LRP2020: Machine Learning Advantages in Canadian Astrophysics

TL;DR

This paper discusses the growing role of machine learning in Canadian astrophysics, highlighting its potential to revolutionize data analysis, solve complex problems, and position Canada as a leader in the field.

Contribution

It emphasizes Canada's leadership in ML and astrophysics, and explores how ML can transform astrophysical research with large datasets in the 2020s.

Findings

ML increases analysis speed by tens of millions of times

ML enables solutions to previously unsolvable astrophysical problems

Canada's investment in ML positions it as a global leader in astrophysics applications

Abstract

The application of machine learning (ML) methods to the analysis of astrophysical datasets is on the rise, particularly as the computing power and complex algorithms become more powerful and accessible. As the field of ML enjoys a continuous stream of breakthroughs, its applications demonstrate the great potential of ML, ranging from achieving tens of millions of times increase in analysis speed (e.g., modeling of gravitational lenses or analysing spectroscopic surveys) to solutions of previously unsolved problems (e.g., foreground subtraction or efficient telescope operations). The number of astronomical publications that include ML has been steadily increasing since 2010. With the advent of extremely large datasets from a new generation of surveys in the 2020s, ML methods will become an indispensable tool in astrophysics. Canada is an unambiguous world leader in the development of the field of machine learning, attracting large investments and skilled researchers to its prestigious AI Research Institutions. This provides a unique opportunity for Canada to also be a world leader in the application of machine learning in the field of astrophysics, and foster the training of a new generation of highly skilled researchers.