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Bayesian Techniques for Finding and ...

Hoffman, John.

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Bayesian Techniques for Finding and Characterizing Variable Stars: Application to the Hungarian-Made Automated Telescope Surveys.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Bayesian Techniques for Finding and Characterizing Variable Stars: Application to the Hungarian-Made Automated Telescope Surveys./
Author:	Hoffman, John.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	152 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-09, Section: B.
Contained By:	Dissertations Abstracts International80-09B.
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13423905
ISBN:	9780438866867

Bayesian Techniques for Finding and Characterizing Variable Stars: Application to the Hungarian-Made Automated Telescope Surveys.
Hoffman, John.

Bayesian Techniques for Finding and Characterizing Variable Stars: Application to the Hungarian-Made Automated Telescope Surveys. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 152 p.

Source: Dissertations Abstracts International, Volume: 80-09, Section: B.

Thesis (Ph.D.)--Princeton University, 2019.

This item must not be sold to any third party vendors.

Wide-field variability surveys like HAT (Bakos et al., 2004, 2013), OGLE (Udalski et al., 1994), Pan-STARRs (Chambers et al., 2016a), ASAS (Pojmanski, 1997; Jayasinghe et al., 2018c), and high-precision space-borne instruments such as Kepler (Koch et al., 2010), Gaia (Gaia Collaboration et al., 2016), and CoRoT (CoRot Team, 2016) have revolutionized the field of variable star astronomy. The future is still brighter - LSST (LSST Science Collaboration et al., 2009) will increase the number of known variable stars by over three orders of magnitude once the decadal survey completes in 2029. Analyzing variability in 10 billion objects requires a balance between statistical rigor and computational speed. This thesis presents the development and exploration of several techniques for detecting and characterizing variable stars in time-series photometry applied to data from the HATSouth (Bakos et al., 2013) automated survey. The first chapter develops a scalable algorithm for fitting periodic templates to noisy, irregularly-sampled timeseries. Template fitting is important for finding low- amplitude signals, as well as for finding and characterizing signals in very sparse timeseries. Recently (Sesar et al., 2016) used template fitting to successfully characterize O(105 ) RR Lyrae in Pan-STARRs DR1 photometry, where O(10) observations per ugrizy filter make traditional period finding techniques less sensitive. The second chapter presents a study of RR Lyrae observed by HATSouth (Bakos et al., 2013). We crossmatch with several external variable star catalogs to obtain several thousand RR Lyrae, and then use a Random Forest classifier to find hundreds of new RR Lyrae in HATSouth. We perform a careful Bayesian analysis of all RR Lyrae lightcurves to characterize double mode pulsations and the Blazhko effect. The third chapter develops a semi-supervised framework for performing automated variable star classification in the presence of sparse, noisy labels. We demonstrate that these techniques are capable of learning accurate classifications despite only using three labeled examples per 7 classes during training. This approach is also capable of handling non-Euclidean features such as the phase-invariant shape of periodic lightcurves.

ISBN: 9780438866867Subjects--Topical Terms:

517247
Statistics.

Bayesian Techniques for Finding and Characterizing Variable Stars: Application to the Hungarian-Made Automated Telescope Surveys.
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500 $a Source: Dissertations Abstracts International, Volume: 80-09, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Bakos, Gaspar A.;Hartman, Joel.
502 $a Thesis (Ph.D.)--Princeton University, 2019.
506 $a This item must not be sold to any third party vendors.
520 $a Wide-field variability surveys like HAT (Bakos et al., 2004, 2013), OGLE (Udalski et al., 1994), Pan-STARRs (Chambers et al., 2016a), ASAS (Pojmanski, 1997; Jayasinghe et al., 2018c), and high-precision space-borne instruments such as Kepler (Koch et al., 2010), Gaia (Gaia Collaboration et al., 2016), and CoRoT (CoRot Team, 2016) have revolutionized the field of variable star astronomy. The future is still brighter - LSST (LSST Science Collaboration et al., 2009) will increase the number of known variable stars by over three orders of magnitude once the decadal survey completes in 2029. Analyzing variability in 10 billion objects requires a balance between statistical rigor and computational speed. This thesis presents the development and exploration of several techniques for detecting and characterizing variable stars in time-series photometry applied to data from the HATSouth (Bakos et al., 2013) automated survey. The first chapter develops a scalable algorithm for fitting periodic templates to noisy, irregularly-sampled timeseries. Template fitting is important for finding low- amplitude signals, as well as for finding and characterizing signals in very sparse timeseries. Recently (Sesar et al., 2016) used template fitting to successfully characterize O(105 ) RR Lyrae in Pan-STARRs DR1 photometry, where O(10) observations per ugrizy filter make traditional period finding techniques less sensitive. The second chapter presents a study of RR Lyrae observed by HATSouth (Bakos et al., 2013). We crossmatch with several external variable star catalogs to obtain several thousand RR Lyrae, and then use a Random Forest classifier to find hundreds of new RR Lyrae in HATSouth. We perform a careful Bayesian analysis of all RR Lyrae lightcurves to characterize double mode pulsations and the Blazhko effect. The third chapter develops a semi-supervised framework for performing automated variable star classification in the presence of sparse, noisy labels. We demonstrate that these techniques are capable of learning accurate classifications despite only using three labeled examples per 7 classes during training. This approach is also capable of handling non-Euclidean features such as the phase-invariant shape of periodic lightcurves.
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