Gaia Data Release 1 - The Cepheid & RR Lyrae star pipeline and its application to the south ecliptic pole region
Key Words.:star: general – Stars: oscillations – Stars: variables: Cepheids – Stars: variables: RR Lyrae – Methods: data analysis – Magellanic Clouds
Context:The European Space Agency spacecraft Gaia is expected to observe about 10,000 Galactic Cepheids and over 100,000 Milky Way RR Lyrae stars (a large fraction of which will be new discoveries), during the five-year nominal lifetime spent scanning the whole sky to a faint limit of = 20.7 mag, sampling their light variation on average about 70 times.
Aims:We present an overview of the Specific Objects Study (SOS) pipeline developed within the Coordination Unit 7 (CU7) of the Data Processing and Analysis Consortium (DPAC), the coordination unit charged with the processing and analysis of variable sources observed by Gaia, to validate and fully characterise Cepheids and RR Lyrae stars observed by the spacecraft. The algorithms developed to classify and extract information such as the pulsation period, mode of pulsation, mean magnitude, peak-to-peak amplitude of the light variation, sub-classification in type, multiplicity, secondary periodicities, light curve Fourier decomposition parameters, as well as physical parameters such as mass, metallicity, reddening and, for classical Cepheids, age, are briefly described.
Methods:The full chain of the CU7 pipeline was run on the time-series photometry collected by Gaia during 28 days of Ecliptic Pole Scanning Law (EPSL) and over a year of Nominal Scanning Law (NSL), starting from the general Variability Detection, general Characterisation, proceeding through the global Classification and ending with the detailed checks and typecasting of the SOS for Cepheids and RR Lyrae stars (SOS Cep&RRL). We describe in more detail how the SOS Cep&RRL pipeline was specifically tailored to analyse Gaia’s -band photometric time-series with a South Ecliptic Pole (SEP) footprint, which covers an external region of the Large Magellanic Cloud (LMC), and to produce results for confirmed RR Lyrae stars and Cepheids to be published in Gaia Data Release 1 (Gaia DR1).
Results:-band time-series photometry and characterization by the SOS Cep&RRL pipeline (mean magnitude and pulsation characteristics) are published in Gaia DR1 for a total sample of 3,194 variable stars, 599 Cepheids and 2,595 RR Lyrae stars, of which 386 (43 Cepheids and 343 RR Lyrae stars) are new discoveries by Gaia. All 3,194 stars are distributed over an area extending 38 degrees on either side from a point offset from the centre of the LMC by about 3 degrees to the north and 4 degrees to the east. The vast majority, but not all, are located within the LMC. The published sample also includes a few bright RR Lyrae stars that trace the outer halo of the Milky Way in front of the LMC.
Easy to recognise thanks to their characteristic light variation, Cepheids and RR Lyrae stars are radial pulsating variables that trace stellar populations with different age and chemical composition: classical Cepheids (hereinafter, DCEPs) trace a young (300 Myr) stellar component; anomalous Cepheids (ACEPs) can trace stars of intermediate age (1-5 Gyr) and metal poor content ([Fe/H] dex), although it is still matter of debate whether they might also arise from coalescence of binary stars as old as about 10 Gyr; finally, the RR Lyrae stars and the Type II Cepheids (T2CEPs) trace an old (10 Gyr) stellar population.
They are primary standard candles in establishing the cosmic distance ladder, because Cepheids conform to period–luminosity (), period–luminosity–colour () and period – Wesenheit () relations, whereas the RR Lyrae stars follow a luminosity–metallicity relation in the visual band (–[Fe/H]) and a period–luminosity–metallicity () relation in the infrared.
With its multi-epoch monitoring of the full sky, Gaia will discover and measure position, parallax, proper motion and time-series photometry of thousands of Cepheids and RR Lyrae stars in the Milky Way (MW) and its surroundings, down to a faint magnitude limit of 20.7 mag. The spacecraft is expected to observe from 2,000 to 9,000 MW Cepheids, about 70,000 RR Lyrae stars in the Galactic halo, from 15,000 to 40,000 RR Lyrae in the MW bulge, (see table 3 in Eyer et al. 2012, and references therein) and according to the most recent estimates (Soszynski et al. 2015b, 2016) over 45,500 RR Lyrae stars and 9,500 Cepheids in the Magellanic Clouds. Gaia will revise upwards these statistics, as ongoing surveys such as OGLE-IV (Soszynski et al. 2015a, b, c, 2016), Catalina Sky Survey (CSS, Drake et al. 2013; Torrealba et al. 2015), Pan-STARRS (Hernitschek et al. 2016), LINEAR (Sesar et al. 2013) and PTF (Cohen et al. 2015), are constantly reporting new discoveries and increased numbers of RR Lyrae stars and Cepheids both in the MW and in its neighbour companions.
Gaia’s complete census of the Galactic Cepheids and RR Lyrae will allow a breakthrough in our understanding of the MW structure by tracing young and old variable stars all the way through from the Galactic bulge, to the disk, to the halo, and likely revealing new streams and faint satellites that bear witness of the MW hierarchical build-up (see e.g. Clementini 2016). But most importantly, Gaia will measure the parallax of tens of thousands of Galactic Cepheids and RR Lyrae stars, along with milli-mag optical spectrophotometry ( broad-band white-light magnitude, blue and red spectro-photometry) and radial velocities and chemistry for those within reach of the Radial Velocity Spectrometer (RVS; 17 mag). The unprecedented accuracy of Gaia measurements for local Cepheids and RR Lyrae stars will allow the absolute calibration via parallax of the Cepheid , , and of the [Fe/H] and infrared relations for RR Lyrae stars, along with a test of the metallicity effects through simultaneous abundance measurements. This will enable re-calibration of “secondary” distance indicators probing distances far into the unperturbed Hubble flow and a total re-assessment of the whole cosmic distance ladder, from local to cosmological distances, in turn significantly improving our knowledge of the Hubble constant. The physical parameters of Cepheids and RR Lyrae stars will be constrained by Gaia photometry, parallax, metallicity and radial velocity (RV) measurements, that will also constrain the input physics of theoretical pulsation models. This will further improve the use of Cepheids and RR Lyrae stars as standard candles and stellar population tracers.
In this paper we describe the Specific Objects Study (SOS) pipeline developed within the Coordination Unit 7 (CU7) of the Data Processing and Analysis Consortium (DPAC), the coordination unit in charge of the processing and analysis of variable sources observed by Gaia, to validate and fully characterise Cepheids and RR Lyrae stars observed by the spacecraft. A detailed description of the Gaia mission, its scientific goals and performance, as well as a comprehensive illustration of the Gaia DPAC structure and activities can be found in Gaia Collaboration et al. (2016c). A summary of the astrometric, photometric and survey properties of Gaia Data Release 1 (Gaia DR1) and a description of scientific quality and limitations of this first data release are provided in Gaia Collaboration et al. (2016a). The photometric data set and the processing of the -band photometry released in Gaia DR1 are thoroughly discussed in van Leeuwen et al. (2016), Carrasco et al. (2016), Riello et al. (2016), and Evans et al. (2016).
We note that a rather strict policy is adopted within DPAC for what concerns the processing and dissemination of Gaia data. Specifically, it was decided to be consistent in how DPAC does the processing and what it is published in Gaia releases. That is, we do not release results which are based on Gaia data that is not published. For instance, since no , photometry, is released in Gaia DR1, only the -band time series photometry was used for processing and classification of the variable sources released in Gaia DR1. Furthermore, characterisation and classification of Gaia variable sources rely only on Gaia data. That is, we do not complement Gaia’s time series with external non-Gaia data to increase the number of data points or the time-span of the Gaia observations. Literature published data are used once the processing is completed, only to validate results (i.e. characterisation and classification of the variable sources) which are, however, purely and exclusively based on Gaia data. If and how this may have limited the efficiency of the Gaia pipeline for Cepheids and RR Lyrae stars is extensively discussed in the paper and most specifically in Section 3.2. On the other hand, we are also sure that the next Gaia data releases will significantly improve both census and results for variable stars, and will also emend misclassifications if/where they have occurred.
The SOS pipeline for Cepheids and RR Lyrae stars, hereinafter referred to as SOS Cep&RRL pipeline, is one of the latest stages of the general variable star analysis pipeline. Steps of the processing prior the SOS Cep&RRL pipeline are fully described in Eyer et al. (2016), to which the reader is referred to for details.
Validation of the classification provided by the general variable star analysis pipeline is necessary, since Cepheids and RR Lyrae stars overlap in period with other types of variables (e.g. binary systems, long period variables, etc.). SOS Cep&RRL uses specific features such as the parameters of the light curve Fourier decomposition and diagnostic tools like the Gaia colour-magnitude diagram (CMD), the amplitude ratios, the period-amplitude (), , and relations, and the Petersen diagram (Petersen, 1973), to check the classification, to derive periods and pulsation modes, and to identify multimode pulsators. RV measurements obtained by the RVS are also planned for use as soon as they become available, to identify binary/multiple systems.
