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We study the problem of data-driven background estimation, arising in thesearch of physics signals predicted by the Standard Model at the Large HadronCollider. Our work is motivated by the search for the production of pairs ofHiggs bosons decaying into four bottom quarks. A number of other physicalprocesses, known as background, also share the same final state. The dataarising in this problem is therefore a mixture of unlabeled background andsignal events, and the primary aim of the analysis is to determine whether theproportion of unlabeled signal events is nonzero. A challenging but necessaryfirst step is to estimate the distribution of background events. Past work inthis area has determined regions of the space of collider events where signalis unlikely to appear, and where the background distribution is thereforeidentifiable. The background distribution can be estimated in these regions,and extrapolated into the region of primary interest using transfer learningwith a multivariate classifier. We build upon this existing approach in twoways. First, we revisit this method by developing a powerful new classifierarchitecture tailored to collider data. Second, we develop a new method forbackground estimation, based on the optimal transport problem, which relies onmodeling assumptions distinct from earlier work. These two methods can serve ascross-checks for each other in particle physics analyses, due to thecomplementarity of their underlying assumptions. We compare their performanceon simulated double Higgs boson data.

Background Modeling for Double Higgs Boson Production: Density Ratios and Optimal Transport