An Automated Pipeline for Variability Detection and Classification for the Small Telescopes Installed at the Liverpool Telescope
type: CreativeWork
description: The Small Telescopes at the Liverpool Telescope (STILT) is an almost decade old project to install a number of wide field optical instruments to the Liverpool Telescope, named Skycams, to monitor weather conditions and yield useful photometry on bright astronomical sources. The motivation behind this thesis is the development of algorithms and techniques which can automatically exploit the data generated during the first 1200 days of Skycam operation to catalogue variable sources in the La Palma sky. A previously developed pipeline reduces the Skycam images and produces photometric time-series data named light curves of millions of objects. 590,492 of these objects have 100 or more data points of sufficient quality to attempt a variability analysis. The large volume and relatively high noise of this data necessitated the use of Machine Learning and sophisticated optimisation techniques to successfully extract this information. The Skycam instruments have no control over the orientation and pointing of the Liverpool Telescope and therefore resample areas of the sky highly irregularly. The term used for this resampling in astronomy is ‘cadence’. The unusually irregular Skycam cadence places increased strain on the algorithms designed for the detection of periodicity in light curves. This thesis details the development of a period estimation method based on a novel implementation of a genetic algorithm combined with a generational clustering method. Named GRAPE (Genetic Routine for Astronomical Period Estimation), this algorithm deconstructs the space of possible periods for a light curve into regions in which the genetic population clusters. These regions are then fine-tuned using a k-means clustering algorithm to return a set of independent period candidates which are then analysed using a Vuong closeness test to discriminate between aliased and true periods. This thesis demonstrates the capability of GRAPE on a set of synthetic light curves built using traditional r…
abstract: The Small Telescopes at the Liverpool Telescope (STILT) is an almost decade old project to install a number of wide field optical instruments to the Liverpool Telescope, named Skycams, to monitor weather conditions and yield useful photometry on bright astronomical sources. The motivation behind this thesis is the development of algorithms and techniques which can automatically exploit the data generated during the first 1200 days of Skycam operation to catalogue variable sources in the La Palma sky. A previously developed pipeline reduces the Skycam images and produces photometric time-series data named light curves of millions of objects. 590,492 of these objects have 100 or more data points of sufficient quality to attempt a variability analysis. The large volume and relatively high noise of this data necessitated the use of Machine Learning and sophisticated optimisation techniques to successfully extract this information. The Skycam instruments have no control over the orientation and pointing of the Liverpool Telescope and therefore resample areas of the sky highly irregularly. The term used for this resampling in astronomy is ‘cadence’. The unusually irregular Skycam cadence places increased strain on the algorithms designed for the detection of periodicity in light curves. This thesis details the development of a period estimation method based on a novel implementation of a genetic algorithm combined with a generational clustering method. Named GRAPE (Genetic Routine for Astronomical Period Estimation), this algorithm deconstructs the space of possible periods for a light curve into regions in which the genetic population clusters. These regions are then fine-tuned using a k-means clustering algorithm to return a set of independent period candidates which are then analysed using a Vuong closeness test to discriminate between aliased and true periods. This thesis demonstrates the capability of GRAPE on a set of synthetic light curves built using traditional r…
author: McWhirter, PR
keywords: QA75 Electronic computers. Computer science, QB Astronomy
date published: 2018-10
date modified: 2026-07-24T06:30:25Z
provider: Liverpool Jon Moores University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Liverpool John Moores University

PropertyValue
property ID: DOI
value: 10.24377/researchonline.ljmu.ac.uk.00009479