A bstract Measuring di-Higgs production in the four-bottom channel is challenged by overwhelming QCD backgrounds and imperfect simulations. We develop a Bayesian mixture model that simultaneously infers signal and background fractions and their individual shapes directly in the signal region. The likelihood is a nuanced combination of a one-dimensional kinematic discriminator and per-jet flavour scores; with their correlations incorporated via kinematic bins. Monte Carlo informs weak Dirichlet priors, while the posterior adjusts to the interplay of the model, priors and observed data. Using pseudo-data simulated with standard tools and with controlled mismatches, we show that the method corrects biased priors, delivers 68–95% credible intervals for the signal count, and improves dataset-level ROC/AUC relative to simple cut-and-count baselines. This study highlights how Bayesian inference can harvest information present in the signal region and self-calibrate model parameters, providing a robust route to increased sensitivity in di-Higgs searches.
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Álvarez et al. (Tue,) studied this question.
www.synapsesocial.com/papers/69fd7e90bfa21ec5bbf06cb6 — DOI: https://doi.org/10.1007/jhep05(2026)032
Ezequiel Álvarez
Leandro Da Rold
Manuel Szewc
Journal of High Energy Physics
Universidad Nacional de La Plata
National University of General San Martín
Balseiro Institute
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