LRP2020: Probing Diverse Phenomena through Data-Intensive Astronomy

TL;DR

This white paper discusses the rise of data-intensive astronomy, emphasizing the importance of international collaboration, infrastructure, training, and interdisciplinary approaches to advance the field in the era of large, complex datasets.

Contribution

It proposes initiatives for Canada to enhance its capabilities in data-intensive astronomy through collaboration, infrastructure, training, and interdisciplinary coordination.

Findings

Data-intensive methods are transforming astronomy across multiple fields.

International collaboration and infrastructure are key to leveraging large datasets.

Training and interdisciplinary approaches are essential for future success.

Abstract

The era of data-intensive astronomy is being ushered in with the increasing size and complexity of observational data across wavelength and time domains, the development of algorithms to extract information from this complexity, and the computational power to apply these algorithms to the growing repositories of data. Data-intensive approaches are pushing the boundaries of nearly all fields of astronomy, from exoplanet science to cosmology, and they are becoming a critical modality for how we understand the universe. The success of these approaches range from the discovery of rare or unexpected phenomena, to characterizing processes that are now accessible with precision astrophysics and a deep statistical understanding of the datasets, to developing algorithms that maximize the science that can be extracted from any set of observations. In this white paper, we propose a number of initiatives to maximize Canada's ability to compete in this data-intensive era. We propose joining international collaborations and leveraging Canadian facilities for legacy data potential. We propose continuing to build a more agile computing infrastructure that's responsive to the needs of tackling larger and more complex data, as well as enabling quick prototyping and scaling of algorithms. We recognize that developing the fundamental skills of the field will be critical for Canadian astronomers, and discuss avenues through with the appropriate computational and statistical training could occur. Finally, we note that the transition to data-intensive techniques is not limited to astronomy, and we should coordinate with other disciplines to develop and make use of best practises in methods, infrastructure, and education.