David Chemaly dc824@cam.ac.uk
Canada
Astronomy, Queens' College
PhD thesis: Machine Learning and Stellar Tidal Streams: Constraining Dark Matter
Research interests:
- Stellar Streams
- Dark Matter
- Machine Learning
- Galaxies
Stellar streams are like cosmic rivers of stars, formed when smaller galaxies get pulled apart by the gravitational pull of larger ones, like our Milky Way. These starry trails reveal clues about the invisible force of dark matter that surrounds galaxies. By studying the shapes and patterns of these streams, we can map out where dark matter might be and how much of it exists. The challenge lies in the sheer volume of data and the intricate details embedded within. This is where machine learning comes in. Machine learning dives deep into this cosmic puzzle, helping us see patterns that might be invisible to the naked eye. Through this fusion of tech and cosmos, I work on unravelling the universe's most enigmatic secrets and touching the very fabric of the unknown.
Who or what inspired you to pursue your research interests?
From a young age, I've been captivated by the night sky, a vast tapestry of mysteries waiting to be unravelled. It was the ethereal dance of the stars that first drew me in, but as I delved deeper, the invisible forces governing their movements became my obsession. Dark matter, an enigma making up much of our universe yet remaining unseen, beckoned me with its riddles. Coupled with the rise of machine learning, which promises to decipher even the most intricate cosmic patterns, I saw a unique opportunity. The fusion of astrophysics and artificial intelligence not only drives my research but fuels my passion: to decode the universe's silent whispers.