The main tasks of the SOS Cep&RRL pipeline are: i) to validate and refine the detection and classification of Cepheids and RR Lyrae stars in the Gaia data base, provided by the general variable star analysis pipeline, by cleaning the sample from contaminating objects, i.e. other types of variables falling into the same period domain; ii) to check and improve the period determination and the light curve modelling; iii) to identify the pulsation modes and the objects with secondary and multiple periodicities; iv) to classify RR Lyrae stars and Cepheids into sub-types (fundamental mode – RRab, first overtone – RRc, and double mode – RRd) according to the pulsation mode for the RR Lyrae stars, and DCEPs, ACEPs and T2CEPs for the Cepheids, along with identification of pulsation modes for the former two, and sub-classification into W Virginis (WVIR), BL Herculis (BLHER) and RV Tauri (RVTAU) types for the latter; v) to identify and flag variables showing modulations of the light curve, due to a binary companion or to the Blazhko effect (Blazhko, 1907), that may falsify both the star brightness and the derived trigonometric parallax; vi) to use the pulsation properties and derive physical parameters (luminosity, mass, radius, effective temperature, metallicity, reddening, etc.) of confirmed bona fide RR Lyrae stars and Cepheids to be ingested into the Gaia main data base, by means of a variety of methods specifically tailored to these types of variables.
The paper is organised as follows: Section 2 provides a description of the whole architecture of the SOS Cep&RRL pipeline, its diagnostic tools and their definition in the Gaia pass-bands. Section 3 presents the dataset and source selection on which the SOS Cep&RRL pipeline was run and describes how the pipeline was specifically tailored and simplified to analyse the Gaia SEP -band time series data of candidate Cepheids and RR Lyrae stars. Section 4 presents results of the SOS Cep&RRL analysis that are published in Gaia DR1. Finally, the main results and future developments of the pipeline are summarised in Section 5.
2 The SOS Cep&RRL pipeline
In this Section we present the SOS Cep&RRL pipeline in its complete form, briefly describing all its algorithms and tools, only part of which could actually be applied due to the specific characteristics of the dataset published in Gaia DR1 (see Section 3.2 for details).
In order to validate and refine the classification of Cepheids and RR Lyrae stars provided by the general variable star analysis pipeline, and be able in the future to make comparisons with the parameters measured for these stars by other DPAC processing tasks, different procedures are implemented in the SOS Cep&RRL pipeline as described in the following sections. The whole processing of the SOS Cep&RRL pipeline is presented schematically in Figs. 1, 2, and 3 and described in detail in Sections 2.1, 2.3 and 2.4. For each candidate confirmed as a bona fide RR Lyrae star or Cepheid, SOS Cep&RRL returns the most probable period, pulsation mode, multiple periodicities, if any, associated amplitudes, intensity-averaged magnitudes and mean radial velocities (if RVS data are available) as well as flagging of binary or multiple systems, to be published in the Gaia main data base.
The main input of SOS Cep&RRL is the calibrated Gaia time-series photometry (-magnitudes and integrated , photometry) processed by CU5, the DPAC coordination unit charged with the photometric processing of Gaia data, for all sources pre-classified as candidate Cepheids and RR Lyrae stars by the general variability Characterization and Classification processing, along with general information on the data, such as number of transits per source (after outlier removal), typical errors, time span of the observations, mean and median magnitude values, etc., computed by the Statistical Parameters module in the general pipeline (Eyer et al. 2016) prior SOS Cep&RRL.
Additional information will be progressively added in future releases such as: time-series RVs for sources within the RVS magnitude limit, Gaia’s parallaxes (distances), astrophysical parameters such as: effective temperature (), gravity () and absorption () inferred by combining Gaia’s astrometry, photometry and spectroscopy. They can be used to optimise the SOS Cep&RRL processing.
2.1 Initial processing: characterisation of the light curves (Period search & Fourier fitting)
The SOS Cep&RRL processing starts by characterising the -band light curve (as well as the and light curves and the RVS RV curve if/when available) of sources classified as candidate Cepheids and RR Lyrae stars by the Supervised Classification module of the general variable star analysis pipeline. This part of the SOS Cep&RRL processing is common to both Cepheids and RR Lyrae stars and its main steps are sketched in Fig. 1.
The first step is the derivation of the source periodicity independently and with a different method to that used in the general pipeline Characterisation module. We used the Lomb-Scarge algorithm (Lomb 1976, Scargle 1982) as opposed to the least squares method used in Characterisation (see Eyer et al. 2016). Tests performed on Cepheids and RR Lyrae stars in the Hipparcos catalogue showed that the Lomb-Scarge method reduces the number of large deviating period values for these specific types of variables. The period derived by SOS Cep&RRL is used to fold the light curve which is then modelled with a truncated Fourier series in the form:
Zero-point (zp), period
(1/), harmonic number (), amplitudes (), and
phases () of the harmonics, for the -band light curve are first computed with a linear fitting procedure. They provide initial trial values for the non-linear
modelling of the light curve which is performed using the Levenberg-Marquardt (Levenberg 1944, Marquardt 1963) non-linear fitting
algorithm (module NonLinearFourierAnalysis of Fig. 1). The non-linear fitting refines both the period and model of the light curve.
In Gaia DR1 only the -band time-series photometry is available for the sources, hence, the period resulting from the non-linear fitting of the -band light curve is
the final period adopted for the source and module LcMeasurements directly measures the source intensity-averaged
mean magnitude, peak-to-peak -amplitude and
epoch of maximum light from the -band light curve modelled with the non-linear fitting algorithm
a new light curve is generated where: (value of each phase point) = (value of the original phase point) + (random number) (error of the original phase point value). Random numbers range between 0 and 1;
a new model is computed with the non-linear modelling procedure, using as trial values the model parameters of the original light curve;
these two first steps are iterated 100 times;
for each parameter of the model, average and standard deviation are computed over the 100 simulations;
the standard deviations derived in the previous step are assigned as uncertainties of the parameters resulting from the non-linear modelling.
The final step in the general part of the SOS Cep&RRL pipeline is the detection of possible secondary periodicities in the source periodogram. This is the task of module SecondaryPeriodicities. For RR Lyrae stars we look for one and for Cepheids for two additional frequencies beyond the first periodicity. Following the procedure adopted in the NonLinearFourierAnalysis module, the residuals: observed model phase points, are searched for secondary periodicities. If a significant secondary frequency is found the procedure is iterated twice. The steps of the SecondaryPeriodicities algorithm are as follows:
residuals are computed as: (observed) (corresponding model) phase points;
the Lomb-Scargle period search method is run on the residual time-series;
As it will be discussed in Section 5, this module will be revised in preparation for the Gaia second data release (Gaia DR2) in order to properly take into account the actual significance of the detected secondary periodicities.
After this common part the analysis proceeds in two separate branches each specifically tailored to the processing of RR Lyrae stars and Cepheids, respectively. This is summarised in the flow-charts in Figs. 2 and 3. Sources classified as candidate Cepheids by the general Classification pipeline will be sent first to the Cepheid branch. Conversely, sources classified as candidate RR Lyrae stars will first be processed through the RR Lyrae branch. However, as it will be described in Section 3.2 for the processing of the Gaia DR1 data we followed a different approach to decide which branch a source should be sent to.
2.2 Conversions to Gaia’s passbands
A number of tools specifically applicable for the characterisation of RR Lyrae stars and Cepheids are adopted in the different modules of the Cepheid and RR Lyrae branches (see Figs. 2 and 3). They include: the CMD, the -band diagram (also known, for RR Lyrae stars, as Bailey diagram, Bailey 1902), the , and relations for Cepheids, the different planes in the Fourier parameter space, and the Petersen diagram (Petersen 1973) for double-mode Cepheids and RR Lyrae stars. All limits and loci of these tools are defined for the Johnson photometric system. We have converted them to the Gaia photometric system in order to apply them directly to Gaia sources. We have used pass-band transformations provided in Jordi et al. (2010) and subsequent updates (Jordi, personal communication) to compute conversion formulae appropriate for the colour and metallicity ranges of Cepheids and RR Lyrae stars ( 2.5 mag and [Fe/H] 0.5 dex ) and transform the Johnson-Cousins , to the Gaia , , pass-bands. The conversion formula for the band is provided in Section A and shown in Fig. 36.
We then used a sample of 128 RR Lyrae stars
belonging to the Galactic halo and bulge, to the Galactic globular clusters M3 and M68 and to the LMC and Small Magellanic Cloud (SMC) and
77 classical Cepheids in the MW, LMC and SMC, for which excellent light curves
in the Johnson and passbands have been
published by Soszynski et al. (2008a, 2009, 2010a, 2010c, 2011a), Cacciari et al. (2005), Walker (1994),
Moffett & Barnes (1984), Gieren (1981), Coulson & Caldwell (1985); Coulson et al. (1985), and Berdnikov & Turner (1995); Berdnikov et al. (2012, 2014).
The two samples cover entirely the parameter space of pulsation modes,
periods and metallicities for RR Lyrae stars and Cepheids.
We used Eq. (A.1) to transform the , light curves of these selected samples
to Gaia passband and fitted both original , and Gaia-transformed band light curves with truncated Fourier series obtaining , , , parameters and peak-to-peak amplitude in each band.
