Dylan Magill (He/Him)

Current Research:

Tidal Disruption of Stars : A Critical Look at Machine Learning Methods for Finding Hungry Black Holes

Dylan’s research focuses on developing a machine-learning algorithm to identify tidal disruption events (TDEs), which occur when stars are ripped apart by the intense gravitational forces they experience as they approach supermassive black holes. TDEs are vital for investigating the properties and feeding conditions of black holes.

The Vera C. Rubin Observatory will soon begin its 10-Year Legacy Survey of Space and Time (LSST), which will result in a 100x increase in the number of transients we observe. With this greatly enhanced discovery rate, machine learning methods will be necessary to prioritise the interesting ones for follow-up study.

Another key aspect of Dylan’s research is investigating the potential environmental impacts of astronomical research. The immense scale of data processing required for the new large sky surveys will likely have a significant carbon dioxide output, contributing to anthropogenic climate change and negatively impacting the environment.

Biography:

Dylan started his PhD in October 2024 under the supervision of Dr Matt Nicholl after graduating with a First in MSci Physics with Astrophysics from QUB earlier that year. Additionally, Dylan completed a Summer Studentship in the Astrophysics Research Centre in the Summer of 2023. Dylan is a Leverhulme Interdisciplinary Network on Algorithmic Solutions (LINAS) Doctoral Scholar. Through this, he conducts additional research focusing on machine learning algorithms under the supervision of Dr Thai Son Mai and on the environmental impacts of astronomy and policy suggestions to limit and mitigate this under the supervision of Dr Marisa McVey.

Research Interests:

• Tidal Dirsuption Events
• Machine Learning
• Environmental Impacts of Astronomy
• Time Domain Astronomy, Supernovae

Supervisors:

Dr Matt Nicholl (ARC), Dr Thai Son Mai (EECS) and Dr Marisa McVey (Law)