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Astrophysics Research Centre

School of Mathematics and Physics

Crowd-Sourced Applications to the Next Generation Transit Survey


When an extrasolar planet (exoplanet) transits or passes in front of its host star, a portion of the star’s light is blocked, resulting in a characteristic dimming that can be measured from Earth. Today we know of over ~4000 transiting planets/planet candidates. The Next Generation Transit Survey (NGTS) consists of twelve automated 20cm telescopes working in tandem to monitor a 96 square degree patch of sky. Observing since 2015, the survey has produced a rich dataset to probe stellar variability and search tens of thousands of stars for the signatures of transiting exoplanets. NGTS applies automated algorithms to identify transiting planet candidates that are then reviewed and vetted by science team inspection. This process involves a large number of false positives and potential biases created by the small number of reviewers. Citizen science projects such as Galaxy Zoo and Planet Hunters have demonstrated that analysis tasks performed by large numbers of non-expert volunteers are of value, with the crowd-sourced assessments outperforming the majority of automated routines and machine learning algorithms.

Queen’s University Belfast is leading the development of a web-based citizen science project built upon the Zooniverse platform for a crowd-based search of the NGTS data. Our aim is to search for transiting planet signals that may have been missed in the initial NGTS search. The public will be enlisted to help in this search via a website where they will be tasked with inspecting several characteristic plots from stars identified from the NGTS algorithmic search.

The Project

The NGTS citizen science project is expected to launch in early to mid-2021. The main aim of the PhD project is to analysis and interpret the first results from this brand-new citizen science project. The student would take a leading role in the citizen science project, developing a software pipeline to combine the multiple volunteer classifications together and identify the most promising planet candidates. The project would also involve measuring a detection efficiency from simulated planet transits in order to probe planet population statistics. Additional aspects of the project could involve other citizen science/crowd-sourced applications/machine learning applications to the NGTS light curves including searches for stellar flares. We expect that a component of this project will involve performing, analyzing, and interpreting observational data from optical ground-based telescopes to validate and characterize discoveries from the NGTS citizen science project. There are likely to be opportunities within this PhD project for follow-up observations at UK-supported telescopes and observatories. Additionally, some aspects of the main PhD project can be tailored to the student’s interests.

More info

Supervisor: Dr. Meg Schwamb m.schwamb@qub.ac.uk, Co-supervisor: Prof. Chris Watson c.a.watson@qub.ac.uk

public/phds2021/2021_schwamb.txt · Last modified: 2020/12/17 11:46 by mschwamb

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