Grants and Contributions

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 study accretion onto the supermassive black hole at the Galactic Center, Sagittarius A*, using MIRI MRS IFU time-series spectral monitoring of Sgr A* and its surrounding region. Along with simultaneous Chandra observations, this data will be used to characterize the black hole’s MIR and X-ray emission in different flux density states, determine the nature of the emission, and constrain models of the accretion and radiation processes.

This project aims to use satellite measurements and targeted experiments to evaluate and improve the current understanding of chemical emissions from Canadian forest fires, crucial for enhancing modeling of smoke plumes and assessing their impacts on human health and the environment. By focusing on obtaining glyoxal and formaldehyde column densities and refining emission factors and branching ratios, the research seeks to advance forest fire smoke modeling, potentially mitigating the societal challenges posed by increasing prevalence of forest fires.

The main objectives of the project include expanding the range of gases for creating nanobubbles, standardizing methods for their characterization, and testing their resilience in conditions simulating space. The goal is to investigate plant growth in microgravity conditions that tests these systems in terrestrial environments within a simulated space environment. The expected outcomes include advancements in scientific knowledge and technological development in space health and life science, training opportunities for a minimum of 5 students, and benefits to Canadian populations.

The long-term objective of this project is to develop a low-cost wide field-of-view (FOV) optical system for debris detection from a nanosatellite. The project will aim to develop a more compact camera capable of real-time image processing for space surveillance in challenging lighting conditions. The project objectives are to design an advanced star field simulator to accurately represent challenging imaging environment, to develop an algorithm using neural network for star and RSO detection in non-ideal light conditions, and to design a real-time adaptive camera control in response to changes in illumination conditions.

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.

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. By utilizing synthetic aperture radar (SAR) data from satellites such as RADARSAT Constellation Mission (RCM) and RADARSAT-2, along with future NISAR observations, the project aims to provide the Canadian Ice Service (CIS) with improved operational ice charts, enhancing marine nowcasts and forecasts in the Canadian Arctic without manual intervention.

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.