Finally, we used the quantities obtained with the above procedure to derive relationships and transform the Fourier parameters and peak-to-peak amplitudes from the Johnson-Cousins
to the Gaia -band. The relations obtained with this procedure are described by Eqs. (2) to (19)
Formulae to convert to the Gaia -band the literature peak-to-peak amplitudes of RR Lyrae stars are provided by Eqs. (2) and (3):
We show in Fig. 4 the diagram in the Gaia band of RR Lyrae stars in the LMC, the SMC and the Galactic bulge and halo, obtained converting the literature and amplitudes using eqs. (2) and (3). We used literature amplitudes from Soszynski et al. (2009, 2010c, 2011a) for the RR Lyrae stars in the Magellanic Clouds, and from Pojmanski (1997) for the Galactic variables. In the figure a black solid line is drawn to separate RRab from RRc types. Also shown in the figure are ACEPs (filled circles) and T2CEPs (crosses) in the LMC and SMC taken from Soszynski et al. (2008b, 2015a). Their amplitudes were transformed to the -band also using eqs. (2) and (3), since colours and amplitudes of ACEPs and T2CEPs are similar to those of RR Lyrae stars. The diagram in Fig. 4 is the main tool used to separate RR Lyrae stars into RRab and RRc types (see Section 3). However, since mixing of the pulsation modes occurs close to the separation line and further contamination is also caused by ACEPs, we also used the Fourier parameters to separate different pulsation modes and variable types.
Eqs. (4) to (11) provide formulae to convert to the Gaia -band the literature values of the Fourier parameters , , and of RR Lyrae stars:
Figs. 5 and 6 show the -band Fourier parameters and of RR Lyrae stars computed using eqs. (4), (5), (8) and (9) plotted versus period (). The vs plot was used along with the diagram to distinguish RR Lyrae stars from ACEPs and to better separate the RRab and RRc types (see Section 3).
Finally. Eqs. (12) to (19) provide formulae to convert to the Gaia -band the literature values of the Fourier parameters , , and of Cepheids:
2.3 RR Lyrae branch
The different steps of the processing for sources classified as RR Lyrae stars by the general Classification pipeline are summarised in Fig. 2. However, only some modules of the RR Lyrae branch were activated for the analysis of the Gaia DR1 data.
Identification of double-mode RR Lyrae stars: RRLyraeDoubleModeSearch module
For RR Lyrae stars for which a secondary periodicity was detected by module SecondaryPeriodicities of the initial SOS Cep&RRL processing (see Fig. 1) the RRLyraeDoubleModeSearch module sets to first overtone period () and fundamental mode period () the shorter and the longer periodicities, respectively. Then if both and are longer than 0.3 d, it checks whether the star locates within the regions of the / vs plane (generally known as Petersen diagram, Petersen 1973), allowed for RR Lyrae double-mode pulsation. The allowed loci in the Petersen diagram were defined using the , and / values of 1,335 RRd variables observed by the OGLE and ASAS surveys of the LMC, SMC, Galactic bulge and halo. Their Petersen diagram is shown in Fig. 7. A lower limit of 0.3 d for the , values was also inferred from Fig. 7, which shows that no RRd pulsators are known with d. Schematically, the RRLyraeDoubleModeSearch algorithm performs the following steps:
computes the / ratio;
if the source falls in the region defined as follows: / and 0.30 0.62 days, then the RR Lyrae star is identified as RRd.
The light curve modelling of a confirmed double-mode RR Lyrae star is then refined in module NonLinearDoubleModeModeling by applying the non-linear fitting procedure with the proper truncated Fourier series and fitting simultaneously the two pulsation modes. In a similar way, the period, the epoch, the peak-to-peak amplitudes and the Fourier decomposition are recomputed and refined. The RRLyraeDoubleModeSearch module of SOS Cep&RRL was tested but its results not yet included for Gaia DR1.
Blazhko effect search, position in the CMD & amplitude ratios
There are three modules in the RR Lyrae branch (BlazhkoEffectSearch, PositionInTheCMD and AmplitudeRatios) that could not be activated because in the Gaia DR1 we lack information to properly operate them. They are briefly described in the following.
The BlazhkoEffectSearch module searches for RR Lyrae stars affected by the Blazhko effect (Blazhko 1907), a periodic modulation of the amplitude and/or the phase of the main pulsation that occurs on timescales typically varying from a few days to hundreds of days. This phenomenon is shown by some 25-30% of the Galactic RRab and 5% of the RRc stars. However, recent detections of Blazhko stars with very small amplitudes suggest that these numbers may be underestimated (an occurrence rate as high as 50% follows from studies by Jurcsik et al., Jurcsik personal communication). Using data from future releases Gaia may provide accurate and updated occurrence rates. RV measurements and multi-band photometric data, to be obtained by Gaia with the foreseen long time base of observations, are fundamental to identify irregular and Blazhko variables among the RR Lyrae stars, and can help to understand whether they are due to nonradial modes excited during pulsation.
The PositionInTheCMD module verifies that sources classified as RR Lyrae stars fall inside the RR Lyrae instability strip (IS) in the CMD, by checking whether the colours of the targets are compatible with the IS for RR Lyrae stars. Gaia CMD is a fundamental tool to clean the sample of RR Lyrae stars by possible contaminating objects, since other types of variables falling in the same period domain, as for instance eclipsing binaries (ECLs), should lie predominantly outside the RR Lyrae IS, in this diagram. Knowledge of the source absolute magnitude (via parallax) and colour (or effective temperature) are needed to use this tool. Parallaxes are published in Gaia in DR1 only for stars with 12 mag, as part of the Tycho-Gaia Astrometric Solution (TGAS; Lindegren et al. 2016). Cepheids and RR Lyrae stars for which results of the SOS Cep&RRL processing are published in Gaia DR1 are fainter than this limit (see Section 3.1), hence, parallaxes are not yet available for them. This limited the use of the CMD tool, as it is described in Section 3.
The AmplitudeRatios module uses the peak-to-peak amplitude ratios between Gaia’s three different photometric bands to check whether the observed periodicity is due to a contact binary system mimicking an RR Lyrae-like light curve (this may be the case especially for RRc pulsators). In fact, amplitude ratios between different photometric bands are expected to be close to unity in the case of ECLs. Lacking , time-series, this tool could not be activated for Gaia DR1.
diagram and mode identification: ModeIdentification module
RRab and RRc types occupy different locations in the -band peak-to-peak amplitude versus period diagram (see Fig. 4) and in the - , and - planes (see Figs. 5 and 6). The ModeIdentification module combines results from these three diagnostics to identify the pulsation mode of candidate RR Lyrae stars and/or recognise misclassified objects. According to Fig. 4, RR Lyrae stars are confined within 0.2 d 1.0 d in period and within 0.05 mag Amp() 2.0 mag in -band amplitude. Sources outside these ranges will be flagged as possible misclassified objects. Furthermore, RRc and RRab types can then be separated with a line described by the equation: Amp(G) = 4.0 +2.2. Mixing of RRc and RRab pulsators occurs close to the separation line, and significant contamination also exists in Fig. 4 between RR Lyrae stars and ACEPs. However, they can both be reduced by using the star position in the Fourier and versus planes (Figs. 5 and 6) where the two types locate in separated regions, of which we defined the borders as discussed in Section 4.
RR Lyrae star classification
This module performs the final assignment of a source to the RR Lyrae class, based on results of the previous SOS Cep&RRL modules.
If the source is confirmed to be an RR Lyrae star, thus validating the initial class assignment provided by the general Classification pipeline,
the analysis proceeds with derivation of the stellar parameters
2.4 Cepheid branch
The processing of sources classified as Cepheid candidates by the general Classification pipeline occurs in the Cepheid branch first, the steps of which are schematically shown in Fig. 3. As with the RR Lyrae stars only some modules of the Cepheid branch could be used for the analysis of the Cepheids in Gaia DR1 . In particular, the first step of the Cepheid branch processing would be the identification of Cepheids in binary or multiple systems, using RV measurements obtained by the RVS and the ratios of photometric amplitudes in the different passband. Since neither RVs nor , photometry are available in Gaia DR1 this module was not activated. Similarly, we could not use the PositionInTheCMD module to check whether the candidate Cepheids fell inside the DCEP, ACEP and TCEP instability strips in the CMD. Analysis in the Cepheids branch starts from the search for multimode variables performed by the CepMultModeSearch module.
Identification of multi-mode Cepheids: CepMultiModeSearch module
The CepMultiModeSearch module identifies Cepheids pulsating simultaneously in different modes by checking the period ratios of the different periodicities found by the SecondaryPeriodicities module of the general trunk (Fig. 1). The relevant period ratios are: 1O over F (/ ), second-overtone (2O) over F ( /) and so on ( / ; /; / ). The typical values for these ratios are known both from empirical and theoretical studies. Allowed periods and period ratios for multi-mode DCEPs, following Soszynski et al. (2008a), are reported in Table 1.
The following different cases are considered, depending on the number of secondary periodicities found by the SecondaryPeriodicities module of the SOS Cep&RRL general trunk:
case 1: two periodicities are found
The algorithm sets to and the shorter and the longer periods, respectively. Then it checks whether the ratio / falls within the allowed ranges, as listed in Table 1.
If none of the above conditions is satisfied the source is rejected as multimode Cepheid.
case 2: three periodicities are found
The algorithm sets to , and the shorter, the intermediate, and the longer periodicity, respectively. Then it checks whether the / , / and / ratios are within the allowed ranges, as listed in Table 1.
The algorithm sets to , and the shorter, the intermediate, and the longer periodicity, respectively. Then it checks whether the / , / and / ratios are within the allowed ranges, as listed in Table 1.
