Journal
JOURNAL OF HIGH ENERGY PHYSICS
Volume -, Issue 4, Pages -Publisher
SPRINGER
DOI: 10.1007/JHEP04(2021)139
Keywords
Higgs Physics; Beyond Standard Model
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Analysis of kinematic shapes can help separate production modes sensitive to the bottom-quark Yukawa coupling from background processes, providing insights into the size and possibly the phase of y(b). By making machine learning algorithms interpretable, we can selectively focus on the most important kinematic variables in the analysis.
The associated production of a bb (b) over bar pair with a Higgs boson could provide an important probe to both the size and the phase of the bottom-quark Yukawa coupling, y(b). However, the signal is shrouded by several background processes including the irreducible Zh, Z -> b (b) over bar background. We show that the analysis of kinematic shapes provides us with a concrete prescription for separating the y(b)-sensitive production modes from both the irreducible and the QCD-QED backgrounds using the b (b) over bar gamma gamma final state. We draw a page from game theory and use Shapley values to make Boosted Decision Trees interpretable in terms of kinematic measurables and provide physics insights into the variances in the kinematic shapes of the different channels that help us complete this feat. Adding interpretability to the machine learning algorithm opens up the black-box and allows us to cherry-pick only those kinematic variables that matter most in the analysis. We resurrect the hope of constraining the size and, possibly, the phase of y(b) using kinematic shape studies of b (b) over barh production with the full HL-LHC data and at FCC-hh.
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