The Library
Automatic vetting of planet candidates from ground based surveys : machine learning with NGTS
Tools
Tools
Tools
(2018) Automatic vetting of planet candidates from ground based surveys : machine learning with NGTS. Monthly Notices of the Royal Astronomical Society, 478 (3). pp. 4225-4237. doi:10.1093/mnras/sty1313
|
PDF
WRAP-automatic-vetting-planet-candidates-surveys-machine-learning-Armstrong-2018.pdf - Accepted Version - Requires a PDF viewer. Download (1484Kb) | Preview |
Official URL: https://doi.org/10.1093/mnras/sty1313
Abstract
State of the art exoplanet transit surveys are producing ever increasing quantities of data. To make the best use of this resource, in detecting interesting planetary systems or in determining accurate planetary population statistics, requires new automated methods. Here we describe a machine learning algorithm that forms an integral part of the pipeline for the NGTS transit survey, demonstrating the efficacy of machine learning in selecting planetary candidates from multi-night ground based survey data. Our method uses a combination of random forests and self-organising-maps to rank planetary candidates, achieving an AUC score of 97.6% in ranking 12368 injected planets against 27496 false positives in the NGTS data. We build on past examples by using injected transit signals to form a training set, a necessary development for applying similar methods to upcoming surveys. We also make the autovet code used to implement the algorithm publicly accessible. autovet is designed to perform machine learned vetting of planetary candidates, and can utilise a variety of methods. The apparent robustness of machine learning techniques, whether on space-based or the qualitatively different ground-based data, highlights their importance to future surveys such as TESS and PLATO and the need to better understand their advantages and pitfalls in an exoplanetary context.
| Item Type: | Journal Article | |||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software Q Science > QB Astronomy |
|||||||||||||||||||||
| Divisions: | Faculty of Science > Physics | |||||||||||||||||||||
| Library of Congress Subject Headings (LCSH): | Extrasolar planets, Astronomy -- Data processing, Machine learning, Algorithms | |||||||||||||||||||||
| Journal or Publication Title: | Monthly Notices of the Royal Astronomical Society | |||||||||||||||||||||
| Publisher: | Oxford University Press | |||||||||||||||||||||
| ISSN: | 0035-8711 | |||||||||||||||||||||
| Official Date: | 21 August 2018 | |||||||||||||||||||||
| Dates: |
|
|||||||||||||||||||||
| Date of first compliant deposit: | 18 May 2018 | |||||||||||||||||||||
| Volume: | 478 | |||||||||||||||||||||
| Number: | 3 | |||||||||||||||||||||
| Page Range: | pp. 4225-4237 | |||||||||||||||||||||
| DOI: | 10.1093/mnras/sty1313 | |||||||||||||||||||||
| Status: | Peer Reviewed | |||||||||||||||||||||
| Publication Status: | Published | |||||||||||||||||||||
| Publisher Statement: | This article has been published in a revised form in Monthly Notices of the Royal Astronomical Society https://doi.org/10.1093/mnras/sty1313. This version is free to view and download for private research and study only. Not for re-distribution, re-sale or use in derivative works. © 2018 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society | |||||||||||||||||||||
| Access rights to Published version: | Restricted or Subscription Access | |||||||||||||||||||||
| RIOXX Funder/Project Grant: |
|
|||||||||||||||||||||
| Related URLs: |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
