The Kuiper belt -- an area of the solar system past Neptune, stretching from 30 to 50 AU -- is densely populated with small objects. There are an estimated tens of billions to trillions of objects in this belt that are larger than a kilometre, but only around a thousand have been discovered. All of these detected Kuiper belt objects (KBOs) are larger than 15km, big icy bodies like the dwarf planet Pluto. Most objects in the Kuiper belt are far smaller, but kilometre-sized bodies are too small to be seen directly by telescopes.
If you are capable of imaging very dim objects at a very fast rate, these small KBOs can be detected by looking for the diffraction effects caused when they pass in front of distant stars. Fresnel diffraction causes a characteristic pattern in the light curve of the star that varies depending on KBO size, observing wavelength, the star's angular diameter, and so on. These events last for only a fraction of a second, but modern electron multiplying charge-coupled devices (EMCCDs) are capable of imaging these occultations.
At Western University, I developed a detection pipeline for Colibri, a new telescope array dedicated to the search for KBOs via this occultation method. With over 6TB of data imaged per night, the pipeline must employ autonomous real-time detection, erasing most collected data and only retaining information on candidate occultation events. The pipeline is primarily written in Python, incorporating functionality from Astropy, Source Extractor, Joblib, and Astrometry.net.
My work on the Colibri pipeline is published in the Publications of the Astronomical Society of the Pacific. This project was supervised by Stan Metchev at Western University.
Due to the finite speed of light, looking at distant objects in outer space means looking back in time. Look far enough away and you can find protoclusters: the precursors to the galaxy clusters we see today.
As a byproduct of a South Pole Telescope survey to study the fine structure of the cosmic microwave background radiation, a number of dusty, star-forming galaxies were identified at high redshift. Using data from ALMA, APEX, Spitzer, and Herschel, I analyzed the regions surrounding these galaxies, searching for other sources at similar redshift, quantifying their properties, and determining whether the regions were truly protoclusters. I also designed spectoscropic masks for follow-up observations with Gemini and ran simulations to predict the results of future surveys.
I am a co-author of a paper published in Nature, with further results from this work in preparation. This project was supervised by Scott Chapman at Dalhousie University.
My current work involves using machine learning to estimate the effective temperatures of hot Jupiters. This project is supervised by Nick Cowan at McGill University.