Exploring enhanced non-resonant di-Higgs production at the HL-LHC with neural networks

Abstract

We investigate di-Higgs production in the $b\bar{b}\gamma\gamma$ final state at the LHC, focusing on scenarios where the gluon fusion process is enhanced by new colored scalars, which could be identified as squarks or leptoquarks. We consider 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, we perform a discovery analysis with deep neural networks, exploring various architectures and input variables. Our results show that the discrimination power is maximized by employing two dedicated classifiers, one trained against QCD backgrounds and another against backgrounds involving single-Higgs processes. Furthermore, we demonstrate that including high-level features -- such as the invariant masses $m_{\gamma\gamma}$, $m_{bb}$, and $m_{hh}$, as well as the transverse momenta and angular separations of the photon and $b$-jet pairs -- significantly improves the performance compared to using only low-level features as the invariant mass and momenta of the final particles. For the latter case, we find that architectures processing photon and $b$-jet variables separately can enhance the significance for BM$_{\mathrm{H}}$. Projecting for an integrated luminosity of 3 ab$^{-1}$, we obtain a significance of 7.3 for BM$_{\mathrm{L}}$, while it drops to 3.1 for BM$_{\mathrm{H}}$. In the particular case of BM$_{\mathrm{L}}$, discovery level significance can be reached at 1.7 ab$^{-1}$.