Probing dark QCD sector through the Higgs portal with machine learning at the LHC
Abstract The QCD-like dark sector with GeV-scale dark hadrons has the potential to generate new signatures at the Large Hadron Collider (LHC). In this paper, we consider a singlet scalar mediator in the tens of GeV-scale that connects the dark sector and the Standard Model (SM) sector via the Higgs...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2023-08-01
|
| Series: | Journal of High Energy Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/JHEP08(2023)187 |
| _version_ | 1797647503608250368 |
|---|---|
| author | Chih-Ting Lu Huifang Lv Wei Shen Lei Wu Jia Zhang |
| author_facet | Chih-Ting Lu Huifang Lv Wei Shen Lei Wu Jia Zhang |
| author_sort | Chih-Ting Lu |
| collection | DOAJ |
| description | Abstract The QCD-like dark sector with GeV-scale dark hadrons has the potential to generate new signatures at the Large Hadron Collider (LHC). In this paper, we consider a singlet scalar mediator in the tens of GeV-scale that connects the dark sector and the Standard Model (SM) sector via the Higgs portal. We focus on the Higgs-strahlung process, q q ¯ $$ q\overline{q} $$ ′ → W * → WH, to produce a highly boosted Higgs boson. Our scenario predicts two different processes that can generate dark mesons: (1) the cascade decay from the Higgs boson to two light scalar mediators and then to four dark mesons; (2) the Higgs boson decaying to two dark quarks, which then undergo a QCD-like shower and hadronization to produce dark mesons. We apply machine learning techniques, such as Convolutional Neural Network (CNN) and Energy Flow Network (EFN), to the fat-jet structure to distinguish these signal processes from large SM backgrounds. We find that the branching ratio of the Higgs boson to two light scalar mediators can be constrained to be less than about 10% at 14 TeV LHC with L $$ \mathcal{L} $$ = 3000 fb −1. |
| first_indexed | 2024-03-11T15:17:09Z |
| format | Article |
| id | doaj.art-b554d49588904e5cbb1fd449a8a42052 |
| institution | Directory Open Access Journal |
| issn | 1029-8479 |
| language | English |
| last_indexed | 2024-03-11T15:17:09Z |
| publishDate | 2023-08-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of High Energy Physics |
| spelling | doaj.art-b554d49588904e5cbb1fd449a8a420522023-10-29T12:11:22ZengSpringerOpenJournal of High Energy Physics1029-84792023-08-012023814410.1007/JHEP08(2023)187Probing dark QCD sector through the Higgs portal with machine learning at the LHCChih-Ting Lu0Huifang Lv1Wei Shen2Lei Wu3Jia Zhang4Department of Physics and Institute of Theoretical Physics, Nanjing Normal UniversityDepartment of Physics and Institute of Theoretical Physics, Nanjing Normal UniversityCAS Key Laboratory of Theoretical Physics, Institute of Theoretical Physics, Chinese Academy of SciencesDepartment of Physics and Institute of Theoretical Physics, Nanjing Normal UniversityDepartment of Physics and Institute of Theoretical Physics, Nanjing Normal UniversityAbstract The QCD-like dark sector with GeV-scale dark hadrons has the potential to generate new signatures at the Large Hadron Collider (LHC). In this paper, we consider a singlet scalar mediator in the tens of GeV-scale that connects the dark sector and the Standard Model (SM) sector via the Higgs portal. We focus on the Higgs-strahlung process, q q ¯ $$ q\overline{q} $$ ′ → W * → WH, to produce a highly boosted Higgs boson. Our scenario predicts two different processes that can generate dark mesons: (1) the cascade decay from the Higgs boson to two light scalar mediators and then to four dark mesons; (2) the Higgs boson decaying to two dark quarks, which then undergo a QCD-like shower and hadronization to produce dark mesons. We apply machine learning techniques, such as Convolutional Neural Network (CNN) and Energy Flow Network (EFN), to the fat-jet structure to distinguish these signal processes from large SM backgrounds. We find that the branching ratio of the Higgs boson to two light scalar mediators can be constrained to be less than about 10% at 14 TeV LHC with L $$ \mathcal{L} $$ = 3000 fb −1.https://doi.org/10.1007/JHEP08(2023)187Specific BSM PhenomenologyCompositenessModels for Dark MatterDark Matter at Colliders |
| spellingShingle | Chih-Ting Lu Huifang Lv Wei Shen Lei Wu Jia Zhang Probing dark QCD sector through the Higgs portal with machine learning at the LHC Journal of High Energy Physics Specific BSM Phenomenology Compositeness Models for Dark Matter Dark Matter at Colliders |
| title | Probing dark QCD sector through the Higgs portal with machine learning at the LHC |
| title_full | Probing dark QCD sector through the Higgs portal with machine learning at the LHC |
| title_fullStr | Probing dark QCD sector through the Higgs portal with machine learning at the LHC |
| title_full_unstemmed | Probing dark QCD sector through the Higgs portal with machine learning at the LHC |
| title_short | Probing dark QCD sector through the Higgs portal with machine learning at the LHC |
| title_sort | probing dark qcd sector through the higgs portal with machine learning at the lhc |
| topic | Specific BSM Phenomenology Compositeness Models for Dark Matter Dark Matter at Colliders |
| url | https://doi.org/10.1007/JHEP08(2023)187 |
| work_keys_str_mv | AT chihtinglu probingdarkqcdsectorthroughthehiggsportalwithmachinelearningatthelhc AT huifanglv probingdarkqcdsectorthroughthehiggsportalwithmachinelearningatthelhc AT weishen probingdarkqcdsectorthroughthehiggsportalwithmachinelearningatthelhc AT leiwu probingdarkqcdsectorthroughthehiggsportalwithmachinelearningatthelhc AT jiazhang probingdarkqcdsectorthroughthehiggsportalwithmachinelearningatthelhc |