The Gaia spectrophotometric standard stars survey - I. Preliminary results

M. Gebran

Mon. Not. R. Astron. Soc. 000, 1–16 (2012) Printed 26 July 2012 (MN LATEX style file v2.2) arXiv:1207.6042v1 [astro-ph.IM] 25 Jul 2012 The Gaia spectrophotometric standard stars survey. I. Preliminary results. E. Pancino1⋆, G. Altavilla1 , S. Marinoni1,2,3, G. Cocozza1, J. M. Carrasco4, M. Bellazzini1 , A. Bragaglia1 , L. Federici1 , E. Rossetti7, C. Cacciari1 , L. Balaguer Núñez4 , A. Castro5, F. Figueras4, F. Fusi Pecci1 , S. Galleti1 , M. Gebran6, C. Jordi4, C. Lardo7, E. Masana4, M. Monguió4, P. Montegriffo1, S. Ragaini1 , W. Schuster5, S. Trager8, F. Vilardell9 , and H. Voss4† 1 Osservatorio Astronomico di Bologna, INAF, Via C. Ranzani, 1, I-40127, Bologna, Italy Astronomico di Roma, INAF, Via di Frascati, 33, I-00040, Monte Porzio Catone, Italy 3 ASI Science Data Center c/o ESA-ESRIN, Via Galileo Galilei, s/n, I-00044, Frascati, Italy 4 Departament d’Astronomia i Meteorologia,Institut del Ciènces del Cosmos (ICC), Universitat de Barcelona (IEEC-UB), c/ Martı́ i Franquès, 1, 08028 Barcelona, Spain 5 Observatorio Astronómico Nacional, Universidad Nacional Autónoma de México, Apartado Postal 877, C. P. 22800 Ensenada, B. C., México 6 Department of Physics and Astronomy, Notre Dame University-Louiaze, PO Box 72, Zouk Mikael, Zouk Mosbeh, Lebanon 7 Dipartimento di Astronomia, Università di Bologna, Via C. Ranzani, 1, I-40127 Bologna, Italy 8 Kapteyn Institute, University of Groningen, P.O. Box 800, 9700 AV Groningen, the Netherlands 9 Institut d’Estudis Espacial de Catalunya, Edifici Nexus, c/ Capitá, 2-4, desp. 201, E-08034 Barcelona, Spain 2 Osservatorio Accepted XXXX December XX. Received XXXX December XX; in original form XXXX October XX ABSTRACT We describe two ground based observing campaigns aimed at building a grid of approximately 200 spectrophotometric standard stars (SPSS), with an internal ≃1% precision and tied to Vega within ≃3%, for the absolute flux calibration of data gathered by Gaia, the ESA astrometric mission. The criteria for the selection and a list of candidates are presented, together with a description of the survey strategy and the adopted data analysis methods. We also discuss a short list of notable rejected SPSS candidates and difficult cases, based on identification problems, literature discordant data, visual companions, and variability. In fact, all candidates are also monitored for constancy (within ±5 mmag, approximately). In particular, we report on a CALSPEC standard, 1740346, that we found to be a δ Scuti variable during our short-term monitoring (1–2 h) campaign. Key words: Catalogs – Techniques: spectroscopic – Techniques: photometric – Stars: variables. 1 INTRODUCTION Gaia is an ESA (European Space Agency) all sky astrometric, photometric, and spectroscopic survey mission aimed at measuring parallaxes, proper motions, radial velocities, and astrophysical parameters of ≃109 stars (≃1% of the Galactic stellar population) down to magnitude G≃201 , corresponding to V≃20–25 mag, depending on spectral type. The astrometric accuracy is expected to be 5–14 µas for bright ⋆ email: elena.pancino@oabo.inaf.it † Based on data obtained within the Gaia DPAC (Data Processing and Analysis Consortium) — and coordinated by the GBOG (Ground-based Observations for Gaia) working group — at various telescopes; see acknowlegements. 1 the Gaia G-band is the unfiltered broad band defined by the instrumental response curve, see also Figure 1, extracted from Jordi et al. (2010). c 2012 RAS stars (V<12 mag), and to reach ≃300 µas down to V≃20 mag. Radial velocities will be measured for stars brighter than V≃17 mag, depending on spectral type, and their precision will range from 1 km s−1 for the bright stars down to 15–20 km s−1 for the faintest stars, bluer stars having higher uncertainties. The updated science performances of Gaia can be found on the Gaia ESA webpage2 . The expected launch will be in August 2013, from the ESA launch site at Kourou in French Guiana. Gaia will operate for approximately 5 years, with a possible 1 year extension, and the final catalogue is expected to be published 3 years after mission completion, while