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stat.AP2026
Machine Learning-based Unfolding for Cross Section Measurements in the Presence of Nuisance Parameters
Huanbiao Zhu, Krish Desai, Mikael Kuusela +3
Statistically correcting measured cross sections for detector effects is an important step across many applications. In particle physics, this inverse problem is known as unfolding…
stat.AP2025
Robust semi-parametric signal detection in particle physics with classifiers decorrelated via optimal transport
Purvasha Chakravarti, Lucas Kania, Olaf Behnke +2
Searches for signals of new physics in particle physics are usually done by training a supervised classifier to separate a signal model from the known Standard Model physics (also…
stat.AP2025
COWs and their Hybrids: A Statistical View of Custom Orthogonal Weights
Chad Schafer, Larry Wasserman, Mikael Kuusela
A recurring challenge in high energy physics is inference of the signal component from a distribution for which observations are assumed to be a mixture of signal and background ev…