If none of the above conditions is satisfied the source is rejected as multimode Cepheid.
The light curve modelling of a confirmed multi-mode Cepheid is refined in the NonLinearDoubleModeModeling module by applying the non-linear fitting procedure with the proper truncated Fourier series and fitting simultaneously all pulsation modes (generally two). Periodicities, epoch of maximum light, intensity-averaged mean magnitude, peak-to-peak amplitudes of the two/three pulsation modes and the parameters of the Fourier decomposition are also recomputed and refined taking into account all periodicities. The CepMultiModeSearch module of SOS Cep&RRL was tested but its results not yet included for Gaia DR1.
Identification of Cepheid type (DCEP, ACEP, T2CEP): CepTypeIdentification module
The module CepTypeIdentification combines pulsation and photometric (in the future also spectroscopic) characteristics of the candidate Cepheids computed in the previous modules of the SOS Cep&RRL processing (both in the general trunk and in the Cepheid branch) to subdivide them into DCEP, ACEP and T2CEP types. The module foresees the use of two main diagnostics: (i) the source metallicity that will be derived by the DPAC processing of RVS measurements, as DCEP and T2CEP/ACEP types show in general different metallicity content, being the DCEPs typically more metal-rich than the T2CEPs and ACEPs; (ii) the and relations, which are different for the three types of Cepheids. However, for Gaia DR1 the identification of the Cepheid types could rely only on the use of the -band relations, for which we used the following set of equations:
where is the absolute magnitude in the band. These relations were defined using Cepheids in the LMC for which light curves and pulsation characteristics have been published by Soszynski et al. (2008a, b), and adopting an absolute de-reddened distance modulus for the LMC of mag, from Pietrzynski et al. (2013). They are shown in Fig. 10.
Candidate Cepheids that fall within 4 from any of the above relations are assigned the Cepheid type and pulsation mode of the closest . Conversely, candidate Cepheids falling beyond 4 will be rejected as misclassified sources. T2CEPs are further subdivided in three separate classes: the BLHER class, the WVIR class and the RVTAU class, depending on the pulsation period. The three different subclasses of the T2CEP group follow different and relations and populate different period ranges. The module: T2CEPSubclassification identifies as BLHER the T2CEPs with period in the range 1 4 d; as WVIR the T2CEPs with period in the range 4 20 d; and as RVTAU the T2CEPs with period equal to or longer than 20 days (Soszynski et al. 2008b).
Single-mode DCEPs are known to pulsate in the F, 1O and 2O modes. Since F, 1O and 2O DCEP subclasses occupy different loci in
the versus Period diagram, the DCEPModeIdentification module assigns the
pulsation mode to a DCEP using the parameter of the light curve Fourier decomposition.
Specifically, considering the Fourier decomposition of the -band light
curve, and the vs diagram,
a DCEP will be pulsating in the 1O mode if the following conditions are verified:
0.234 d d
otherwise, it is assigned the F mode. These limits were inferred from the relations of DCEPs based on OGLE-III data.
In Section 4 we discuss and illustrate graphycally the regions we specifically defined in the and versus Period diagrams to separate Cepheids from RR Lyrae stars and to identify the Cepheid pulsation mode.
Similarly, ACEPs are known to pulsate in the F and 1O modes. The ACEPModeIdentification module assigns the pulsation mode to an ACEP by combining results from the classification in types based on the relations (module CepTypeIdentification) and the source period, as 1O ACEPs have periods in the range: 0.35 P 1.20 d, whereas F ACEPs have periods in the range: 1.20 P 2.5 d. These limits were inferred from relations of ACEPs based on OGLE-III data.
This module performs the final assignment of a source to the Cepheid class and its sub-classes, based on results of the previous SOS Cep&RRL modules.
If the source is confirmed to be a Cepheid, thus validating the initial class assignment provided by the general Classification pipeline,
the analysis proceeds with derivation of the stellar parameters
3 Application of the SOS-Cep&RRL pipeline to Gaia DR1 dataset
Results on Cepheids and RR Lyrae stars published in Gaia DR1 are based only on the application of the SOS Cep&RRL pipeline to Gaia -band time series photometry calibrated to the Vega magnitude system, as no / photometry is published in the Gaia DR1 and parallax and RV information are not yet available for these Cepheids and RR Lyrae stars which are fainter than the TGAS sample (Lindegren et al. 2016). This made it necessary to re-arrange and specifically tailor the SOS Cep&RRL pipeline to cope with the limited information available for the analysis of the Cepheid and RR Lyrae star candidates.
Processing of Gaia photometry takes place in an iterative manner organized through cycles (Evans et al. 2016). As detailed in Eyer et al. (2016), two datasets with different photometric calibration, time extent, and source number were used exceptionally, in order to be able to publish Cepheids and RR Lyrae stars in advance of schedule for the Gaia DR1. The first dataset included Cycle 0 (C0) photometry of millions of objects observed in the first 3 months after commissioning (from July 25, 2014), which were reduced by the general variable star processing to a sample of under 20K potential candidate Cepheids and RR Lyrae stars. This selection of sources was further analysed with the Gaia DR1 calibration photometry of Cycle 1 (C1), which spanned almost 14 months of observations.
The global variable star analysis performed for the Gaia first data release is schematically summarised in Fig. 11 which shows both the initial processing on the C0 dataset and the final processing of the C1 photometry released in Gaia DR1. The SOS Cep&RRL actual processing is detailed in Fig. 12 and specifically discussed in Section 3.2.
3.1 Dataset, source selection and initial processing
The dataset processed by the SOS Cep&RRL pipeline to produce results published in Gaia DR1 consists of -band time-series photometry
The characteristic geometric shape of the area covered by Gaia SEP observations is mainly due to the way the spacecraft scanned the sky during the first 28 days of science operation in EPSL when Gaia was kept to repeatedly monitor the two ecliptic poles. Therefore, sources very close ( deg) to the ecliptic poles may have time-series data with more than 200 observations, though quickly dropping off at distances further away. Gaia SEP footprint intercepts a peripheral portion of the LMC, offset by about 3 degrees to the North and 4 degrees to the East of the LMC center, that contains several Cepheids and RR Lyrae stars (as well as other types of variable stars) with characteristics known from the studies by the OGLE (Soszynski et al. 2012, 2015a, 2015b, 2015c, 2016 and references therein) and EROS-2 (according to Kim et al. 2014) surveys. They are shown as grey (RR Lyrae stars) and red (Cepheids) filled circles in Figs. 13 and 14.
Candidate Cepheids and RR Lyrae stars in the Gaia SEP most likely belong to the LMC, hence, to a first approximation, they are all the same distance from us. Due to the lack of parallaxes/distances to support the variability analysis of these sources at this early stage of the Gaia mission, this occurrence, along with the high cadence of the EPSL observations, were important assets that helped the SOS Cep& RRL processing for the first Gaia data release, which specifically focused on the 19,923 Cepheids and RR Lyrae star candidates within the Gaia SEP marked in blue in Figs. 13 and 14.
The scanning law determines the cadence of Gaia multi-epoch observations and, in turn, has a bearing on the alias patterns we may expect to show up in the power spectrum of Gaia time-series data. Particularly strong is the 6 hour alias caused by the spacecraft rotation around its axis, that during EPSL was always kept in the ecliptic plane and fixed with respect to the Gaia-Sun axis (see section 3 in Eyer et al. 2016). This is clearly seen in the two panels of Fig. 15 that show the period - -band amplitude distribution of the 19,923 candidate RR Lyrae stars and Cepheids in the Gaia SEP. Sources are plotted using the period computed by the Characterization workpackage of the general variability pipeline for Cepheid and RR Lyrae star candidates and OGLE’s period for the known variables. The figure also shows that only very few of the known Cepheids and RR Lyrae stars in this region of the LMC have -band amplitude smaller than 0.1 mag, hence suggesting that the large majority of candidates with such small amplitudes are misclassifications, lending support to the choice to not consider them any further (see Section 3.2). Test runs of the SOS Cep&RRL pipeline along with visual inspection of a randomly selected sample of the 19,923 SEP Cepheid and RR Lyrae star candidates during this initial stage of the processing helped to get a general overview of the dataset, improve the global analysis and fine tune the classifiers’ predictions. The visual inspection also confirmed a few (5) candidates that were later added to the final counts.
By excluding sources flagged as RR Lyrae star and Cepheid outliers in the predictions of the used classifiers the sample of automatically identified Cepheid and RR Lyrae star candidates reduces to 9,347 sources. Then further rejecting sources with -band amplitudes smaller than 0.1 mag (more than 3,800) but including the aforementioned 5 candidates identified by visual inspection, the number of SEP Cepheid and RR Lyrae star candidates drops to 6,714 sources. Of them 6,053 were recovered by cross-matching the C0 and C1 photometries. This forms the sample finally processed through the SOS Cep&RRL pipeline.
Statistical properties (time sampling, number of observations, time-series duration, mean/median magnitudes, magnitude uncertainties, etc.) of the -band C0 time-series photometry out of which the initial 19,923 enlarged sample of Cepheid and RR Lyrae star candidates analyzed by the SOS Cep&RRL pipeline was extracted are described in great detail in section 5.2 and figs.14, 15, 16, 17 of Eyer et al. (2016). Whereas, the characteristics of the -band C1 time-series photometry of the sample of 6,053 sources finally processed through the SOS Cep&RRL pipeline and turned into the 3,194 confirmed Cepheids and RR Lyrae stars released in Gaia DR1 are described in section 6.2, figs. 27, 28, 30, 31 and table 3 of Eyer et al. (2016).
