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Title: Mass agnostic jet taggers

Abstract

Searching for new physics in large data sets needs a balance between two competing effects—signal identification vs background distortion. In this work, we perform a systematic study of both single variable and multivariate jet tagging methods that aim for this balance. The methods preserve the shape of the background distribution by either augmenting the training procedure or the data itself. Multiple quantitative metrics to compare the methods are considered, for tagging 2-, 3-, or 4-prong jets from the QCD background. This is the first study to show that the data augmentation techniques of Planing and PCA based scaling deliver similar performance as the augmented training techniques of Adversarial NN and uBoost, but are both easier to implement and computationally cheaper.

Authors:
 [1];  [2];  [2];  [1]
  1. University of Oregon
  2. National Institute for Nuclear Physics, Scuola Normale Superiore di Pisa
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1594194
Grant/Contract Number:  
SC0011640
Resource Type:
Published Article
Journal Name:
SciPost Physics Proceedings
Additional Journal Information:
Journal Name: SciPost Physics Proceedings Journal Volume: 8 Journal Issue: 1; Journal ID: ISSN 2542-4653
Publisher:
Stichting SciPost
Country of Publication:
Netherlands
Language:
English

Citation Formats

Bradshaw, Layne, Mishra, Rashmish K., Mitridate, Andrea, and Ostdiek, Bryan. Mass agnostic jet taggers. Netherlands: N. p., 2020. Web. doi:10.21468/SciPostPhys.8.1.011.
Bradshaw, Layne, Mishra, Rashmish K., Mitridate, Andrea, & Ostdiek, Bryan. Mass agnostic jet taggers. Netherlands. doi:10.21468/SciPostPhys.8.1.011.
Bradshaw, Layne, Mishra, Rashmish K., Mitridate, Andrea, and Ostdiek, Bryan. Fri . "Mass agnostic jet taggers". Netherlands. doi:10.21468/SciPostPhys.8.1.011.
@article{osti_1594194,
title = {Mass agnostic jet taggers},
author = {Bradshaw, Layne and Mishra, Rashmish K. and Mitridate, Andrea and Ostdiek, Bryan},
abstractNote = {Searching for new physics in large data sets needs a balance between two competing effects—signal identification vs background distortion. In this work, we perform a systematic study of both single variable and multivariate jet tagging methods that aim for this balance. The methods preserve the shape of the background distribution by either augmenting the training procedure or the data itself. Multiple quantitative metrics to compare the methods are considered, for tagging 2-, 3-, or 4-prong jets from the QCD background. This is the first study to show that the data augmentation techniques of Planing and PCA based scaling deliver similar performance as the augmented training techniques of Adversarial NN and uBoost, but are both easier to implement and computationally cheaper.},
doi = {10.21468/SciPostPhys.8.1.011},
journal = {SciPost Physics Proceedings},
number = 1,
volume = 8,
place = {Netherlands},
year = {2020},
month = {1}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.21468/SciPostPhys.8.1.011

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