Quasiprobabilistic Density Ratio Estimation with a Reverse Engineered Classification Loss Function

• URL: http://arxiv.org/abs/2512.19913v1
• Date: Mon, 22 Dec 2025 22:37:19 GMT
• Title: Quasiprobabilistic Density Ratio Estimation with a Reverse Engineered Classification Loss Function
• Authors: Matthew Drnevich, Stephen Jiggins, Kyle Cranmer,
• Abstract summary: We introduce a convex loss function that is well-suited for both probabilistic and quasiprobabilistic density ratio estimation.<n>We demonstrate our approach on a real-world example from particle physics, of di-Higgs production in association with jets via gluon-gluon fusion.
• Score: 0.7769607568805288
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We consider a generalization of the classifier-based density-ratio estimation task to a quasiprobabilistic setting where probability densities can be negative. The problem with most loss functions used for this task is that they implicitly define a relationship between the optimal classifier and the target quasiprobabilistic density ratio which is discontinuous or not surjective. We address these problems by introducing a convex loss function that is well-suited for both probabilistic and quasiprobabilistic density ratio estimation. To quantify performance, an extended version of the Sliced-Wasserstein distance is introduced which is compatible with quasiprobability distributions. We demonstrate our approach on a real-world example from particle physics, of di-Higgs production in association with jets via gluon-gluon fusion, and achieve state-of-the-art results.

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