a set of intermediate releases is presently being defined. Although the primary scientific goal of Gaia is the characterization of the Milky Way, its scientific impact will range from solar system studies to distant quasars, from unresolved galaxies to bi- 2 http://www.rssd.esa.int/index.php?project=GAIA&page=index 2 E. Pancino et al. BP G RP RVS Normalized photon response 1 0.8 0.6 0.4 0.2 0 400 600 800 1000 wavelength (nm) Figure 1. The photon response functions of the Gaia G, BP, RP and RVS passbands. naries, from supernovae to microlensing events, from fundamental physics to stellar variability. The wide variety of scientific topics is illustrated by almost 900 papers in ADS (Astrophysics Data System, of which more than 200 refereed) to date, on a diversity of subjects, from the description of various mission components (including software, pipelines, data treatment philosophy) to simulations of the expected scientific harvest in many diverse areas. Some papers summarize the expected science results (see, e.g., Mignard 2005), but no single paper can be complete in this respect, given the huge range of possibilities opened by Gaia. Three main instruments can be found on board Gaia, the AF (Astrometric Field), consisting of 62 CCDs illuminated with white light, which will provide astrometric measurements and integrated Gaia G-band magnitudes (hereafter G); the BP (Blue Photometer) and RP (Red Photometer), consisting of two strips of 7 CCDs each and providing prism dispersed, slitless spectra at a resolution of R=λ/δλ≃20–100, covering the passbands shown in Figure 1 and also providing integrated BP and RP magnitudes (hereafter GBP and GRP ) and the GBP –GRP colour, which will be fundamental for chromaticity corrections of the astrometric measurements; and the RVS (Radial Velocity Spectrograph), providing R≃11000 spectra in the calcium triplet region (8470–8740 Å) projected onto 12 CCDs. The mission output will thus be accurate positions, proper motions and parallaxes, low resolution BP/RP spectra, integrated G, GBP , and GRP magnitudes and the GBP –GRP colour, plus medium resolution RVS spectra and radial velocities for stars brighter than V≃17 mag. A classification of all observed objects will be performed on the basis of BP/RP and RVS spectra and – when possible – their parametrization will be performed as well, which for stars will provide Teff , logg, E(B–V), [Fe/H], and [α/Fe]. Although Gaia is in principle a self-calibrating mission, some Gaia measurements need to be tied to existing absolute reference systems, and many Gaia algorithms need to be trained. Thus extensive theoretical computations and observing campaigns are being carried out. To make a few examples: radial velocity standards that are stable to 1 km s−1 are being obtained (Crifo et al. 2010); extended libraries of observed and theoretical spectra (Tsalmantza & Bailer-Jones 2009; Sordo et al. 2010; Tsalmantza et al. 2012)3 are being established; the Ecliptic poles – that will be repeatedly observed in the initial calibration phase of Gaia observations – are being observed to produce catalogues of magnitudes and high-resolution spectra (Altman & Bastian 2009). Also, the selection and analysis of reference stars (and galaxies, quasars, asteroids, solar system objects and so on) for the training of Gaia algorithms is being carried on by different groups. This paper is the first of a series, which will present different aspects of the survey and of its data products. The series will include technical papers on the instrumental characterization, data papers presenting flux tables, photometric measurements, and lightcurves of our SPSS candidates, and scientific follow-up papers based on survey data and, when needed, on additional data. This paper presents the ongoing observational survey aimed at building the grid of spectrophotometric standard stars (SPSS) for the absolute flux calibration of Gaia spectra and integrated magnitudes. The structure of the paper is the following: the Gaia external calibration model is briefly illustrated in Section 2; the selection criteria and a list of candidate SPSS are presented in Section 3; the observing campaigns and facilities are described in Section 4; a description of the data treatment principles and methods can be found in Section 5; and a set of preliminary results is presented in Section 6. 