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In recent years, machine learning (ML) methods have remarkably improved how cosmologists can interpret data. The next decade will bring new opportunities for data-driven cosmological discovery, but will also present new challenges for adopting ML methodologies and understanding the results. ML could transform our field, but this transformation will require the astronomy community to both foster and promote interdisciplinary research endeavors.
(Abridged) The Truth and Reconciliation Commission of Canada published its calls to action in 2015 with 94 recommendations. Many of these 94 recommendations are directly related to education, language, and culture, some of which the Canadian Astronom
The study of comets affords a unique window into the birth, infancy, and subsequent history of the solar system. There is strong evidence that comets incorporated pristine interstellar material as well as processed nebular matter, providing insights
The standard $Lambda$ Cold Dark Matter cosmological model provides an amazing description of a wide range of astrophysical and astronomical data. However, there are a few big open questions, that make the standard model look like a first-order approx
This white paper describes the science case for Very Long Baseline Interferometry (VLBI) and provides suggestions towards upgrade paths for the European VLBI Network (EVN). The EVN is a distributed long-baseline radio interferometric array, that oper
Wide-angle surveys have been an engine for new discoveries throughout the modern history of astronomy, and have been among the most highly cited and scientifically productive observing facilities in recent years. This trend is likely to continue over