We briefly remind here that this latter sample spans the -band magnitude range 12 20.3 mag, with typical errors on the order of a few milli-mag and 0.02 mag per individual datapoint at bright and faint ends, respectively, -bands amplitudes in the range from 0.1 and 1.5 mag, and between 15 and over 200 phase-point sampling of the light curves (see text and figures in Section 4 for details).
Integrated blue () and red () spectrophotometry from the blue and red photometers on board Gaia is not released in Gaia DR1. This additional limitation of the Gaia DR1 dataset means we could not build the Gaia CMD of the SEP Cepheid and RR Lyrae star candidates. However, we built a -band versus CMD, shown in Fig. 16, using OGLE-IV’s GSEP catalogue of LMC RR Lyrae stars (Soszynski et al. 2012) and OGLE-IV’s catalogues of LMC Cepheids published by Soszynski et al. (2015b, c). The mean magnitudes published by OGLE were transformed to the Gaia -band using eq. (A.1). This gave us some insight on the range in magnitude spanned by RR Lyrae stars and Cepheids in the Gaia SEP (see Section 3.2).
The C1 time-series photometry of the 6,053 Cepheid and RR Lyrae star candidates, selected as described in Section 3.1,
was fed into the SOS Cep&RRL pipeline.
As a first step sources were cross-matched against catalogues of known RR Lyrae stars and Cepheids existing in the literature. We primarily used
the OGLE-III (Soszynski et al. 2009) catalogue for RR Lyrae stars
3.2 Tailoring the SOS Cep&RRL pipeline for the analysis of the SEP data
Given the limitations of the first Gaia data, the SOS Cep&RRL pipeline had to be properly tailored to successfully process the Gaia DR1 data. This occurred through a number of assumptions and simplifications which impacted significantly the pipeline effectiveness and enhanced the need for visual inspections and manual intervention to check and adjust results (see Fig. 12). Specifically,
Lacking parallaxes (distances) for the stars in the sample we assumed that the SEP sources were all the same distance from us, which is an acceptable first approximation given that they are located primarily in the LMC, and we adopted a cut in apparent magnitude to initially separate Cepheids from RR Lyrae stars. A threshold was set using the CMD in Fig. 16 by which we assumed that candidates with 18.5 mag are more likely RR Lyrae stars and after the general trunk (Fig. 1) they were sent to the RR Lyrae branch (Fig. 2) of the SOS Cep&RRL pipeline. Conversely, candidates with 18.5 mag were sent to the Cepheid branch (Fig. 3). However, since the SEP sample might include also Galactic RR Lyrae stars projected against the LMC, candidates brighter than 18.5 mag, with d and -band amplitude larger than 0.5 mag were analyzed in both Cepheid and RR Lyrae branches, as the two types overlap in this region of the parameter space. A fine tuning of the Fourier parameters and definition of strict loci in the vs and vs planes ( see Section 4) were adopted to distinguish bright MW RR Lyrae stars from Cepheids.
Lacking source colours, the classification of Cepheids in different types and pulsation modes could only rely on the relations, complemented by visual inspection of the Fourier planes. Furthermore, without colours, the contaminations by ECLs (both for Cepheids and RRc) and by Scuti (particularly at the faint end of the LMC RR Lyrae star distribution, 20.0 mag) were issues that only the visual inspection of the light curves could alleviate.
The limited time span covered by the Gaia DR1 data, whose bulk spans roughly 28 d, made the measure of period most reliable only for Cepheids with periods shorter than about 10 d. In addition, the strong aliases affecting the SEP data required visual inspection of the light curves and manual intervention (period or type tagging of sources) to solve ambiguous cases.
When , colours, parallaxes and an increasingly complete dataset will become available starting already with Gaia DR2 in 2017, the need for visual inspection and manual intervention will definitely decrease.
For known Cepheids and RR Lyrae stars the periods measured by the SOS Cep&RRL pipeline (hereinafter, ) were compared with the literature values. Agreement was deemed satisfactory if differences
were within 0.01 d for the Cepheids and 0.001 d for RR Lyrae stars.
When differences exceede these limits visual inspection of the light curve folded according to the two different periods helped to decide which
periodicity to finally adopt
Type and mode classification of the known RR Lyrae stars and Cepheids assigned by the SOS Cep&RRL pipeline were also cross-checked against literature. A number of obvious type misclassifications were emended by manual adjustment.
After processing through the SOS Cep&RRL pipeline the light curves of all 6,063 candidates were visually inspected for data quality assurance, validation of the modelled light curve and quantities thereby derived (period, peak-to-peak amplitude, mean magnitude, etc.) and final assessment of the Cepheids and RR Lyrae stars to be published in Gaia DR1.
The run of the SOS Cep&RRL pipeline on the 6,053 candidates combined with visual inspection of the resulting light curves produced a final sample of 3,602 confirmed Cepheids and RR Lyrae stars and rejection of a total number of 2,451 sources. This quite large number of rejections is due to various reasons among which are: candidates identified in C0 which are not present in C1 photometry, photometry issues in the time series data among which very large outliers and/or systematically noisy light curves. However, a major and most important reason for rejection was incomplete sampling of the light curves resulting in unreliable values for the source period, peak-to-peak amplitude and mean magnitude. Finally, further rejections were also due to contamination of the sample by ECLs, and other types of variables ( Scuti, LPVs, etc.). We explicitly note that a large fraction of these rejections (over 50%) are in fact bona fide RR Lyrae stars and Cepheids for which unambiguous determinations of period and/or type classification could be obtained, or are RR Lyrae stars and Cepheids that did not pass the strict quality control criteria we have adopted to validate these first variability results from Gaia. During quality assessment, it was also decided not to release results for double/multi-mode sources (239 in total between RR Lyrae stars and Cepheids) as they were not fully compliant with our internal validation criteria. These are quite strict to ensure a high reliability of the information for variable sources released in Gaia DR1, notwithstanding the actual limitations of the available dataset. By rejecting double-mode RR Lyrae stars and multi-mode Cepheids the sample reduced to 3,363 sources. Finally, 169 additional sources, out of the 3,363 sample, were rejected during the final validation process which was meant to achieve consistency on the total number of sources finally released by different tasks of the DPAC processing contributing results for Gaia DR1.This further trimming lead to a final sample of 3,194 Cepheids and RR Lyrae stars.
Position, -band time series photometry and final results of the SOS Cep&RRL pipeline are published in Gaia DR1 for a total of 3,194 sources. They comprise 599 Cepheids (of which 43 are new from Gaia) and 2,595 RR Lyrae stars (of which 343 are new discoveries). The subdivision of the 3,194 sources according to type, subtype and pulsation mode is summarized in Table 2.
|RR Lyrae Total||2595||343||2252|
For these 3,194 sources the following parameters, computed by the SOS Cep&RRL pipeline, are released in Gaia DR1 along with the related errors:
- source pulsation period
- intensity-averaged mean magnitude
- epoch of maximum light
- and Fourier parameters
- peak-to-peak -band amplitude [Amp()]
- RR Lyrae star subclassification into RRab and RRc types
- Cepheid classification into DCEP, ACEP, T2CEP types
- DCEPs and ACEPs pulsation mode (F, 1O)
- T2CEPs sub-classification into BLHER, WVIR, RVTAU types.
Gaia’s sourceids, coordinates, values of the above quantities and associated statistics, along with the -band time series for each of the 3,104 sources can be retrieved from Gaia first data release archive: http://archives.esac.esa.int/gaia/ and other distribution nodes. The archives also provide tools for queries and to crossmatch Gaia data with other catalogues available in the literature.
We provide in Table 3 specific links to the archive tables as well as summarise names of the parameters, computed by SOS Cep&RRL that can be retrieved from the archive tables. In Section B we provide queries to retrieve the various quantities and parameters listed in Table 3.
|-band time series data|
|Table Content||G-band FoV averaged photometry: observation_time,|
|g_flux, g_flux_error, g_magnitude,|
|Cepheid parameters computed by the SOS Cep&RRL pipeline|
|(one of DCEP, T2CEP or ACEP)|
|(one of FUNDAMENTAL, FIRST_OVERTONE,|
|SECOND_OVERTONE, or UNDEFINED, only for DCEP or ACEP)|
|(BL_HER, W_VIR, or RV_TAU, only for T2CEP)|
|RR Lyrae star parameters computed by the SOS Cep&RRL pipeline|
|Type||best_classification (one of RRC or RRAB)|
and distributions of the total sample of 3,194 sources are shown in Figs. 18 and 19, whereas Figs. 21 and 22 show their distribution in the Fourier vs and vs planes, respectively, according to the period determined by the SOS Cep&RRL pipeline (). The MW RR Lyrae stars are clearly recognized in Fig. 19 for 1 d. This figure also shows the overlap between RR Lyrae stars, DCEPs and ACEPs, occurring in the short period regime ( 1d). Of the 3,194 Cepheids and RR Lyrae stars published in Gaia DR1 2,808 are already known. Fig. 20 shows the comparison with the literature. The comparison is done primarily with OGLE, for which there is the largest number of sources in common (2,659 out of the 2,808 known RR Lyrae stars and Cepheids). Other surveys, among those listed at the end of Section 3.1 were considered in case an OGLE classification was not available. Since contrasting classifications have been manually checked during the processing the number of sources in Fig. 20 for which the SOS Cep&RRL classification differs from the literature is limited and reflects actual differences resulting from the analysis based on Gaia data.
