means clustering algorithm. As a true jet shape with highly effective tagging performances, N-subjettiness has many advantages on the experimental as well as on the theoretical side.","abstract_has_math":false,"creators":["Van Tilburg, Ken"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Dept. of Physics.","school":null,"contributors":[],"advisors":["Jesse Thaler."],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011","date_published":"2011","updated_at":"2026-07-22T22:21:38Z","subjects":["Physics.","Mathematics."],"languages":["eng"],"rights":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 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