5 papers
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…
Response Matrix Estimation in Unfolding Differential Cross Sections
Huanbiao Zhu, Andrea Carlo Marini, Mikael Kuusela +1
The unfolding problem in particle physics is to make inferences about the true particle spectrum based on smeared observations from a detector. This is an ill-posed inverse problem…
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…
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…
Multidimensional Deconvolution with Profiling
Huanbiao Zhu, Krish Desai, Mikael Kuusela +3
In many experimental contexts, it is necessary to statistically remove the impact of instrumental effects in order to physically interpret measurements. This task has been extensiv…