Examples of light curves for RR Lyrae stars and Cepheids released in Gaia DR1 are presented in Figs. 23 and Figs. 24. Light curves are folded according to period and epoch of maximum light determined by the SOS Cep&RRL pipeline. The full atlas of light curves is available in the electronic edition of the journal. Samplers are shown in Sections C (Figs. 38, 39, 40, 41, 42, 43) and D (Figs. 44, 45), for Cepheids and RR Lyrae stars, respectively. New discoveries by Gaia are labelled.
DCEPs with periods ranging from about 6 to 16 d show a secondary maximum (bump) in their light and RV curves, which position varies with the pulsation period (see Bono et al. 2000 for details). This phenomenon is called Hertzsprung progression (Hertzsprung 1926; Ledoux & Walraven 1958).
In the case of Galactic DCEPs the bump appears on the descending branch of the light and RV curves for d, close to maximum light for d, and along the rising branch for longer periods. The period at the center of the Hertzsprung progression varies with metallicity moving to longer periods as metallicity decreases (Marconi et al. 2005; Soszynski et al. 2011a). Fig. 25 shows the -band light curves for a subset of DCEPs that exhibit the Hertzsprung progression. The center of the progression for these DCEPs is close to 9 days, as with the MW DCEPs, thus possibly suggesting that their metallicity is higher than for the bulk of the LMC Cepheids.
Figs. 26 and 27 show, respectively, the number of observations available for the 2,595 SEP RR Lyrae stars and the 599 Cepheids in Gaia DR1. The distribution peaks between 40 and 70 with maximum around 50 transits for the RR Lyrae stars and between 60 and 80 with maximum around 70 transits for the Cepheids. Hence, these SEP sources provide a reasonably realistic representation of the typical number of observations per source that will be available on average at end of the five-year nominal life-time of the Gaia mission, although with a totally different cadence. However, we note that for the specific case of regular, periodic objects with typical periods of RR Lyrae stars and Cepheids and over a large time interval (e.g. the five-year time-span), the sparser NSL cadence being less prone to aliasing than the EPSL cadence, which sampling is too regular, may turn into an advantage for period derivation.
Figs. 28 and 29 show, the period distributions of the RR Lyrae stars and Cepheids in Gaia DR1. In both figures the light-blue shaded areas correspond to new discoveries by Gaia. New and known variables follow very similar distributions. The RR Lyrae stars show the typical bimodal distribution corresponding to RRc and RRab pulsators. The period distribution of the RRab stars peaks at d and confirms a predominantly Oosterhoff-intermediate nature of the LMC RR Lyrae stars (Oosterhoff 1939, Catelan 2009 and references therein). However, a minor component of Oosterhoff type II RRab stars is also present, which defines a second less populated sequence in the diagram shown in Fig. 30.
Fig. 31 shows the distribution of the 2,595 SEP RR Lyrae stars. The LMC RR Lyrae stars in the Gaia SEP appear to typically have 19.0 mag with a dispersion of about 0.5 mag. This rather large dispersion is due to the combination of intrinsic width, reddening, metallicity effects, LMC geometric depth and also to the extended throughput of Gaia -band (330 - 1050 nm), which makes the RR Lyrae star relation to have a significant span in the -band (Gaia Collaboration et al. 2016b). On the other hand, the over six magnitudes scatter seen in Fig. 31 for 18 mag is largely due to Galactic RR Lyrae stars that fall within the Gaia SEP footprint and contaminate the LMC sample.
Fig. 32 shows the spatial distribution of the 2,595 RR Lyrae stars in Gaia DR1 overlaid on the sample of LMC RR Lyrae stars recently released by OGLE-IV (Soszynski et al. 2016, orange filled circles). Marked in green are RR Lyrae stars observed by Gaia that are in common with other surveys listed at the end of Section 3.1. About 700 of them were new discoveries by Gaia that were in the meantime identified also by OGLE (Soszynski et al. 2016). Red filled circles are 343 new RR Lyrae stars discovered by Gaia, they mainly belong to the LMC and allow to cast a first glance on the very extended halo of this galaxy.
Also particularly interesting is the distribution in average magnitude of the 2,595 RR Lyrae stars in the Gaia SEP. This is shown in Fig. 33, where we have sliced the sample into three magnitude bins. The top panel shows 73 RR Lyrae stars with 18.5 mag, sixty-three of them have 17.5 mag and 24 in this brighter sample are new discoveries by Gaia. All RR Lyrae stars in this panel likely belong to the MW halo in front of the LMC. The central panel shows 2,375 RR Lyrae stars with 18.5 19.5 mag. The average apparent magnitude of this sample is = 19.13 mag, with = 0.16 mag, this value is typical of the LMC RR Lyrae stars. They are quite homogeneously distributed and trace the LMC halo (see Fig. 32). The bottom panel shows 147 RR Lyrae stars with 19.5 mag, they follow the clumped distribution of the LMC Cepheids in this area (see Fig. 35) and are fainter than the average of LMC RR Lyrae stars likely because are more affected by reddening, which is expected to be higher in the dusty regions populated by Cepheids. This figure very nicely showcases the potential of Gaia and variable stars to study galactic structure.
Finally, Figs. 34 and 35 show, respectively, the relation and the distribution on sky of the 599 Cepheids in Gaia DR1. The various types of Cepheids in the sample clearly define different relations. They will be used to identify and classify different types of Cepheids in future Gaia data releases (see Section 5).
Red filled circles in Fig. 35 mark 43 new Cepheids discovered by Gaia. This sample comprises 25 DCEPs, 10 ACEPs and 8 T2CEPs, which are located outside the OGLE-IV footprint and at increasing distance from the LMC. According to the position on the relation shown in Fig. 34 they belong to the LMC. However, it is premature to draw any conclusions on the LMC structure from this fairly small sample of new Cepheids, which might also be contaminated at 3% level by ECLs (see Moretti et al. 2014), and by other types of variables (e.g. ellipsoidals or spotted stars) that populate the same period, period-luminosity, period-amplitude, and Fourier parameter domain. Whether some of these misclassifications may have actually occurred, cannot be judged from Gaia DR1 data alone. This will become possible and checks will certainly be done, if/where appropriate, now that we have started the re-processing of all sources in preparation of Gaia DR2. For Gaia second data release our analysis will also be easier because other parts of the CU7 pipeline will be activated, those specifically devoted to binaries of different types (among which also ellipsoidals) and to spotted stars in particular. Furthermore, for Gaia DR2 we will also have , time series available to help us disentangle Cepheids and RR Lyrae stars from other types of variables. Hence, we leave any further discussion of this sample for Gaia DR2 when analysis of Cepheid and RR Lyrae candidates will be extended beyond the SEP footprint and , photometry will be used to help with the Cepheid classification.
5 Conclusions and future developments
We have presented an overview of the Specific Objects Study (SOS) pipeline SOS Cep&RRL, developed in the context of Coordination Unit 7 (CU7) of the Gaia Data Processing and Analysis Consortium (DPAC), to validate and fully characterise Cepheids and RR Lyrae variables observed by the spacecraft. The SOS Cep&RRL pipeline was specifically tailored to analyse the Gaia -band time-series photometry of sources in the South Ecliptic Pole (SEP) footprint, which covers an external region of the LMC, and to produce results for confirmed RR Lyrae stars and Cepheids to be released with Gaia Data Release 1 (Gaia DR1).
Results presented in this paper have been obtained applying the whole variable star analysis pipeline on the time-series photometry collected by Gaia during 28 days of Ecliptic Pole Scanning Law (EPSL) and 13 months Nominal Scanning Law (NSL). In addition to positions and -band time-series photometry, for confirmed Cepheids and RR Lyrae stars in the Gaia SEP, Gaia DR1 contains the following outputs of the SOS Cep&RRL pipeline: period of pulsation, classification in type and pulsation mode, intensity-averaged mean magnitude, peak-to-peak amplitude and Fourier decomposition parameters and . All quantities are provided with related uncertainties. The variable star inventory of Gaia DR1 includes 3,194 variables which comprise 599 Cepheids and 2,595 RR Lyrae stars, 386 of them (43 Cepheids and 343 RR Lyrae stars) are new discoveries by Gaia.
The published sources are distributed over an area extending 38 degrees on either side from a point offset from the centre of the LMC by about 3 degrees to the north and 4 degrees to the east. The vast majority, but not all, are located within the LMC. The sample also includes 63 bright RR Lyrae stars that belong to the MW halo, of which 24 are new Gaia discoveries.
A number of improvements of the SOS Cep&RRL pipeline are planned in view of Gaia forthcoming releases, of which the next one, Gaia DR2, is foreseen in 2017. They include:
Check of the pass-band transformations. In preparation of Gaia DR2, all tools and relations used by SOS Cep&RRL to classify and characterise the Gaia sources will be re-derived directly from Cepheids and RR Lyrae stars released in DR1, overcoming the need for transforming to Gaia pass-bands quantities that are generally known in the Johnson-Cousins system. This will definitely allow the relationships adopted to identify and classify different types of Cepheids to be refined. On the other hand, as shown by Fig. 37 in Section A, the -band transformation (eq. (A.1)) worked rather well for the colour range spanned by the Cepheids and RR Lyrae stars in Gaia DR1.