2 FLUX CALIBRATION MODEL Calibrating (spectro)photometry obtained from the usual type of ground based observations (broadband imaging, spectroscopy) is not a trivial task, but the procedures are well known (see, e.g., Bessell 1999) and many groups have developed sets of appropriate standard stars for the more than 200 photometric known systems, and for spectroscopic observations. In the case of Gaia, several instrumental effects – much more complex than those usually encountered – redistribute light along the SED (Spectral Energy Distribution) of the observed objects. The most difficult Gaia data to calibrate are the BP and RP slitless spectra, requiring a new approach to the derivation of the calibration model and to the SPSS grid needed to perform the actual calibration. Some important complicating effects are: • the large focal plane with its large number of CCDs makes it so that different observations of the same star will be generally on different CCDs, with different quantum efficiencies, optical distorsions, transmissivity and so on. Therefore, each wavelength and each position across the focal plane has its (sometimes very different) PSF (point spread function); • TDI (Time Delayed Integration) continuous reading mode, combined with the need of compressing most of the data before on-ground transmission, make it necessary to translate the full PSF into a linear (compressed into 1D) LSF (Line Spread Function), which of course adds complication into the picture; 3 Tsalmantza & Bailer-Jones (2009), as many other documents cited in the following, is a Gaia technical report that is normally not available to the public. We nevertheless will cite some of these documents because they contain more detailed discussions of the topics treated here, or simply to give appropriate credit to work that was done previously. Future papers of this series will enter in more technical and scientific details. Subject to approval by the ESA and the Gaia DPAC (Data Processing and Analysis Consortium) governing bodies, Gaia technical reports can be provided to interested readers by the authors. c 2012 RAS, MNRAS 000, 1–16 Gaia SPSS survey. I. Preliminary results. [!t] • in-flight instrument monitoring is foreseen, but never comparable to the full characterization that will be performed before launch, so the real instrument – at a certain observation time – will be slightly different from the theoretical one assumed initially, and this difference will change with time; • finally, radiation damage (or CTI, Charge Transfer Inefficiencies) deserves special mention, for it is one of the most important factors in the time variation of the instrument model (Weiler et al. 2011; Prod’Homme 2011; Pasquier 2011). It has particular impact onto the BP and RP dispersed images because the objects travel along the BP and RP CCD strips in a direction that is parallel to the spectral dispersion (wavelength coordinate) and therefore the net effect of radiation damage can be to alter the SED of some spectra. Several solutions are being implemented to mitigate CTI effects, but the global instrument complexity calls for a new approach to spectra flux calibrations. A flux calibration model is currently implemented in the photometric pipeline, which splits the calibration into an internal and an external part. The internal calibration model (Jordi et al. 2007; Jordi 2011; Carrasco et al. 2009; Fabricius et al. 2009) uses a large number of well behaved stars (internal standards), observed by Gaia, to report all observations to a reference instrument, on the same instrumental relative flux and wavelength scales. Once each observation for each object is reported to the internal reference scales, the absolute or external calibration (Montegriffo & Bellazzini 2009a; Ragaini et al. 2009,b, 2011) will use an appropriate SPSS set to report the relative flux scale to an absolute flux scale in physical units, tied to the calibration of Vega (see also Section 3). Alternative approaches where the internal and