Data reduction for low energy nuclear physics experiments using data frames

Marshall, Caleb

doi:10.21468/SciPostPhysCodeb.37

Title: Data reduction for low energy nuclear physics experiments using data frames

Journal Article · 05 November 2024 · SciPost Physics Codebases

DOI: https://doi.org/10.21468/SciPostPhysCodeb.37 · OSTI ID:2476423

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.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: FG02-88ER40387; FG02-97ER41033; FG02-97ER41041; SC0014384

OSTI ID:: 2476423

Journal Information:: SciPost Physics Codebases, Journal Name: SciPost Physics Codebases; ISSN 2949-804X

Publisher:: Stichting SciPostCopyright Statement

Country of Publication:: Netherlands

Language:: English

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