Variability search in M 31 using Principal Component Analysis and the Hubble Source Catalog

Abstract

Principal Component Analysis (PCA) is being extensively used in Astronomy but not yet exhaustively exploited for variability search. The aim of this work is to investigate the effectiveness of using the PCA as a method to search for variable stars in large photometric data sets. We apply PCA to variability indices computed for light curves of 18152 stars in three fields in M 31 extracted from the Hubble Source Catalogue. The projection of the data into the principal components is used as a stellar variability detection and classification tool, capable of distinguishing between RR Lyrae stars, long period variables (LPVs) and non-variables. This projection recovered more than 90% of the known variables and revealed 38 previously unknown variable stars (about 30% more), all LPVs except for one object of uncertain variability type. We conclude that this methodology can indeed successfully identify candidate variable stars.

arXiv comment23 pages, 18 figures, MNRAS accepted

References in corpus (81)

• The Gaia mission
• Final Results from the Hubble Space Telescope Key Project to Measure the Hubble Constant
• The Man Behind the Curtain: X-rays Drive the UV through NIR Variability in the 2013 AGN Outburst in NGC 2617
• First Results from the Catalina Real-time Transient Survey
• Kepler Mission Design, Realized Photometric Performance, and Early Science
• The Palomar Transient Factory: System Overview, Performance and First Results
• GAIA: Composition, Formation and Evolution of the Galaxy
• VISTA Variables in the Via Lactea (VVV): The public ESO near-IR variability survey of the Milky Way
• The Tenth Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the SDSS-III Apache Point Observatory Galactic Evolution Experiment
• Limits on the Macho Content of the Galactic Halo from the EROS-2 Survey of the Magellanic Clouds
• Drizzle: A Method for the Linear Reconstruction of Undersampled Images
• The Pan-STARRS1 Surveys
• A giant stream of metal-rich stars in the halo of the galaxy M31
• Wide-field millimagnitude photometry with HAT: A Tool for Extra-solar Planet Detection
• The Second-Generation Guide Star Catalog: Description and Properties
• The Catalina Surveys Periodic Variable Star Catalog
• The Kilodegree Extremely Little Telescope (KELT): A Small Robotic Telescope for Large-Area Synoptic Surveys
• The first WASP public data release
• The VMC Survey - I. Strategy and First Data
• On Machine-Learned Classification of Variable Stars with Sparse and Noisy Time-Series Data
• The Next Generation Transit Survey (NGTS)
• Distributed and parallel time series feature extraction for industrial big data applications
• Distributions of Galaxy Spectral Types in the Sloan Digital Sky Survey
• Automated supervised classification of variable stars I. Methodology
• The Detailed Star Formation History in the Spheroid, Outer Disk, and Tidal Stream of the Andromeda Galaxy
• Machine-Learned Identification of RR Lyrae Stars from Sparse, Multi-band Data: the PS1 Sample
• Testing tests on AGN microvariability
• Automated Classification of Stellar Spectra. II: Two-Dimensional Classification with Neural Networks and Principal Components Analysis
• Comparative performance of selected variability detection techniques in photometric time series
• Variability Properties of 4 Million Sources in the $TESS$ Input Catalog Observed with the Kilodegree Extremely Little Telescope Survey
• A comparison of period finding algorithms
• Estimation of stellar atmospheric parameters from SDSS/SEGUE spectra
• Version 1 of the Hubble Source Catalog
• The EPOCH Project: I. Periodic variable stars in the EROS-2 LMC database
• PCA Tomography: how to extract information from datacubes
• The Variable Stars and Blue Horizontal Branch of the Metal-Rich Globular Cluster NGC 6441
• RR Lyrae Stars in the Andromeda Halo from Deep Imaging with the Advanced Camera for Surveys
• A Package for the Automated Classification of Periodic Variable Stars
• Gaia Data Release 1 - The Cepheid & RR Lyrae star pipeline and its application to the south ecliptic pole region
