Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Cecilia Borca, Javier Jiménez Peña, David Marckx, Malgorzata Niemiec, Elisabetta Spadaro Norella, Marta Urbaniak

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.

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

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.
Paper Structure (7 sections, 20 figures)

This paper contains 7 sections, 20 figures.

Table of Contents

  1. Introduction
  2. Accessibility and quality of schools
  3. Machine Learning
  4. Detector simulation
  5. Data Acquisition and detector control systems
  6. Detector electronics
  7. Summary and conclusions

Figures (20)

  • Figure 1: The respondents’ career stage distribution (a). Name of the experiment in which the respondents take part (b).
  • Figure 2: The respondents’ fields of research distribution (a). Hot topics indicated by respondents (b).
  • Figure 3: Knowledge of available training programs (a). Have participants attended such programs? (b)
  • Figure 4: Coverage of topics in attended programs (a). Participant satisfaction with training programs (percentage) (b).
  • Figure 5: Usage of ML software (a) and knowledge level (b).
  • ...and 15 more figures