Machine-Learning Analysis of Radiative Decays to Dark Matter at the LHC
Abstract The search for weakly interacting matter particles (WIMPs) is one of the main objectives of the High Luminosity Large Hadron Collider (HL-LHC). In this work we use Machine-Learning (ML) techniques to explore WIMP radiative decays into a Dark Matter (DM) candidate in a supersymmetric framewo...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-07-01
|
| Series: | Journal of High Energy Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/JHEP07(2025)014 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849344624359899136 |
|---|---|
| author | Ernesto Arganda Marcela Carena Martín de los Rios Andres D. Perez Duncan Rocha Rosa M. Sandá Seoane Carlos E. M. Wagner |
| author_facet | Ernesto Arganda Marcela Carena Martín de los Rios Andres D. Perez Duncan Rocha Rosa M. Sandá Seoane Carlos E. M. Wagner |
| author_sort | Ernesto Arganda |
| collection | DOAJ |
| description | Abstract The search for weakly interacting matter particles (WIMPs) is one of the main objectives of the High Luminosity Large Hadron Collider (HL-LHC). In this work we use Machine-Learning (ML) techniques to explore WIMP radiative decays into a Dark Matter (DM) candidate in a supersymmetric framework. The minimal supersymmetric WIMP sector includes the lightest neutralino that can provide the observed DM relic density through its co-annihilation with the second lightest neutralino and lightest chargino. Moreover, the direct DM detection cross section rates fulfill current experimental bounds and provide discovery targets for the same region of model parameters in which the radiative decay of the second lightest neutralino into a photon and the lightest neutralino is enhanced. This strongly motivates the search for radiatively decaying neutralinos which, however, suffers from strong backgrounds. We investigate the LHC reach in the search for these radiatively decaying particles by means of cut-based and ML methods and estimate its discovery potential in this well-motivated, new physics scenario. We demonstrate that using ML techniques would enable access to most of the parameter space unexplored by other searches. |
| format | Article |
| id | doaj-art-40f4e276e7b748c69995d314a81d60ab |
| institution | Kabale University |
| issn | 1029-8479 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of High Energy Physics |
| spelling | doaj-art-40f4e276e7b748c69995d314a81d60ab2025-08-20T03:42:37ZengSpringerOpenJournal of High Energy Physics1029-84792025-07-012025713210.1007/JHEP07(2025)014Machine-Learning Analysis of Radiative Decays to Dark Matter at the LHCErnesto Arganda0Marcela Carena1Martín de los Rios2Andres D. Perez3Duncan Rocha4Rosa M. Sandá Seoane5Carlos E. M. Wagner6Departamento de Física Teórica and Instituto de Física Teórica UAM-CSIC, Universidad Autónoma de MadridFermi National Accelerator LaboratoryDepartamento de Física Teórica and Instituto de Física Teórica UAM-CSIC, Universidad Autónoma de MadridDepartamento de Física Teórica and Instituto de Física Teórica UAM-CSIC, Universidad Autónoma de MadridFermi National Accelerator LaboratoryDepartamento de Física Teórica and Instituto de Física Teórica UAM-CSIC, Universidad Autónoma de MadridEnrico Fermi Institute, Physics Department, University of ChicagoAbstract The search for weakly interacting matter particles (WIMPs) is one of the main objectives of the High Luminosity Large Hadron Collider (HL-LHC). In this work we use Machine-Learning (ML) techniques to explore WIMP radiative decays into a Dark Matter (DM) candidate in a supersymmetric framework. The minimal supersymmetric WIMP sector includes the lightest neutralino that can provide the observed DM relic density through its co-annihilation with the second lightest neutralino and lightest chargino. Moreover, the direct DM detection cross section rates fulfill current experimental bounds and provide discovery targets for the same region of model parameters in which the radiative decay of the second lightest neutralino into a photon and the lightest neutralino is enhanced. This strongly motivates the search for radiatively decaying neutralinos which, however, suffers from strong backgrounds. We investigate the LHC reach in the search for these radiatively decaying particles by means of cut-based and ML methods and estimate its discovery potential in this well-motivated, new physics scenario. We demonstrate that using ML techniques would enable access to most of the parameter space unexplored by other searches.https://doi.org/10.1007/JHEP07(2025)014Dark Matter at CollidersSupersymmetry |
| spellingShingle | Ernesto Arganda Marcela Carena Martín de los Rios Andres D. Perez Duncan Rocha Rosa M. Sandá Seoane Carlos E. M. Wagner Machine-Learning Analysis of Radiative Decays to Dark Matter at the LHC Journal of High Energy Physics Dark Matter at Colliders Supersymmetry |
| title | Machine-Learning Analysis of Radiative Decays to Dark Matter at the LHC |
| title_full | Machine-Learning Analysis of Radiative Decays to Dark Matter at the LHC |
| title_fullStr | Machine-Learning Analysis of Radiative Decays to Dark Matter at the LHC |
| title_full_unstemmed | Machine-Learning Analysis of Radiative Decays to Dark Matter at the LHC |
| title_short | Machine-Learning Analysis of Radiative Decays to Dark Matter at the LHC |
| title_sort | machine learning analysis of radiative decays to dark matter at the lhc |
| topic | Dark Matter at Colliders Supersymmetry |
| url | https://doi.org/10.1007/JHEP07(2025)014 |
| work_keys_str_mv | AT ernestoarganda machinelearninganalysisofradiativedecaystodarkmatteratthelhc AT marcelacarena machinelearninganalysisofradiativedecaystodarkmatteratthelhc AT martindelosrios machinelearninganalysisofradiativedecaystodarkmatteratthelhc AT andresdperez machinelearninganalysisofradiativedecaystodarkmatteratthelhc AT duncanrocha machinelearninganalysisofradiativedecaystodarkmatteratthelhc AT rosamsandaseoane machinelearninganalysisofradiativedecaystodarkmatteratthelhc AT carlosemwagner machinelearninganalysisofradiativedecaystodarkmatteratthelhc |