• A novel variability-based method for quasar selection: evidence for a rest frame ~54 day characteristic timescale
• Kernel PCA for type Ia supernovae photometric classification
• Cepheid theoretical models and observations in HST/WFC3 filters: the effect on the Hubble constant Ho
• The ACS LCID Project. II. Faint Variable Stars in the Isolated Dwarf Irregular Galaxy IC1613
• Machine learning search for variable stars
• Deep HST V- and I-Band Observations of Two Globular Clusters in the Halo of M31
• A New Census of the variable star population in the Globular Cluster NGC 2419
• Astrometry with Hubble Space Telescope Fine Guidance Sensors - A Review
• Periodic and Aperoidic Variability in the Molecular Cloud ρOphiuchus
• Discovery of distant RR Lyrae stars in the Milky Way using DECam
• Optical Identification of Cepheids in 19 Host Galaxies of Type Ia Supernovae and NGC 4258 with the Hubble Space Telescope
• The ACS LCID Project. VIII. The short-period Cepheids of Leo A
• Detecting Variability in Massive Astronomical Time-Series Data I: application of an infinite Gaussian mixture model
• Multi-Epoch HST Observations of IZw18: Characterization of Variable Stars at Ultra-Low Metallicities
• RATS-Kepler -- a deep high cadence survey of the Kepler field
• Search for high-amplitude Delta Scuti and RR Lyrae stars in Sloan Digital Sky Survey Stripe 82 using principal component analysis
• A Panoply of Cepheid Light Curve Templates
• The VVV Templates Project. Towards an Automated Classification of VVV Light-Curves. I. Building a database of stellar variability in the near-infrared
• Detection and Removal of Artifacts in Astronomical Images
• Clustering Based Feature Learning on Variable Stars
• HST/ACS Observations of RR Lyrae Stars in Six Ultra-deep Fields of M31
• Variable stars in the globular cluster NGC 7492. New discoveries and physical parameters determination
• Robust PCA and MIC statistics of baryons in early mini-haloes
• Newly Discovered RR Lyrae Stars in the SDSSXPanXSTARRS1XCatalina Footprint
• Formation of the Andromeda Giant Stream: Asymmetric Structure and Disc Progenitor
• Dwarf spheroidal satellites of M31: I. Variable stars and stellar populations in Andromeda XIX
• Deep Optical Photometry of Six Fields in the Andromeda Galaxy
• New Insights into Time Series Analysis II -- No Correlated Observations
• Precision multi-band photometry with a DSLR camera
• The ACS Nearby Galaxy Survey Treasury III: Cepheids in the Outer Disk of M81
• New Insights into Time Series Analysis - I - Correlated observations
• Improved Variable Star Search in Large Photometric Data Sets -- New Variables in CoRoT Field LRa02 Detected by BEST II
• First results of the Kourovka Planet Search: discovery of transiting exoplanet candidates in the first three target fields
• Searching transients in large-scale surveys. A method based on the Abbe value
• The Variable Star Population of the Globular Cluster B514 in the Andromeda Galaxy
• Catalog Matching with Astrometric Correction and its Application to the Hubble Legacy Archive
• Crowdsourcing quality control for Dark Energy Survey images
• Machine learning techniques to select Be star candidates. An application in the OGLE-IV Gaia south ecliptic pole field
• Detecting Variability in Massive Astronomical Time-Series Data II: Variable Candidates in the Northern Sky Variability Survey
• Optical Photometric Variable Stars towards the Galactic \hii region NGC 2282
• The EB Factory Project I. A Fast, Neural Net Based, General Purpose Light Curve Classifier Optimized for Eclipsing Binaries
• The Berlin Exoplanet Search Telescope II Catalog of Variable Stars. II. Characterization of the CoRoT SRc02 field

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• Deep XMM-Newton observations of the northern disc of M31. I. Source catalogue
• The Hubble Catalog of Variables (HCV)
• The massive star population of the Virgo Cluster galaxy NGC 4535
• Automatic Catalog of RRLyrae from $\sim$ 14 million VVV Light Curves: How far can we go with traditional machine-learning?
• The Local Galactic Transient Survey Applied to an Optical Search for Directed Intelligence