external calibration steps are more inter-connected are being tested to maximise the precision and the accuracy of the Gaia calibration (Brown et al. 2010; Montegriffo et al. 2011a; Montegriffo 2011b; Carrasco et al. 2011). The Gaia calibration model was also described by Pancino (2010), Jordi (2011), and Cacciari (2011). The final flux calibrated products will be: averaged (on all transits – or observations) white light magnitudes, G; integrated BP/RP magnitudes, GBP and GRP ; flux calibrated BP/RP spectra; RVS spectra and integrated GRVS magnitudes, possibly also flux calibrated (Trager 2010). The GBP –GRP colour will be used to correct for chromaticity effects in the global astrometric solution. Only for specific classes of objects, epoch spectra and magnitudes will be released, with variable stars as an obvious example. The external calibration model contains – as discussed – a large number of parameters, requiring a large number (about 200) of calibrators. With the standard calibration techniques (Bessell 1999), the best possible calibrators are hot, almost featureless stars such as WD or hot subdwarfs. Unfortunately, these stars are all similar to each other, forming an intrinsically degenerate set. The Gaia calibration model instead requires to differentiate as much as possible the calibrators, by including smooth spectra, but also spectra with absorption features, both narrow (atomic lines) or wide (molecular bands), appearing both on the blue and the red side of the spectrum4 . An experiment described by Pancino (2010) shows that the inclusion of just a few M stars5 with large molecular ab- 4 Including emission line objects in our set of calibrators is problematic. Emission line stars are often variable and thus do not make good calibrators. Similarly for Quasars, which are typically faint for our ground-based campaigns. Thus, with this calibration model we do not expect to be able to calibrate with very high accuracy emission line objects. 5 While M giants show almost always variations of the order of 0.1– c 2012 RAS, MNRAS 000, 1–16 3 Figure 2. Distribution of our SPSS candidates on the sky. The Galactic plane and center are marked with a dotted line and a large black circle, respectively. The Ecliptic poles are marked as two large grey circles, and two stripes at ±45 deg from the Ecliptic poles (roughly where Gaia is observing more often) are shaded in grey. Our Pillars are shown as three four-pointed stars, the Primary SPSS candidates as six-pointed stars, and the Secondary SPSS candidates as five-pointed stars. The stars size is proportional to the SPSS brightness, ranging from V≃8 (largest symbols) to 15 mag (smallest symbols), approximately. sorptions in the Gaia SPSS set can improve the calibration of similarly red stars by a factor of more than ten (from a formal error of 0.15 mag to an error smaller than 0.01 mag). In conclusion, the complexity of the instrument reflects in a complex calibration model, that requires a large set of homogeneously calibrated SPSS, covering a range of spectral types. No such database exists in the literature, and new observations are necessary to build it. 3 THE CANDIDATE SPSS The Gaia photometric calibration model implies specific needs as it comes to (i) the selection criteria of the SPSS candidates and (ii) the characteristics of their flux tables (i.e., their calibrated spectra). The derived formal requirements (van Leeuwen et al. 2011) define both the SPSS grid and the observing needs and can be summarized as follows: • spectral resolution R=λ/δλ ≃ 1000, i.e., they should oversample the Gaia BP/RP resolution by a factor of 4–5 at least; • wavelength coverage: 3300–10500 Å, corresponding to the full coverage of the BP and RP spectrophotometers; • large sample (approximately 200–300 stars), covering different spectral types, although a large fraction should consist in hot stars, as featureless as possible; 0.2 mag, and thus are not useful as flux standards, M dwarfs rarely do (Eyer & Mowlavi 2008). 