Planet Hunters NGTS: no planet left behind in the Next Generation Transit Survey

Abstract

In this thesis, I present the Planet Hunters NGTS citizen science project. In this project, we enlist the help of members of the public to visually vet candidates from the Next Generation Transit Survey (NGTS). The aim of this project is to detect planet candidates that were missed in initial searches of these data. By combining classifications from multiple non-expert volunteers, we are able to efficiently classify these large datasets much faster than traditional visual vetting. I present 5 new planet candidates discovered through the Planet Hunters NGTS project that were not previously detected in these data. These discoveries demonstrate that the citizen science approach can provide a complementary method to the detection of exoplanets with ground-based surveys such as NGTS and thus can improve the completeness of these surveys.

I detail the identification and characterization of one of these candidates as an eclipsing binary system composed of two M-dwarfs. This system will be a valuable benchmark for testing stellar evolution models and in particular for testing the M-dwarf radius inflation problem.

Finally, motivated by the recovery of this binary system in photometry from the Asteroid Terrestrial-impact Last Alert System (ATLAS), I present an alternative approach for searching these sparse photometric datasets for transiting signals. I find this alternative approach to perform tentatively better than traditional methods at detecting transit signals in sparse photometry.

Overall, this thesis demonstrates the scientific value in the continued application of citizen science to astronomical problems. The ability to detect new and unusual signals that are overlooked by traditional techniques will continue to be of benefit as data volumes from astronomical surveys grow.

Date of Award	Jul 2026
Original language	English
Awarding Institution	Queen's University Belfast
Sponsors	Science & Technology Facilities Council
Supervisor	Megan Schwamb (Supervisor) & Christopher Watson (Supervisor)