Machine-Learning Performance on Higgs-Pair Production Associated with Dark Matter at the LHC
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909399115628544 |
|---|---|
| author | Arganda, Ernesto Epele, Manuel Mileo, Nicolas I. Morales, Roberto A. |
| author_facet | Arganda, Ernesto Epele, Manuel Mileo, Nicolas I. Morales, Roberto A. |
| contents | Di-Higgs production at the LHC associated with missing transverse energy is explored in the context of simplified models that generically parameterize a large class of models with heavy scalars and dark matter candidates. Our aim is to figure out the improvement capability of machine-learning tools over traditional cut-based analyses. In particular, boosted decision trees and neural networks are implemented in order to determine the parameter space that can be tested at the LHC demanding four $b$-jets and large missing energy in the final state. We present a performance comparison between both machine-learning algorithms, based on the maximum significance reached, by feeding them with different sets of kinematic features corresponding to the LHC at a center-of-mass energy of 14 TeV. Both algorithms present very similar performances and substantially improve traditional analyses, being sensitive to most of the parameter space considered for a total integrated luminosity of 1 ab$^{-1}$, with significances at the evidence level, and even at the discovery level, depending on the masses of the new heavy scalars. A more conservative approach with systematic uncertainties on the background of 30\% has also been contemplated, again providing very promising significances. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_03178 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine-Learning Performance on Higgs-Pair Production Associated with Dark Matter at the LHC Arganda, Ernesto Epele, Manuel Mileo, Nicolas I. Morales, Roberto A. High Energy Physics - Phenomenology High Energy Physics - Experiment Di-Higgs production at the LHC associated with missing transverse energy is explored in the context of simplified models that generically parameterize a large class of models with heavy scalars and dark matter candidates. Our aim is to figure out the improvement capability of machine-learning tools over traditional cut-based analyses. In particular, boosted decision trees and neural networks are implemented in order to determine the parameter space that can be tested at the LHC demanding four $b$-jets and large missing energy in the final state. We present a performance comparison between both machine-learning algorithms, based on the maximum significance reached, by feeding them with different sets of kinematic features corresponding to the LHC at a center-of-mass energy of 14 TeV. Both algorithms present very similar performances and substantially improve traditional analyses, being sensitive to most of the parameter space considered for a total integrated luminosity of 1 ab$^{-1}$, with significances at the evidence level, and even at the discovery level, depending on the masses of the new heavy scalars. A more conservative approach with systematic uncertainties on the background of 30\% has also been contemplated, again providing very promising significances. |
| title | Machine-Learning Performance on Higgs-Pair Production Associated with Dark Matter at the LHC |
| topic | High Energy Physics - Phenomenology High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2401.03178 |