Abstract
Searches for pairs of Higgs bosons are the best tools to precisely measure the Higgs boson self-coupling [Formula: see text] in future colliders. We study various strategies for the [Formula: see text] search in the HL-LHC era with focus on constraining [Formula: see text]. We implement a machine-learning-based approach to separate signal and background and apply recent advances in machine learning interpretability, compare the traditional 4 b-jet reconstruction to final states with 1 or 2 large-radius jets, and test scenarios with different top-quark Yukawa couplings, among other factors. This talk was presented as part of the Mini-workshop on Machine Learning at the 10th International Conference on New Frontiers in Physics (ICNFP-2021). a
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