7 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…
Model-Agnostic Signal Discovery with Machine Learning: Bridging the Gap Between Theory and Practice
Oz Amram, Marco Letizia, Mikael Kuusela
Searches for new phenomena in complex scientific data are predominantly model-dependent, optimized for specific hypotheses, and therefore limited in their coverage of the space of…
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…