Deep Learning to Improve the Sensitivity of Di-Higgs Searches in the 4b Channel
arxiv(2024)
摘要
The study of di-Higgs events, both resonant and non-resonant, plays a crucial
role in understanding the fundamental interactions of the Higgs boson. In this
work we consider di-Higgs events decaying into four b-quarks and propose to
improve the experimental sensitivity by utilizing a novel machine learning
algorithm known as Symmetry Preserving Attention Network (Spa-Net) –
a neural network structure whose architecture is designed to incorporate the
inherent symmetries in particle reconstruction tasks. We demonstrate that the
Spa-Net can enhance the experimental reach over baseline methods such
as the cut-based and the Deep Neural Networks (DNN)-based analyses. At the
Large Hadron Collider, with a 14-TeV centre-of-mass energy and an integrated
luminosity of 300 fb^-1, the Spa-Net allows us to establish 95%
C.L. upper limits in resonant production cross-sections that are 10% to 45%
stronger than baseline methods. For non-resonant di-Higgs production,
Spa-Net enables us to constrain the self-coupling that is 9% more
stringent than the baseline method.
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