Gaia , colours will become available with Gaia DR2. This will allow to use relations, whose reduced scatter, compared to the relations, will allow to further improve the classification of Cepheids.
Double-mode RR Lyrae stars and multimode classical Cepheids (F/1O, 1O/2O, etc.) will be identified and fully characterised for Gaia DR2 by improving the detection algorithm to properly take into account the scatter in the folded light curve, thus reducing the number of false positives.
Estimate of the error in period, mean magnitude, peak-to-peak amplitude, etc. will be refined. In particular, errors in the Fourier parameters and , which are currently computed by propagation of the errors in , , and , will be entirely computed via Monte Carlo simulations.
A classifier will be developed to optimise the type and subtype classification of Cepheids and RR Lyrae stars performed by the SOS Cep&RRL pipeline.
The results for Cepheids and RR Lyrae stars shown in this paper demonstrate the excellent quality of Gaia photometry notwithstanding all limitations in the dataset and processing for Gaia Data Release 1. They nicely showcase the potential of Gaia and the promise of Gaia Cepheids and RR Lyrae stars for all areas of the sky in which an appropriate light curve sampling will be achieved.
Acknowledgements.This work has made use of data from the ESA space mission Gaia, processed by the Gaia Data Processing and Analysis Consortium (DPAC). Funding for the DPAC has been provided by national institutions participating in the Gaia Multilateral Agreement. In particular, the Italian participation in DPAC has been supported by Istituto Nazionale di Astrofisica (INAF) and the Agenzia Spaziale Italiana (ASI) through grants I/037/08/0, I/058/10/0, 2014-025-R.0, and 2014-025-R.1.2015 to INAF (PI M.G. Lattanzi), the Belgian participation by the BELgian federal Science Policy (BELSPO) through PRODEX grants, the Swiss participation by the Swiss State Secretariat for Education, Research and Innovation through the ESA Prodex program, the “Mesures d’accompagnement”, the “Activités Nationales Complémentaires”, the Swiss National Science Foundation, and the Early Postdoc.Mobility fellowship, and the Spanish participation by the Spanish Ministry of Economy MINECO-FEDER through grants AyA2014-55216, AyA2011-24052. UK community participation in this work has been supported by funding from the UK Space Agency, and from the UK Science and Technology Research Council. The Gaia mission website is: http://www.cosmos.esa.int/gaia.
Appendix A Conversion formula
In this section we present the conversion formula, appropriate for the colour and metallicity ranges of Cepheids and RR Lyrae stars ( 2.5 mag and [Fe/H] 0.5 dex), we have used to transform the Johnson-Cousins , to the Gaia pass-band. This formula was computed using pass-band transformations provided in Jordi et al. (2010) and subsequent updates (Jordi, personal communication).
The relation is shown by the red solid line in Fig. 36.
It should be noted that the above conversion formula between Gaia and Johnson-Cousins , passbands is based on the pre-launch nominal Gaia passband, hence it might not be perfect. Nevertheless, Fig. 37 shows this transformation to have worked rather well for Cepheids and RR Lyrae stars that are published in Gaia DR1. In the figure we have plotted the mean magnitude computed by the SOS Cep&RRL pipeline for 2,659 sources (510 Cepheids and 2,149 RR Lyrae stars) in common with OGLE-IV versus the source colours from OGLE. Differences are, on average, within 0.1 mag.
New transformations have now been computed using Gaia real data (Gaia Collaboration et al. 2016d). The reader is advised to use the new formulae to make pass-band transformations. As far as future Gaia releases are concerned, Cepheids and RR Lyrae stars published in Gaia DR1 will be used to compute directly in the Gaia -band the and relations used in the SOS Cep&RRL pipeline to classify these types of variable stars.
Appendix B Examples of Gaia archive queries
|Query to retrieve time series of all Cepheids in the Gaia DR1.
select gaia.source_id, gaia.observation_time, gaia.g_flux, gaia.g_flux_error, gaia.g_magnitude, gaia.rejected_by_variability_processing from gaiadr1.phot_variable_time_series_gfov as gaia inner join gaiadr1.cepheid as cep on gaia.source_id=cep.source_id
|Query to retrive time series of all RR Lyrae stars in the Gaia DR1.
select gaia.source_id, gaia.observation_time, gaia.g_flux, gaia.g_flux_error, gaia.g_magnitude, gaia.rejected_by_variability_processing from gaiadr1.phot_variable_time_series_gfov as gaia inner join gaiadr1.rrlyrae as rr on gaia.source_id=rr.source_id
|Query to retrieve the number of processed observations and SOS Cep&RRL-computed parameters of all Cepheids in the Gaia DR1.
select cep.source_id, num_observations_processed, type_best_classification, mode_best_classification, type2_best_sub_classification, p1, p1_error, num_harmonics_for_p1, epoch_g, epoch_g_error, int_average_g, int_average_g_error, peak_to_peak_g, peak_to_peak_g_error, phi21_g, phi21_g_error, r21_g, r21_g_error from gaiadr1.phot_variable_time_series_gfov_statistical_parameters as stat inner join gaiadr1.cepheid as cep on stat.source_id=cep.source_id
|Query to retrieve the number of processed observations and SOS Cep&RRL-computed parameters of all RR Lyrae in the Gaia DR1.
select rr.source_id, num_observations_processed, best_classification, p1, p1_error, num_harmonics_for_p1, epoch_g, epoch_g_error, int_average_g, int_average_g_error, peak_to_peak_g, peak_to_peak_g_error, phi21_g, phi21_g_error, r21_g, r21_g_error from gaiadr1.phot_variable_time_series_gfov_statistical_parameters as stat inner join gaiadr1.rrlyrae as rr on stat.source_id=rr.source_id
Appendix C Atlas of the -band light curves for Cepheids
In this Section we present the -band light curves of 599 Cepheids observed by Gaia in the SEP region. The light curves are folded according to the period and epoch of maximum light computed by the SOS Cep&RRL pipeline and are plotted grouping the Cepheids by type and pulsation mode according to the following order: DCEPs 1O (230 in total), DECPs F (313), DCEPs NoMode (15 DCEPs for which we did not identify the pulsation mode); ACEPs 1O (7 sources), ACEPs F (6), ACEPs NoMode (3); T2CEPs BLHER (11 sources), T2CEPs WVIR (10), T2CEPs RVTAU (2) and T2CEPs NoMode (2). In each group the sources are desplayed in order of increasing period.
Appendix D Atlas of the -band light curves for RR Lyrae stars
In this Section we present the -band light curves of 2,595 RR Lyrae stars observed by Gaia in the SEP region. The light curves are folded according to the period and epoch of maximum light computed by the SOS Cep&RRL pipeline and are plotted grouping the RR Lyrae stars according to the pulsation mode, with 685 RRc stars first, followed by 1,910 RRab stars. In each group the sources are desplayed in order of increasing period.
- institutetext: INAF-Osservatorio Astronomico di Bologna, Via Ranzani 1, I - 40127 Bologna, Italy
- email: email@example.com
- institutetext: INAF-Osservatorio Astronomico di Capodimonte, Salita Moiariello 16, I - 80131 Napoli, Italy
- institutetext: Department of Astronomy, University of Geneva, Ch. des Maillettes 51, CH-1290 Versoix, Switzerland
- institutetext: Department of Astronomy, University of Geneva, Ch. d’Ecogia 16, CH-1290 Versoix, Switzerland
- institutetext: Konkoly Observatory, Research Centre for Astronomy and Earth Sciences, Hungarian Academy of Sciences, H-1121 Budapest, Konkoly Thege M. ut 15-17, Hungary
- institutetext: SixSq, Rue du Bois-du-Lan 8, CH-1217 Geneva, Switzerland
- institutetext: Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK
- institutetext: Royal Observatory of Belgium, Ringlaan 3, B-1180 Brussels, Belgium
- institutetext: Institute of Astronomy, KU Leuven, Celestijnenlaan 200D, 3001 Leuven, Belgium
- institutetext: Dpto. Inteligencia Artificial, UNED, c/ Juan del Rosal 16, 28040 Madrid, Spain
- An independent search for the best period and best-fit model calculations will be performed on the , and RV data, when they become available. Peak-to-peak amplitudes and amplitude ratios for all photometric bands will be computed and an internal consistency check among periods from different pass-bands will be performed by the periodCheck module (see Fig. 1). This module was not activated for DR1.
- Fourier parameters will be computed in the future also for the , light curves and for the RV curve to perform consistency checks.
- Only exceptions are the errors of the Fourier parameters and which are currently computed by propagation of the errors in , , and , the latter being computed via Monte Carlo simulations.
- The - conversions are particularly valuable since the OGLE team usually publishes results for RR Lyrae stars and Cepheids in the band.
