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Title: Tagging boosted hadronic objects with dynamical grooming

Abstract

We evaluate the phenomenological applicability of the dynamical grooming technique, introduced in [1], to boosted W and top tagging at LHC conditions. An extension of our method intended for multi-prong decays with an internal mass scale, such as the top quark decay, is presented. First, we tackle the reconstruction of the mass distribution of W and top jets quantifying the smearing due to pileup. When compared to state-of-the-art grooming algorithms like SoftDrop and its recursive version, dynamical grooming shows an enhanced resilience to background fluctuations. In addition, we asses the discriminating power of dynamical grooming to distinguish W (top) jets from QCD ones by performing a two-step analysis: introduce a cut on the groomed mass around the W (top) mass peak followed by a restriction on the N-subjettinnes ratio τ21 (τ32). For W jets, the out-of-the-box version of dynamical grooming, free of ad-hoc parameters, results into a comparable performance to SoftDrop. Regarding the top tagger efficiency, 3-prong dynamical grooming, in spite of its simplicity, presents better performance than SoftDrop and similar results to Recursive SoftDrop.

Authors:
; ; ORCiD logo
Publication Date:
Research Org.:
Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Nuclear Physics (NP); Trond Mohn Foundation
OSTI Identifier:
1734422
Alternate Identifier(s):
OSTI ID: 1763350
Report Number(s):
BNL-220906-2021-JAAM
Journal ID: ISSN 2470-0010; PRVDAQ; 114013
Grant/Contract Number:  
SC0012704; BFS2018REK01
Resource Type:
Published Article
Journal Name:
Physical Review D
Additional Journal Information:
Journal Name: Physical Review D Journal Volume: 102 Journal Issue: 11; Journal ID: ISSN 2470-0010
Publisher:
American Physical Society
Country of Publication:
United States
Language:
English
Subject:
73 NUCLEAR PHYSICS AND RADIATION PHYSICS; Perturbative QCD; quark & gluon jets; top quark; W & Z bosons

Citation Formats

Mehtar-Tani, Yacine, Soto-Ontoso, Alba, and Tywoniuk, Konrad. Tagging boosted hadronic objects with dynamical grooming. United States: N. p., 2020. Web. doi:10.1103/PhysRevD.102.114013.
Mehtar-Tani, Yacine, Soto-Ontoso, Alba, & Tywoniuk, Konrad. Tagging boosted hadronic objects with dynamical grooming. United States. https://doi.org/10.1103/PhysRevD.102.114013
Mehtar-Tani, Yacine, Soto-Ontoso, Alba, and Tywoniuk, Konrad. Mon . "Tagging boosted hadronic objects with dynamical grooming". United States. https://doi.org/10.1103/PhysRevD.102.114013.
@article{osti_1734422,
title = {Tagging boosted hadronic objects with dynamical grooming},
author = {Mehtar-Tani, Yacine and Soto-Ontoso, Alba and Tywoniuk, Konrad},
abstractNote = {We evaluate the phenomenological applicability of the dynamical grooming technique, introduced in [1], to boosted W and top tagging at LHC conditions. An extension of our method intended for multi-prong decays with an internal mass scale, such as the top quark decay, is presented. First, we tackle the reconstruction of the mass distribution of W and top jets quantifying the smearing due to pileup. When compared to state-of-the-art grooming algorithms like SoftDrop and its recursive version, dynamical grooming shows an enhanced resilience to background fluctuations. In addition, we asses the discriminating power of dynamical grooming to distinguish W (top) jets from QCD ones by performing a two-step analysis: introduce a cut on the groomed mass around the W (top) mass peak followed by a restriction on the N-subjettinnes ratio τ21 (τ32). For W jets, the out-of-the-box version of dynamical grooming, free of ad-hoc parameters, results into a comparable performance to SoftDrop. Regarding the top tagger efficiency, 3-prong dynamical grooming, in spite of its simplicity, presents better performance than SoftDrop and similar results to Recursive SoftDrop.},
doi = {10.1103/PhysRevD.102.114013},
journal = {Physical Review D},
number = 11,
volume = 102,
place = {United States},
year = {2020},
month = {12}
}

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