Carnevali, Francesco (2024) Search for Vector-Like Quark with Machine Learning techniques at the CMS experiment. [Tesi di dottorato]
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| Item Type: | Tesi di dottorato |
|---|---|
| Resource language: | English |
| Title: | Search for Vector-Like Quark with Machine Learning techniques at the CMS experiment |
| Creators: | Creators Email Carnevali, Francesco francesco.carnevali@unina.it |
| Date: | 10 March 2024 |
| Number of Pages: | 120 |
| Institution: | Università degli Studi di Napoli Federico II |
| Department: | Fisica |
| Dottorato: | Fisica |
| Ciclo di dottorato: | 36 |
| Coordinatore del Corso di dottorato: | nome email Canale, Vincenzo vincenzo.canale@unina.it |
| Tutor: | nome email Iorio, Alberto Orso Maria UNSPECIFIED |
| Date: | 10 March 2024 |
| Number of Pages: | 120 |
| Keywords: | Vector-Like Quarks, Machine Learning, CMS |
| Settori scientifico-disciplinari del MIUR: | Area 02 - Scienze fisiche > FIS/04 - Fisica nucleare e subnucleare |
| Date Deposited: | 19 Mar 2024 10:32 |
| Last Modified: | 18 Mar 2026 08:49 |
| URI: | http://www.fedoa.unina.it/id/eprint/15472 |
Collection description
This thesis presents a search for singly produced Vector-Like Quark T decaying to a top quark and a Higgs boson or a new boson A, using data from the CMS experiment at the LHC with centre-of-mass energy of 13 TeV and an integrated luminosity of 138 fb⁻¹. New identification criteria for leptonically decaying top quarks are developed using Machine Learning. The T candidate is reconstructed using the top quark candidate and the H/A-tagged jet 4-momenta, utilizing the resulting mass as a discriminating variable to identify potential signals. Upper limits on production cross sections are estimated at 95% CL, demonstrating good sensitivity above 1.2 TeV for the T->tH decay mode and providing the first expected cross section upper limits for the T->tA channel as a function of VLQ T and boson A masses.
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