Panel Discussion: Practical Problem Solving for Machine Learning

TL;DR

This paper discusses the challenges and solutions in practical problem solving for machine learning in astrophysics, emphasizing interpretability, training, and community adoption.

Contribution

It provides insights into overcoming barriers in machine learning application in astrophysics and suggests future directions for the field.

Findings

Identified key barriers like interpretability and training.

Proposed strategies for improving ML adoption in astrophysics.

Discussed future pathways for the field.

Abstract

Machine Learning is a powerful tool for astrophysicists, which has already had significant uptake in the community. But there remain some barriers to entry, relating to proper understanding, the difficulty of interpretability, and the lack of cohesive training. In this discussion session we addressed some of these questions, and suggest how the field may move forward.