The data include about 28 days of Ecliptic Pole Scanning Law (EPSL), starting from the 25th of July 2014, followed by a month-long transition to the Nominal Scanning Law (NSL) and then by an additional year of NSL data until the 16th of September 2015.
The set of 3194 time series and derived parameters of Cepheids and RR Lyrae stars published in this data release are located near the South ecliptic pole, where the number of Field-of-View (FoV) transits per source is already similar to the full-sky average at the end of the 5-year mission, thanks to the dense sampling of sources near the Ecliptic poles (up to 8 times per day) of the initial EPSL phase.
The -band time series mean magnitude per source covers a range from 11.7 to 20.1, with a standard deviation from 0.03 to 0.90 mag.
Full details of such time series are described in section 6 of Eyer et al. (2017).
6.2.1 Input catalogue
Author(s): Lorenzo Rimoldini, Berry Holl, Krzysztof Nienartowicz
The input data consisted of the Gaia data as specified in the following sections, together with information of crossmatched catalogues used in the creation of classification training sets (Section 6.2.2), as well as validation of the results (Section 6.5.2).
Only Gaia source positions were used for selection and crossmatch purposes in the variability processing of Gaia DR1.
Photometry was available in the form of per-field-of-view -band flux time series provided by CU5.
Preceding the Gaia DR1 data processing, classification was performed on a preliminary data set in which the not yet fully calibrated mean ‘colour’ was used because it significantly improved the classification, this information is however not published for the Gaia DR1 data.
RVS instrument data
No RVS data was available for the variability processing of Gaia DR1.
Astrophysical parameters data
No astrophysical parameters were available for the variability processing of Gaia DR1.
For this release, sources were only processed when they were located within 38 from the South ecliptic pole (referred to as the ‘SEP region’) and having a minimum of 20 field-of-view -band observations.
6.2.2 Training sets
Author(s): Lorenzo Rimoldini
Machine-learning classifiers were used in the detection of variable sources as well as in the general classification of variability types.
Training sets were constructed from a selection of Gaia sources crossmatched with surveys in the literature, using preliminary Gaia photometry as described in sections 5.1, 5.3, and 5.5 of Eyer et al. (2017).