Data Reduction for Low Energy Nuclear Physics Experiments Using Data Frames

TL;DR

This paper presents flexible data reduction methods using data frames for low energy nuclear physics experiments with digital data acquisition, enabling interactive analysis and efficient event building.

Contribution

It introduces data frame-based techniques and two novel event building methods for transforming raw data into correlated multi-detector events within a Python package.

Findings

Data frame operations support common analysis needs.

Referenced and referenceless event building methods effectively transform raw data.

Demonstrated techniques on real data sets.

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, \texttt{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.