Codebase release 2.0 for sauce
Year: 2024
Type: article
Abstract: Low energy nuclear physics experiments are transitioning towards fully digital data acquisition systems. Realizing the gains in flexibility afforded by these systems relies on equally flexible data reduction techniques. In this paper, methods utilizing data frames and in-memory techniques to work with data, including data from self-triggering, digital data acquisition systems, are discussed within the context of a Python package, sauce. It is shown that data frame operations can encompass common analysis needs and allow interactive data analysis. Two event building techniques, dubbed referenced and referenceless event building, are shown to provide a means to transform raw list mode data into correlated multi-detector events. These techniques are demonstrated in the analysis of two example data sets.
Source: SciPost Physics Codebases
Author: Caleb Marshall
Institutions: Ohio University, Facility for Rare Isotope Beams
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
Awards: MRI: Development of a Separator for Capture Reactions (SECAR) Phase 2, STUDIES OF NUCLEAR ASTROPHYSICS, STRUCTURE, AND REACTIONS WITH NEUTRONS, RADIOACTIVE BEAMS, AND OTHER PROBES, A NEXT GENERATION RECOIL SEPARATOR FOR NUCLEAR ASTROPHYSICS SECAR (SEPARATOR FOR CAPTURE REACTIONS), G425872791, PFC: Joint Institute for Nuclear Astrophysics, JINA, STUDIES OF NUCLEAR PROCESSES, STUDIES OF NUCLEAR STRUCTURE USING NEUTRONS AND CHARGED PARTICLES
OpenAlex ID: W4404114977