Exploring enhanced non-resonant di-Higgs production at the HL-LHC with neural networks
Leandro Da Rold, Manuel Epele, Anibal D. Medina, Nicol\'as I. Mileo, Alejandro Szynkman

TL;DR
This paper explores using deep neural networks to enhance the detection of non-resonant di-Higgs production at the HL-LHC, focusing on scenarios with new colored scalars and employing advanced classification techniques.
Contribution
It introduces a neural network-based analysis with dedicated classifiers and high-level features to improve di-Higgs signal discrimination in new physics scenarios.
Findings
Achieves a significance of 7.3 for BM_L at 3 ab$^{-1}$
Discriminates signal from backgrounds more effectively with high-level features
Potential for discovery of non-resonant di-Higgs production at HL-LHC
Abstract
We investigate di-Higgs production in the 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 and BM, 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…
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Taxonomy
TopicsParticle physics theoretical and experimental studies · Computational Physics and Python Applications · Particle Detector Development and Performance
