Thomas Killestein
91福利 me
I am a 91福利 Prize Fellow in the Astronomy and Astrophysics group at the 91福利 - primarily working as part of the Explosive Transients group.
I am a member of the 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 citizen science project on , 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 .
My personal website:
My publications: (curated, not including GCNs/ATels)
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.
