Study of enhancing the signal-background separation for the Higgs boson production in association with a single top-quark in pp collisions at the LHC
Abstract
In this paper, a multivariate analysis based approach for enhancing the extraction of the rare Standard Model (SM) process being studied - Higgs boson production in association with a single top quark (tH) - from the prevailing background processes is presented. The analysis was performed using a Monte Carlo generated dataset simulating the outcomes of proton-proton collisions at the Large Hadron Collider (LHC). A deep fully connected neural network was developed for signal-background discrimination, incorporating a masking mechanism designed to suppress the contribution of input variables associated with objects that were not reconstructed in a given event. The performance of the masked architecture was evaluated on independent Monte Carlo samples. The results of the proposed methodology are compared with a conventional cut-based approach in terms of the statistical significance of the tH production channel. The developed framework was tested using a simulated dataset corresponding approximately to the combined statistical samples of LHC Runs 1, 2, and 3.
Related
- Measurements of isolated photon pair production in association with jets in pp collisions at √s = 8 TeV with the ATLAS detector
- Measurement of the cross-section for the production of a W boson in association with b-jets in pp collisions at √s=13 TeV with the ATLAS detector
- Convolutional non-parametric Gamma-Ray Signal and Background Separation
- Charge separation measurements in Au+Au collisions at = 7.7−200 GeV in search of the chiral magnetic effect*
- Constraint on the Higgs boson width in the diphoton final state using signal-background interference in proton-proton collisions at √s = 13 TeV
BibTeX
@misc{azimzade2026study,
title = {{Study of enhancing the signal-background separation for the Higgs boson production in association with a single top-quark in $pp$ collisions at the LHC}},
author = {Rustam Azimzade and Alina Dadashova and Igor Boiko and Vladimir Lyubushkin and Nazim Huseynov},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.04802},
eprint = {2611.04802},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.04802},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Study of enhancing the signal-background separation for the Higgs boson production in association with a single top-quark in pp collisions at the LHC
AU - Rustam Azimzade
AU - Alina Dadashova
AU - Igor Boiko
AU - Vladimir Lyubushkin
AU - Nazim Huseynov
PY - 2026
DA - 2026/11/03
PB - arXiv
DO - 10.48550/arxiv.2611.04802
UR - https://arxiv.org/abs/2611.04802
AB - In this paper, a multivariate analysis based approach for enhancing the extraction of the rare Standard Model (SM) process being studied - Higgs boson production in association with a single top quark (tH) - from the prevailing background processes is presented. The analysis was performed using a Monte Carlo generated dataset simulating the outcomes of proton-proton collisions at the Large Hadron Collider (LHC). A deep fully connected neural network was developed for signal-background discrimination, incorporating a masking mechanism designed to suppress the contribution of input variables associated with objects that were not reconstructed in a given event. The performance of the masked architecture was evaluated on independent Monte Carlo samples. The results of the proposed methodology are compared with a conventional cut-based approach in terms of the statistical significance of the tH production channel. The developed framework was tested using a simulated dataset corresponding approximately to the combined statistical samples of LHC Runs 1, 2, and 3.
ER -