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:

Bradshaw, Layne ^[1]; Mishra, Rashmish K. ^[2]; Mitridate, Andrea ^[2]; Ostdiek, Bryan ^[1]

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+ Show Author Affiliations

1. University of Oregon
2. National Institute for Nuclear Physics, Scuola Normale Superiore di Pisa

Publication Date:

2020-01-24

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