SciPost Submission Page
Data Reduction for Low Energy Nuclear Physics Experiments Using Data Frames
by Caleb Marshall
Submission summary
| Authors (as registered SciPost users): | Caleb Marshall |
| Submission information | |
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| Preprint Link: | scipost_202406_00025v1 (pdf) |
| Code repository: | https://github.com/camarsha/sauce |
| Date submitted: | 2024-06-12 15:05 |
| Submitted by: | Marshall, Caleb |
| Submitted to: | SciPost Physics Codebases |
| Ontological classification | |
|---|---|
| Academic field: | Physics |
| Specialties: |
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| Approach: | Computational |
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.
Current status:
Reports on this Submission
Strengths
1 - dynamic event building is a key feature that improves further analysis steps
2 - enables rapid exploration of analysis strategies
3 - automated analysis with one-line commands is facilitated with this approach
4 - extremely clear exposition of the approach
5 - excellent worked examples
Weaknesses
1 - a graphical user interface would be useful for those researchers who are not proficient in python but who still want to use this novel approach
Report
This work reports on a novel data frame-centered approach to data analysis that includes event building, data exploration, and automated/scripted analysis. The approach is optimized for experiments with low event rates and low channel numbers -- very different than analysis frameworks originating in high energy physics. There is a strong need for such a flexible system, especially in light of the latest low energy nuclear physics accelerator facilities, where rapid data analysis will be key to quick publication of results that is so critical for success these days.
The description of the approach is excellent in that it shows the advantages of its functionalities over existing systems, and it has a very clear exposition of the features. Most exciting is that this enables rapid exploration of different analysis strategies without endless repetition of the compile/sort/display/revise workflow. Additionally, the dynamic event building approach enables full control over all subsequent analysis steps, rather than being forced to use an approach dictated by others. The worked examples are explained is great detail and will be valuable for anyone choosing to use this system in the future.
If this system is well publicized, it could well become a standard at a number of facilities!
Requested changes
no changes requested
Recommendation
Publish (surpasses expectations and criteria for this Journal; among top 10%)