In the realm of high-energy physics, a team of researchers from the Universidad Nacional de La Plata in Argentina has been delving into the intricacies of di-Higgs production, a process that could potentially unlock new avenues in our understanding of particle physics and, by extension, the energy sector. The team, comprising Leandro Da Rold, Manuel Epele, Anibal D. Medina, Nicolás I. Mileo, and Alejandro Szynkman, has been exploring this phenomenon at the High-Luminosity Large Hadron Collider (HL-LHC), with their findings recently published in the Journal of High Energy Physics.
The researchers focused on the di-Higgs production in the $b\bar{b}γγ$ final state, a process that could be enhanced by new colored scalars, which might be identified as squarks or leptoquarks. These particles are hypothetical and could potentially play a role in future energy technologies, particularly in advanced nuclear processes or particle accelerators. The team considered two benchmarks characterized by the mass of the lightest colored scalar, BM$_{\mathrm{L}}$ and BM$_{\mathrm{H}}$, corresponding to 464 GeV and 621 GeV, respectively.
Using Monte Carlo simulations for both the signal and the dominant backgrounds, the researchers performed a discovery analysis with deep neural networks. They explored various architectures and input variables, finding that the discrimination power was maximized by employing two dedicated classifiers. One classifier was trained against QCD backgrounds, while the other was trained against backgrounds involving single-Higgs processes. This dual-classifier approach could potentially improve the efficiency of particle identification and background noise reduction in future energy technologies.
The team also demonstrated that including high-level features, such as the invariant masses $m_{γγ}$, $m_{bb}$, and $m_{hh}$, as well as the transverse momenta and angular separations of the photon and $b$-jet pairs, significantly improved the performance compared to using only low-level features. For the latter case, they found that architectures processing photon and $b$-jet variables separately could enhance the significance for BM$_{\mathrm{H}}$.
Projecting for an integrated luminosity of 3 ab$^{-1}$, the researchers obtained a significance of 7.3 for BM$_{\mathrm{L}}$, while it dropped to 3.1 for BM$_{\mathrm{H}}$. In the particular case of BM$_{\mathrm{L}}$, discovery level significance could be reached at 1.7 ab$^{-1}$. These findings could have implications for the energy sector, particularly in the development of advanced nuclear technologies and particle accelerators, by improving our understanding of particle interactions and enhancing the efficiency of energy production processes.
In summary, the research conducted by the team from the Universidad Nacional de La Plata sheds light on the potential of di-Higgs production in the $b\bar{b}γγ$ final state, with significant implications for the energy sector. By leveraging deep neural networks and advanced simulation techniques, the researchers have paved the way for more efficient particle identification and background noise reduction, potentially leading to advancements in energy technologies.
This article is based on research available at arXiv.