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Title: LeptonJet Categorization with Decision Tree

Abstract

With much evidence for dark matter arising from astronomical observations, particle physics has also begun to look for dark matter candidates. Here, a boosted decision tree machine learning algorithm (BDT) is used to distinguish signal from background in a search for self-interacting dark matter at CMS with two different final states of signal. The signal final states are two displaced collimated pairs of leptons (leptonJets). In this project, possible improvements to the BDT are explored. Various methods of signal separation are studied, as well as various accuracy metrics to study and compare the different models. Through the separated models and improvements in training, a slight increase in accuracy score was obtained (0.01% on electrons, 0.46% on muons). Additionally, the separated models were able to recognize a significantly increased percentage of signal, particularly against the electron signal. Separated models are being considered as part of the larger an alysis.

Authors:
 [1]
  1. Chicago U.
Publication Date:
Research Org.:
Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
Contributing Org.:
CMS
OSTI Identifier:
1565937
Report Number(s):
FERMILAB-POSTER-19-118-CMS
oai:inspirehep.net:1754619
DOE Contract Number:  
AC02-07CH11359
Resource Type:
Conference
Country of Publication:
United States
Language:
English
Subject:
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS

Citation Formats

Seddon-Stettler, Sadie. LeptonJet Categorization with Decision Tree. United States: N. p., 2019. Web.
Seddon-Stettler, Sadie. LeptonJet Categorization with Decision Tree. United States.
Seddon-Stettler, Sadie. Tue . "LeptonJet Categorization with Decision Tree". United States. https://www.osti.gov/servlets/purl/1565937.
@article{osti_1565937,
title = {LeptonJet Categorization with Decision Tree},
author = {Seddon-Stettler, Sadie},
abstractNote = {With much evidence for dark matter arising from astronomical observations, particle physics has also begun to look for dark matter candidates. Here, a boosted decision tree machine learning algorithm (BDT) is used to distinguish signal from background in a search for self-interacting dark matter at CMS with two different final states of signal. The signal final states are two displaced collimated pairs of leptons (leptonJets). In this project, possible improvements to the BDT are explored. Various methods of signal separation are studied, as well as various accuracy metrics to study and compare the different models. Through the separated models and improvements in training, a slight increase in accuracy score was obtained (0.01% on electrons, 0.46% on muons). Additionally, the separated models were able to recognize a significantly increased percentage of signal, particularly against the electron signal. Separated models are being considered as part of the larger an alysis.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {1}
}

Conference:
Other availability
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