Grants and Contributions

Develop digital adoption plan

The recently launched Euclid Space Telescope will observe billions of galaxies across 10 billion years of Cosmic history to build a 3-dimensional map of a third of the sky. This will allow us to trace the formation and growth of the earliest supermassive black holes and their impact on the lives of galaxies. This project will enable Canada to utilize Euclid's extraordinary dataset to search for the origin of supermassive black holes and reveal the evolution of galaxies and their stars, while building the necessary expertise for Canada to lead its own wide field missions in the next decade.

Following the JWST ERS and Cycle 2 GO Announcement of Opportunity published on July 6, 2023, the CSA is providing funding via a Grant Agreement to the university to conduct their research using the JWST data.

This project will extract emission line maps from JWST/NIRISS data to produce a publicly available catalog which will then be used to analyze the spatial distribution of star formation within galaxies at Cosmic Noon. These emission line maps detail where emission line fluxes originate from within galaxies and contain information about the chemical abundances, distribution of dust, ages of the stellar populations, and the rate of star formation. This project is part of the OutThere survey, and alongside other data from the survey will further our understanding of how galaxies grow and evolve.

Project activities include: (1) providing Afghan women with a safe and conducive learning environment at the Asian University for Women campus in Bangladesh; (2) facilitating access to internationally recognized university level education programs; and (3) promoting community engagement and career development through access to leadership and life skills programs.

Furthermore, the current lack and/or public availability of relevant data sets hinders deployment of both current sustainability assessment methods and advanced machine leaning techniques to both develop data gap filling techniques and to identify best-fit technology solutions to known sustainability challenges.

Develop digital adoption plan

This project aims to enhance Canada's training capacity in satellite-based Earth observation to meet the growing demand for skilled professionals capable of handling large datasets and utilizing technologies like cloud computing and machine learning. Collaborating with industry and government partners across various application areas, the project will revise existing courses and develop new training materials to address emerging gaps, ensuring a mix of traditional university courses and flexible workshops accessible to professionals and students, ultimately providing long-lasting benefits to Canadians beyond the project's duration.

The project aims to develop machine learning-based methods for automatic estimation of Arctic ice concentration, classification of sea ice types, and monitoring of pack ice leads in the Arctic Ocean, focusing on regions like the Beaufort Sea and Canadian Arctic waters.

The objective of this project is to develop an AI-based algorithm that can improve atmospheric correction of satellite images over Canadian lakes. This will address current inaccuracies that hinder monitoring efforts, which are crucial for various aspects of Canadian life. The project aims to create a hyperspectral algorithm for accurate atmospheric correction by collecting in situ data and using advanced modeling techniques. AI will be leveraged to improve computational efficiency and enable more precise utilization of remote sensing data. This will ultimately advance the field of lake monitoring and benefit multiple stakeholders.

The Copernicus Expansion Missions Sea Ice Experiment (CEMSIE) project, led by a consortium of Canadian and European universities, with support from the European Space Agency (ESA), aims to enhance satellite monitoring of Arctic sea ice. This is to be achieved by simultaneously deploying multiple surface-based electromagnetic instruments in Dease Strait, near the Canadian High Arctic Research Station in Cambridge Bay, Nunavut. These instruments mimic three soon-to-be-launched ESA Copernicus Sentinel Expansion mission satellites: CRISTAL, CIMR, and ROSE-L.

CEMSIE's primary objectives include demonstrating how data integration from these three sensors can provide more comprehensive information than the sum of its parts. This integration aims to reduce uncertainties and enhance the accuracy of microwave satellite estimates of sea ice concentration, snow depth, and sea ice thickness.