6 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…
Unsupervised Evaluation of Multi-Turn Objective-Driven Interactions
Emi Soroka, Tanmay Chopra, Krish Desai +1
Large language models (LLMs) have seen increasing popularity in enterprise applications where AI agents and humans engage in objective-driven interactions. However, these systems a…
Unbinned Inference with Correlated Events
Krish Desai, Owen Long, Benjamin Nachman
Modern machine learning has enabled parameter inference from event-level data without the need to first summarize all events with a histogram. All of these unbinned inference metho…
Neural Posterior Unfolding
Fernando Torales Acosta, Jay Chan, Krish Desai +4
Differential cross section measurements are the currency of scientific exchange in particle and nuclear physics. A key challenge for these analyses is the correction for detector d…
Moment Unfolding
Krish Desai, Benjamin Nachman, Jesse Thaler
Deconvolving ("unfolding'') detector distortions is a critical step in the comparison of cross section measurements with theoretical predictions in particle and nuclear physics. Ho…
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…