Results of the analysis of a survey for young scientists on training quality in HEP instrumentation software and machine learning

TL;DR

This paper analyzes a survey of early-career physicists revealing gaps in training for open-source software and machine learning, aiming to guide improvements in educational programs.

Contribution

It provides a detailed analysis of survey data on training quality and accessibility for young scientists in HEP instrumentation software and machine learning.

Findings

High usage of open-source tools without formal training

Significant lack of training opportunities reported

Survey results to inform training program improvements

Abstract

A 2021 study by the ECFA Early-Career Researchers Panel revealed that 71% of 334 respondents used open-source software tools in their instrumentation work, yet 70% reported receiving no training for these tools. In response, the Software and Machine Learning for Instrumentation group was formed in the ECFA Early-Career Researchers Panel to assess the accessibility and quality of training programs in machine learning and software for early-career researchers in experimental and applied physics. This group launched a new survey, reaching 174 participants. This report summarises the survey results in detail, and is intended to serve as a guiding document to improve the training programs that are available to early-career researchers.