4 E. Pancino et al. [!t] Table 1. Pillars Star RA (J2000)a Dec (J2000)a B V Typeb (hh:mm:ss) (dd:pp:ss) (mag) (mag) G 191-B2B 05:05:30.61 +52:49:51.95 11.46c 11.78c DA0 GD 71 05:52:27.63 +15:53:13.37 12.78b 13.03b DA1 GD 153 12:57:02.33 +22:01:52.52 13.07b 13.35b DA1 a van Leeuwen (2007) coordinates; b Bohlin, Lindler, & Riess (2005) magnitudes and spectral types; c Landolt & Uomoto (2007) magnitudes. The basic principles of our calibration strategy were first outlined by Bellazzini et al. (2006). The sky distribution of our candidates is shown in Figure 2, while the magnitude and spectral type distributions are shown in Figure 3. More details on the selection criteria, sources, and candidate lists can be found in Altavilla et al. (2008) and Altavilla et al. (2010b). 3.1 Pillars Figure 3. Distribution of all our SPSS candidates in magnitude (top panel) and spectral type (bottom panel). • magnitude range 9<V<15 mag: when observed by Gaia they should ensure an end-of-mission S/N≃100 over most of the wavelength range, without saturating; • typical uncertainty on the absolute flux, with respect to the assumed calibration of Vega (Bohlin & Gilliland 2004; Bohlin 2007)6 of ≃3%, excluding small troubled areas in the spectral range (telluric bands residuals, extreme red and blue edges), where it can be somewhat worse; • very homogeneous data treatment and quality, i.e., the SPSS flux tables should have ≃1% internal precision; • photometric stability within ±5 mmag, necessary to ensure the above accuracy and precision. The CALSPEC7 (Bohlin 2007) and the Stritzinger et al. (2005) databases are very good starting points (see also Bessell & Murphy 2012, for further references), but new observations are needed. It is clear that if we add the requirements deriving from a ground-based campaign8 to the above ones, it becomes very difficult to assemble the grid in a relatively short time. Therefore we decided to proceed in steps. The link between Vega and our SPSS will be ensured by three Pillars (Section 3.1); these will enable to calibrate the Primary SPSS (Section 3.2), our ground-based calibrators spread over the whole sky. The primary SPSS will in turn enable to calibrate our Secondary SPSS (Section 3.3), which constitute the actual Gaia grid, together with the eligible Primaries. Our three pillars are the CALSPEC pillars and were selected from Bohlin, Colina, & Finley (1995) and Bohlin (1996). They are the DA (pure hydrogen atmosphere) white dwarfs (WD) named G 191-B2B, GD 71, and GD 153, three well known and widely used standards. A fourth star from Bohlin, Colina, & Finley (1995), HZ 43, was excluded from our list because it is member of a binary system. Its companion, a dMe star (Dupuis et al. 1998), at a distance of ≃3”, is brighter longward of ≃7000Å (Bohlin, Dickinson, & Calzetti 2001), and therefore not usable in our ground-based campaign, where the actual seeing ranges from ≃0.5” up to >2” in some cases and the slit width is 10”-12” for our spectra. The flux calibrated spectra of the Pillars, available in the CALSPEC database, are tied to the revised Vega flux9 , and their flux calibrations are based on the comparison of WD model atmospheres10 and spectra obtained with the Faint Object Spectrograph (FOS) aboard HST. The Pillars are in the temperature range 32 000 6 Teff 6 61 000 K and the FOS spectrophotometry agrees with the model fluxes to within 2% over the whole UV-visible range. In addition, the simulated B and V magnitudes of the data agree to better than 1% with the Landolt photometry (Landolt & Uomoto 2007). Some of the most recent literature measurements for the three Pillars are listed in Table 1. 3.2 Primary SPSS candidates The candidate Primary SPSS are 44 bright (9.V.14 mag — see also Table 2), well known spectrophotometric standards with spectra already in the CALSPEC flux scale, or which can be easily tied to that scale with dedicated ground-based observations. We selected 6 A great promise for the future of flux calibrations comes from the ACCESS mission (Kaiser et al. 2010). We tried to include a few of their primary targets in our SPSS candidates list. 7 http://www.stsci.edu/hst/observatory/cdbs/calspec.html 8 Observations must be feasible with 2–4 m class telescopes, all year round from both hemispheres, and the SPSS must be free from relatively bright companions, that might be seen as separate objects from space, but are close enough to contaminate the SPSS aperture photometry and wide slit spectra, owing to the Earth’s atmospheric seeing. 