- This occurs through the module StellarParametersDerivation still partially under development, by which stellar intrinsic parameters are derived through a variety of methods specifically appropriate for RR Lyrae stars. These include: radius estimates via Baade-Wesselink analysis (see, e.g., Cacciari et al. 1989, 1992, and references therein); metallicity estimates from the parameter of the Fourier light curve decomposition; reddening estimates from the light and colour curves; and mass estimates for double-mode pulsators through the Petersen diagram and the pulsation equation (Di Criscienzo et al. 2004) for single-mode pulsators. No specific tool to detect RR Lyrae stars in binary systems has been implemented in the pipeline yet, as binary RR Lyrae stars are an extremely rare event, only one has been firmly established so far (Wade et al., 1999), less than two dozen candidates are reported in total by Hajdu et al. (2015) and Liska et al. (2015) but none of them has been spectroscopically confirmed yet.
- This occurs in the module StellarParametersDerivation. This module is under development and includes: radius estimates via Baade-Wesselink analysis (see, e.g. Barnes & Evans 1976, Ripepi et al. 1997); metallicity estimates for DCEPs from the and parameters of the Fourier light curve decomposition; identification of binary Cepheids from astrometry and RVS measurements and determination of their orbital elements; DCEP mass and age estimates.
- Each point in the -band time-series is the mean of the 9 CCD measurements collected during one observation of a source by Gaia.
- The OGLE-IV catalogue of the LMC RR Lyrae stars (Soszynski et al. 2016) became available after the SOS Cep&RRL processing and been finished with discovery of over 1,000 new LMC RR Lyrae stars, of which 2/3 turned out to be in common with Soszynski et al. (2016).
- For new RR Lyrae stars and Cepheids discovered by Gaia, such a check was performed by comparing P and the period computed by Characterization in the general variable star analysis pipeline.
- With the terms NoMode and NoSubType, we have flagged sources for which the pulsation mode (for DCEPs and ACEPS) or the subtype (for T2CEP) could not be automatically and univocally identified by the SOS Cep&RRL pipeline.
- The BJD of the epoch of maximum light is offset by JD 2455197.5 d (= J2010.0).
- Bailey, S. I. 1902, Annals of Harvard College Observatory, 38, 1
- Barnes, T. G., & Evans, D. S. 1976, MNRAS, 174, 489
- Berdnikov, L. N., Kniazev, A. Y., Sefako, R., Kravtsov, V. V., & Zhujko, S. V. 2014, Astronomy Letters, 40, 125
- Berdnikov, L. N., & Turner, D. G. 1995, Pis’ma Astr. Zhurnal, 21, 803
- Berdnikov, L. N., Vozyakova, O.V., Kniazev, A. Y., et al. 2012, Astronomy Reports, 56, 290
- Blazhko, S. 1907, Astr. Nachr., 175, 325
- Bono, G., Marconi, M., & Stellingwerf, R. F. 2000, A&A, 360, 245
- Cacciari, C., Clementini, G., Prevot, L., & Buser, R. 1989, A&A, 209, 141
- Cacciari, C., Clementini, G., & Fernley, J. A. 1992, ApJ, 396, 219
- Cacciari, C., Corwin, T. M., & Carney, B. W. 2005, AJ, 129, 267
- Carrasco, J., et al., 2016, A&A, DPACP-9 (to be updated)
- Catelan, M. 2009, Ap&SS, 320, 261
- Clementini, G. 2016, in Communications from the Konkoly Observatory of the Hungarian Academy of Sciences Vol. 105, p. 3, RR Lyrae 2015 - High-Precision Studies of RR Lyrae stars, ed. L. Szabados, R. Szabo, & K. Kinemuchi
- Cohen, J.G., Sesar, B., Banholzer, S., et al. 2015, in IAU Symposium 317, Global Properties of Stellar Halos From the Milky Way to External Galaxies, in press, (arXiv:1509.05997)
- Coulson, I. M., & Caldwell, J. A. R. 1985, SAAO Circulars, 9, 5
- Coulson, I. M., Caldwell, J. A. R., & Gieren, W. P. 1985, ApJS, 57, 595
- de Bruijne, J. H. J., Rygl, K. L.J., Antoja, T. 2014, EAS Publ. Ser., 67, 23
- Di Criscienzo, M., Marconi, M., & Caputo, F., 2004, ApJ, 612, 1092
- Drake, A.J., Catelan, M., Djorgovski, S.G., et al. 2013, ApJ, 763, 32
- Eyer, L., Mowlavi, N., Ewans, D.W., et al. 2016, A&A, DPACP-15 (To be updated)
- Eyer, L., Palaversa, L., Mowlavi, N., et al. 2012, Ap&SS, 341, 207
- Evans, D. et al. 2016, A&A, DPACP-11 (To be updated)
- Gaia Collaboration, Brown, A.G.A., Vallenari, A., Prusti, T., et al. 2016a, A&A, in press, (arXiv:1609.04172)
- Gaia Collaboration, Clementini, G., Eyer, L., Ripepi, V., et al. 2016b, A&A, DPACP-23 (To be updated)
- Gaia Collaboration, Prusti, T., de Bruijne, J.H.J., et al. 2016c, A&A, in press, (arXiv:1609.04153)
- Gaia Collaboration, van Leeuwen, F., et al. 2016, A&A, DPACP-24 (To be updated)
- Gieren, W. 1981, ApJS, 47, 315
- Hajdu, G., Catelan, M., Jurcsik, J., et al. 2015, MNRAS, 449, L113
- Hernitschek, N., Schlafly, E.F., Sesar, B., et al. 2016, ApJ, 817, 73
- Hertzsprung, E. 1926, Bull. Astron. Inst. Netherlands, 3, 115
- Jordi, C., Gebran, M., Carrasco, J. M., et al. 2010, A&A, 523, 48
- Jurcsik, J. & Kovács, G. 1996, A&A, 312,111
- Kim, D.-W., Protopapas, P., Bailer-Jones, C. A. L., et al. 2014, A&A, 566, 43
- Ledoux, P., & Walraven, T. 1958, Handbuch der Physik, 51, 353
- Levenberg, K. 1944, Quarterly of Applied Mathematics, 2, 164-168
- Lindegren, L., Lammers, U., Bastian, U., et al. 2016, A&A, in press, (arXiv:1609.04303)
- Liska, J., Skarka, M., Zejda, M., et al. 2015, MNRAS, 459, 4360
- Lomb, N. R. 1976, Ap&SS, 39, 447
- Marconi, M., Musella, I., & Fiorentino, G. 2005, ApJ, 632, 590
- Marquardt, D. W. 1963, SIAM Journal on Applied Mathematics, 11, 431
- Moffett, T. J., & Barnes, T. G. 1984, ApJS, 55, 389
- Moretti, M. I., Clementini, G., Muraveva, T. et al., 2014, MNRAS, 437, 2702
- Oosterhoff, P.T. 1939, Observatory 62, 104
- Osuna, P., Ortiz, I., Lusted, J., et al. 2008, IVOA Astronomical Data Query Language Version 2.00, IVOA Recommendation 30 October 2008
- Petersen, J. O. 1973, A&A, 27, 89
- Pietrzyński, G., Graczyk, D., Gieren, W., et al. 2013, Nature, 495, 76
- Pojmański, G. 1997, Acta Astron., 47, 467
- Poleski, R., Soszyński, I., Udalski, A., et al., 2010, Acta Astron., 60, 179
- Riello, M., et al., 2016, A&A, DPACP-10 (to be updated)
- Ripepi, V., Barone, F., Milano, L., & Russo, G. 1997, A&A, 318, 797
- Scargle, J. D. 1982, ApJ, 263, 835
- Sesar, B., Ivezić, Z., Stuart, J.S., et al. 2013, AJ, 146, 21
- Soszyński, I., Poleski, R., Udalski, A., et al., 2008a, Acta Astron., 58, 163
- Soszyński, I., Udalski, A., Szymański, M. K., et al., 2008b, Acta Astron., 58, 293
- Soszyński, I., Udalski, A., Szymański, M. K., et al. 2009, Acta Astron., 59, 1
- Soszyński, I., Poleski, R., Udalski, A., et al., 2010a, Acta Astron., 60, 17
- Soszyński, I., Udalski, A., Szymański, M. K., et al., 2010b, Acta Astron., 60, 91
- Soszyński, I., Udalski, A., Szymański, M. K., et al., 2010c, Acta Astron., 60, 165
- Soszyński, I., Dziembowski, W. A., Udalski, A., et al., 2011a, Acta Astron., 61, 1
- Soszyński, I., Udalski, A., Pietrukowicz, P., et al. 2011b, Acta Astron., 61, 285
- Soszyński, I., Udalski, A., Poleski, R., et al. 2012, Acta Astron., 62, 219
- Soszyński, I., Udalski, A., Szymański, M. K., et al., 2015a, Acta Astron., 65, 233
- Soszyński, I., Udalski, A., Szymański, M. K., et al., 2015b, Acta Astron., 65, 297
- Soszyński, I., Udalski, A., Szymański, M. K., et al., 2015c, Acta Astron., 65, 329
- Soszyński, I., Udalski, A., Szymański, M. K., et al. 2016, Acta Astron., 66, 131
- Torrealba, G., Catelan, M., Drake, A. J., et al. 2015, MNRAS, 446, 2251
- van Leeuwen, F., et al., 2016, A&A, DPACP-12 (to be updated)
- Wade, R. A., Donley, J., Fried, R., White, R. E., & Saha, A. 1999, AJ, 118, 2442
- Walker, A. R. 1994, AJ, 108, 555