Mind the Gap: Navigating Inference with Optimal Transport Maps

Abstract:Machine learning (ML) techniques have recently enabled enormous gains in sensitivity to new phenomena across the sciences. In particle physics, much of this progress has relied on excellent simulations of a wide range of physical processes. However, due to the sophistication of modern machine learning algorithms and their reliance on high-quality training samples, discrepancies between simulation and experimental data can significantly limit their effectiveness. In this work, we present a solution to this ``misspecification'' problem: a model calibration approach based on optimal transport, which we apply to high-dimensional simulations for the first time. We demonstrate the performance of our approach through jet tagging, using a dataset inspired by the CMS experiment at the Large Hadron Collider. A 128-dimensional internal jet representation from a powerful general-purpose classifier is studied; after calibrating this internal ``latent'' representation, we find that a wide variety of quantities derived from it for downstream tasks are also properly calibrated: using this calibrated high-dimensional representation, powerful new applications of jet flavor information can be utilized in LHC analyses. This is a key step toward allowing the unbiased use of ``foundation models'' in particle physics. More broadly, this calibration framework has broad applications for correcting high-dimensional simulations across the sciences.

Data Analysis, Statistics and Probability (physics.data-an); Machine Learning (cs.LG); High Energy Physics - Experiment (hep-ex); Machine Learning (stat.ML)

arXiv:2507.08867 [physics.data-an]

(or arXiv:2507.08867v3 [physics.data-an] for this version)