| dc.contributor |
Van Kooten, Rick |
|
| dc.creator |
Forland, Blake |
|
| dc.date |
2022-01-19T02:41:10Z |
|
| dc.date |
2022-01-19T02:41:10Z |
|
| dc.date |
2021-12 |
|
| dc.date.accessioned |
2023-02-24T18:26:53Z |
|
| dc.date.available |
2023-02-24T18:26:53Z |
|
| dc.identifier |
https://hdl.handle.net/2022/27061 |
|
| dc.identifier.uri |
http://localhost:8080/xmlui/handle/CUHPOERS/260313 |
|
| dc.description |
Thesis (Ph.D.) - Indiana University, Department of Physics, 2021 |
|
| dc.description |
In this work two distinct yet intimately related efforts are presented. First, the development of a jet tagger consisting of a hybrid, parameterized convolutional neural network optimized for discriminating boosted, four prong jets against a majority QCD background. Uncertainties in tagging four prong jet are estimated, calibrating the tagger for use on such objects in data for the first time in ATLAS. Second, the aforementioned tagger is used as a core component in an analysis to set 95% CL limits for resonant di-Higgs production into the fully hadronic $bbVV$ final state. This analysis is reinterpreted under the $X → SH$ model into the same final state. |
|
| dc.language |
en |
|
| dc.publisher |
[Bloomington, Ind.] : Indiana University |
|
| dc.rights |
This work is under a CC-BY license. You are free to copy and redistribute the material in any format, as well as remix, transform, and build upon the material as long as you give appropriate credit to the original creator, provide a link to the license, and indicate any changes made. |
|
| dc.rights |
https://creativecommons.org/licenses/by/4.0/ |
|
| dc.subject |
bsm physics |
|
| dc.subject |
machine learning |
|
| dc.subject |
standard model |
|
| dc.subject |
di-higgs |
|
| dc.subject |
atlas |
|
| dc.subject |
cern |
|
| dc.title |
INAUGURAL ATLAS SEARCHES FOR RESONANT DI-HIGGS AND $SH$ SIGNALS IN THE BOOSTED, FULLY-HADRONIC $b\overline{b}V V^{∗}$ FINAL STATE AT ATLAS USING $\sqrt{S} = 13$ TEV DATA AND NOVEL MACHINE LEARNING TECHNIQUES |
|
| dc.type |
Doctoral Dissertation |
|