3 papers
astro-ph.IM2019
LRP2020: Machine Learning Advantages in Canadian Astrophysics
K. A. Venn, S. Fabbro, A Liu +12
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 po…
astro-ph.IM2019
LRP2020: Probing Diverse Phenomena through Data-Intensive Astronomy
Mubdi Rahman, Dustin Lang, Renée Hložek +2
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 algorit…
astro-ph.IM2019
Cleaning our own Dust: Simulating and Separating Galactic Dust Foregrounds with Neural Networks
K. Aylor, M. Haq, L. Knox +2
Separating galactic foreground emission from maps of the cosmic microwave background (CMB), and quantifying the uncertainty in the CMB maps due to errors in foreground separation a…