Searching transients in large-scale surveys. A method based on the Abbe value

Abstract

(Abridged) A new method is presented to identify transient candidates in large-scale surveys based on the variability pattern in their light curves. The method is based on the Abbe value, that estimates the smoothness of a light curve, and on a newly introduced value called the excess Abbe that estimates the regularity of the light curve variability pattern over the duration of the observations. Based on simulated light curves, transients are shown to occupy a specific region in the Abbe versus Excess Abbe diagram, distinct from sources presenting pulsating-like features in their light curves or having featureless light curves. The method is tested on real light curves taken from EROS-2 and OGLE-II surveys in a 0.50deg x 0.17deg field of the sky in the LMC. The method identifies 43 EROS-2 transient candidates out of a total of ~1300 variable stars, and 19 more OGLE-II candidates. The efficiency of the method is further tested by comparing the list of transient candidates with known Be stars in the literature. It is shown that all Be stars known in the studied field of view with detectable bursts or outbursts are successfully extracted by the method. In addition, four new transient candidates displaying bursts and/or outbursts are found in the field, of which at least two are good new Be candidates. The new method proves to be a potentially powerful tool to extract transient candidates from large-scale multi-epoch surveys. The better the photometric measurement uncertainties are, the cleaner the list of detected transient candidates is. In addition, the diagram is shown to be a good diagnostic tool to check the data quality of multi-epoch photometric surveys. A trend of instrumental and/or data reduction origin, for example, will manifest itself by an unexpected distribution of points in the diagram.

Accepted for publication in A&A

References in corpus (13)

• First Results from the Catalina Real-time Transient Survey
• The Palomar Transient Factory: System Overview, Performance and First Results
• GAIA: Composition, Formation and Evolution of the Galaxy
• Limits on the Macho Content of the Galactic Halo from the EROS-2 Survey of the Magellanic Clouds
• The MACHO Project LMC Microlensing Results from the First Two Years and the Nature of the Galactic Dark Halo
• Gravitational Microlensing in the Local Group
• Catalog of 93 Nova Light Curves: Classification and Properties
• The ROTSE-III Robotic Telescope System
• Depth estimation of the Large and Small Magellanic Clouds
• Microlensing optical depth toward the Galactic Bulge using bright sources from OGLE-II
• On Be Star Candidates and Possible Blue Pre-Main Sequence Objects in the Small Magellanic Cloud
• R Coronae Borealis stars in the Galactic Bulge discovered by EROS-2
• A New View of the Circumstellar Environment of SN 1987A

Cited by in corpus (15)

• Comparative performance of selected variability detection techniques in photometric time series
• Gaia Data Release 2: The first Gaia catalogue of long-period variable candidates
• A comprehensive power spectral density analysis of astronomical time series I: the Fermi-LAT gamma-ray light curves of selected blazars
• Machine learning search for variable stars
• A Comprehensive Power Spectral Density Analysis of Astronomical Time Series. II. The Swift/BAT Long Gamma-Ray Bursts
• Gaia eclipsing binary and multiple systems. Two-Gaussian models applied to OGLE-III eclipsing binary light curves in the Large Magellanic Cloud
• Gaia Data Release 2. Short-timescale variability processing and analysis
• Machine learning techniques to select Be star candidates. An application in the OGLE-IV Gaia south ecliptic pole field
• Semi-Supervised Classification and Clustering Analysis for Variable Stars
• The Hubble Catalog of Variables (HCV)
• Optical variability modeling of newly identified blazar candidates behind Magellanic Clouds
• Analytical representation of Gaussian processes in the $\mathcal{A}-\mathcal{T}$ plane
• Variability search in M 31 using Principal Component Analysis and the Hubble Source Catalog
• Unsupervised learning for variability detection with Gaia DR3 photometry. The main sequence-white dwarf valley
• The Hubble Catalog of Variables