A New Deep-Neural-Network--Based Missing Transverse Momentum Estimator, and its Application to W Recoil
| dc.contributor.advisor | Belloni, Alberto | en_US |
| dc.contributor.author | Feng, Yongbin | en_US |
| dc.contributor.department | Physics | en_US |
| dc.contributor.publisher | Digital Repository at the University of Maryland | en_US |
| dc.contributor.publisher | University of Maryland (College Park, Md.) | en_US |
| dc.date.accessioned | 2021-02-14T06:39:04Z | |
| dc.date.available | 2021-02-14T06:39:04Z | |
| dc.date.issued | 2020 | en_US |
| dc.description.abstract | This dissertation presents the first Deep-Neural-Network–based missing transverse momentum (pTmiss) estimator, called “DeepMET”. It utilizes all reconstructed particles in an event as input, and assigns an individual weight to each of them. The DeepMET estimator is the negative of the vector sum of the weighted transverse momenta of all input particles. Compared with the pTmiss estimators currently utilized by the CMS Collaboration, DeepMET is found to improve the pTmiss resolution by 10-20%, and is more resilient towards the effect of additional proton-proton interactions accompanying the interaction of interest. DeepMET is demonstrated to improve the resolution on the recoil measurement of the W boson and reduce the systematic uncertainties on the W mass measurement by a large fraction compared with other pTmiss estimators. | en_US |
| dc.identifier | https://doi.org/10.13016/e6ze-zycc | |
| dc.identifier.uri | http://hdl.handle.net/1903/26843 | |
| dc.language.iso | en | en_US |
| dc.subject.pqcontrolled | Particle physics | en_US |
| dc.title | A New Deep-Neural-Network--Based Missing Transverse Momentum Estimator, and its Application to W Recoil | en_US |
| dc.type | Dissertation | en_US |
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