Thomas Killestein

About me

I am a Warwick Prize Fellow in the Astronomy and Astrophysics group at the University of Warwick - primarily working as part of the Explosive Transients group.

I am a member of the Gravitational-wave Optical Transient Observer team, having joined during my PhD. A key focus of my work is developing high performance deep learning classification algorithms for finding transient candidates in GOTO data, autonomously identifying their hosts, and identifying the most interesting objects -- facilitating rapid follow-up.

I co-lead the Kilonova Seekers citizen science project on Zooniverse, which enables members of the public to make impactful scientific discoveries by inspecting data from GOTO in near-real time. I lead the technical development of the project, developing databases and pipelines to accelerate citizen science to keep pace with the speed of time-domain astronomy. A full list of discoveries from the project is available here.

My personal website: https://tkilleste.inLink opens in a new window

My publications: via NASA ADSLink opens in a new window (curated, not including GCNs/ATels)

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Research interests:

• Machine learning and deep learning for time-domain astronomy: automating the large-scale discovery, classification, and analysis of astrophysical transients.
• Citizen science: empowering the public to make novel discoveries, and using the wisdom of the crowd to drive the state-of-the-art and quantify selection biases.
• Interacting transients: understanding the final years of the lives of the most massive stars, from observing in exquisite detail the dense circumstellar material surrounding them.
• Software development: building the tools required to process and reduce astronomical data, in reproducible and open ways.