Machine Learning and Cosmology

The interplay between models and observations is a cornerstone of the scientific method, aiming to inform which theoretical models are reflected in the observed data. Within cosmology, as both models and observations have substantially increased in complexity over time, the tools needed to enable a rigorous comparison have required updating as well. With an eye towards the next decade in cosmology, the vast data volumes to be delivered by ongoing and upcoming surveys, as well as the ever-expanding theoretical search-space, motivate a re-thinking of the statistical machinery used. In particular, we are now at a crucial juncture where we may be limited by the statistical and data-driven tools themselves rather than the quality or volume of the available data. Methods based on artificial intelligence (AI) and machine learning (ML) have recently emerged as promising tools for cosmological applications, demonstrating the ability to overcome some of the computational bottlenecks associated with traditional statistical techniques. Machine learning is starting to see increased adoption across different subfields of and for various applications within cosmology. At the same time, the nascent and emergent nature of practical artificial intelligence motivates careful continued development and significant care when it comes to their application in the sciences, as well as cognizance of their potential for broader societal impact. In this white paper, we provide an overview of some of the ways machine learning methods are becoming increasingly central to the way cosmological data is collected, analyzed, and interpreted. Along the way, we highlight our vision for necessary developments, framing these as recommendations--both technological as well as sociological--for the widespread safe and equitable adoption of machine learning methods within cosmology in the coming decade.