Searching transients in large-scale surveys

Aims. A new method is presented to identify transient candidates in large-scale surveys based on the variability pattern in their light curves. Methods. The method is based on the Abbe value, \hbox{$\Ab$}, that estimates the smoothness of a light curve, and on a newly introduced value called the excess Abbe and denoted \hbox{$\excessAb$}, that estimates the regularity of the light curve variability pattern over the duration of the observations. Results. Based on simulated light curves, transients are shown to occupy a specific region in the \hbox{${\it \diagram}$} 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 0.50° × 0.17° field of the sky in the Large Magellanic Cloud centered at RA(J2000) = 5h25m56.5s and Dec(J2000) = –69d29m43.3s. The method identifies 43 EROS-2 transient candidates out of a total of ~1300 variable stars, and 19 more OGLE-II candidates, 10 of which do not have any EROS-2 variable star matches and which would need further confirmation to assess their reliability. 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. Conclusions. 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 \hbox{$\diagram$} 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