6 papers
The Well-Tempered Likelihood: Honest Confidence Intervals for Misspecified Models
Benjamin Nachman, Jesse Thaler
Likelihood-based inference in particle physics, and in the physical sciences more broadly, relies on the assumption that the model accurately describes the data. When the model is…
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…
FlexCAST: Enabling Flexible Scientific Data Analyses
Benjamin Nachman, Dennis Noll
The development of scientific data analyses is a resource-intensive process that often yields results with untapped potential for reuse and reinterpretation. In many cases, a devel…
Stay Positive: Neural Refinement of Sample Weights
Benjamin Nachman, Dennis Noll
Monte Carlo simulations are an essential tool in particle physics data analysis. Events are typically generated alongside weights that redistribute the cross section of the simulat…
CODEX-b: Opening New Windows to the Long-Lived Particle Frontier at the LHC
Giulio Aielli, Juliette Alimena, Saul Balcarcel-Salazar +50
This document is written as a contribution to the European Strategy of Particle Physics (ESPP) update. We offer a detailed overview of current developments and future directions fo…
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…