Determination of the polarization fractions in using a deep machine learning technique
Abstract
The unitarization of the longitudinal vector boson scattering (VBS) cross section by the Higgs boson is a fundamental prediction of the Standard Model which has not been experimentally verified. One of the most promising ways to measure VBS uses events containing two leptonically decaying same-electric-charge W bosons produced in association with two jets. However, the angular distributions of the leptons in the W boson rest frame, which are commonly used to fit polarization fractions, are not readily available in this process due to the presence of two neutrinos in the final state. In this paper we present a method to alleviate this problem by using a deep machine learning technique to recover these angular distributions from measurable event kinematics and demonstrate how the longitudinal-longitudinal scattering fraction could be studied. Furthermore, we show that this method doubles the expected sensitivity when compared to previous proposals.
- Authors:
-
- Univ. of Michigan, Ann Arbor, MI (United States)
- Brookhaven National Lab. (BNL), Upton, NY (United States)
- Publication Date:
- Research Org.:
- Brookhaven National Lab. (BNL), Upton, NY (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), High Energy Physics (HEP)
- OSTI Identifier:
- 1335491
- Alternate Identifier(s):
- OSTI ID: 1254679
- Report Number(s):
- BNL-112187-2016-JA
Journal ID: ISSN 2470-0010; PRVDAQ
- Grant/Contract Number:
- SC00112704; SC0008062; SC0012704
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Physical Review D
- Additional Journal Information:
- Journal Volume: 93; Journal Issue: 9; Journal ID: ISSN 2470-0010
- Publisher:
- American Physical Society (APS)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS
Citation Formats
Searcy, Jacob, Huang, Lillian, Pleier, Marc -Andre, and Zhu, Junjie. Determination of the WW polarization fractions in pp→W±W±jj using a deep machine learning technique. United States: N. p., 2016.
Web. doi:10.1103/PhysRevD.93.094033.
Searcy, Jacob, Huang, Lillian, Pleier, Marc -Andre, & Zhu, Junjie. Determination of the WW polarization fractions in pp→W±W±jj using a deep machine learning technique. United States. https://doi.org/10.1103/PhysRevD.93.094033
Searcy, Jacob, Huang, Lillian, Pleier, Marc -Andre, and Zhu, Junjie. Fri .
"Determination of the WW polarization fractions in pp→W±W±jj using a deep machine learning technique". United States. https://doi.org/10.1103/PhysRevD.93.094033. https://www.osti.gov/servlets/purl/1335491.
@article{osti_1335491,
title = {Determination of the WW polarization fractions in pp→W±W±jj using a deep machine learning technique},
author = {Searcy, Jacob and Huang, Lillian and Pleier, Marc -Andre and Zhu, Junjie},
abstractNote = {The unitarization of the longitudinal vector boson scattering (VBS) cross section by the Higgs boson is a fundamental prediction of the Standard Model which has not been experimentally verified. One of the most promising ways to measure VBS uses events containing two leptonically decaying same-electric-charge W bosons produced in association with two jets. However, the angular distributions of the leptons in the W boson rest frame, which are commonly used to fit polarization fractions, are not readily available in this process due to the presence of two neutrinos in the final state. In this paper we present a method to alleviate this problem by using a deep machine learning technique to recover these angular distributions from measurable event kinematics and demonstrate how the longitudinal-longitudinal scattering fraction could be studied. Furthermore, we show that this method doubles the expected sensitivity when compared to previous proposals.},
doi = {10.1103/PhysRevD.93.094033},
journal = {Physical Review D},
number = 9,
volume = 93,
place = {United States},
year = {Fri May 27 00:00:00 EDT 2016},
month = {Fri May 27 00:00:00 EDT 2016}
}
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