9 Vega was calibrated using STIS (Space Telescope Imaging Spectrograph) observations (Bohlin & Gilliland 2004) and the calibration was later revised by Bohlin (2007). 10 Hubeny NLTE models (Hubeny & Lanz 1995). See also Bohlin (2007) and references therein. In particular, these model flux distributions are normalized to an absolute flux of Vega of 3.46 × 10−9 erg cm−2 s−1 Å−1 at 5556 Å. c 2012 RAS, MNRAS 000, 1–16 Gaia SPSS survey. I. Preliminary results. them according to the criteria outlined above, and with the additional criterium that the sample should be observable from both hemispheres, all year round, with 2–4 m class telescopes, as mentioned above. We searched for candidates the best existing datasets, such as CALSPEC, Oke (1990), Hamuy et al. (1992, 1994), Stritzinger et al. (2005). As already noted, the Primary SPSS will be calibrated using the Pillars, and will constitute our grid of ground-based calibrators for the Secondary SPSS. Those Primaries which a posteriori will satisfy also the criteria outlined for the Secondary SPSS (e.g., will have an end-of-mission satisfactory S/N ratio when observed by Gaia) will be included in the final list of Gaia SPSS. The Primary SPSS candidates are listed in Table 2 along with some recent literature information. We mention here that one of the CALSPEC standards, star 1740346, was found to be a variable with an amplitude of the order of 10 mmag, and is probably a δ Scuti type variable, as described in Section 6.3. We are gathering additional data to characterize its variability. 3.3 Secondary SPSS candidates The Secondary SPSS are selected according to the criteria given above; in particular they need to provide BP/RP spectra with an adequate end-of-mission S/N ratio (see above). This was statistically verified for all our SPSS candidates (Carrasco et al.( 2006; Carrasco et al. 2007) with a set of simulations of the expected number of transits depending on the position on the sky and on the launch conditions. Stars fainter than V≃13 mag need to have a higher number of transits to gather sufficient end-of-mission S/N when observed by Gaia. The candidates surviving this test are presented in Table 4 along with some recent literature information. Not all literature data (especially magnitudes and spectral types) have the same precision11 , but we gathered the best data available, to our knowledge; we will hopefully produce more precise information from our own data and, later, from Gaia. Our source catalogues were mainly (but not only): • the “Catalog of Spectroscopically Identified White Dwarfs” (McCook & Sion 1999), containing 2249 stars in the original paper, and 12876 in the online — regularly updated — catalogue12 at the time of writing; • “A Catalog of Spectroscopically Confirmed White Dwarfs from the Sloan Digital Sky Survey Data Release 4” (SDSS) (Eisenstein et al. 2006), containing 9316 objects. The complete data-set is available online13 ; • a list of 121 DA white dwarfs for which there are FUSE14 data (Barstow 2010, private communication); • a selection of metal poor stars from “A survey of proper motion stars. 12: an expanded sample” (Carney et al. 1994) containing 52 stars (Korn 2010, private communication); 11 Literature data come from a variety of heterogeneous sources, and are determined with many diferent methods. In particular, in Tables 2 and 4, the most uncertain magnitudes are those derived with the approximated formulae from the TYCHO magnitudes (Hog et al. 1998), while the most uncertain spectral types are the ones roughly estimated by us from the Carney et al. (1994) temperatures. 12 http://www.astronomy.villanova.edu/WDCatalog/index.html 13 http://iopscience.iop.org/0067-0049/167/1/40/datafile1.txt 14 http://fuse.pha.jhu.edu/ c 2012 RAS, MNRAS 000, 1–16 5 Table 2. Primary SPSS candidates Star RA (J2000) (hh:mm:ss) Dec (J2000) (dd:pp:ss) B (mag) V Type (mag) EG 21 GD 50 HZ 2 LTT 3218 AGK+81266 GD 108 Feige 34 LTT 4364 Feige 66 Feige 67 HZ 44 GRW+705824 EG 274 EG 131 LTT 7987 G 93-48 LTT 9491 Feige 110 White dwarfs and hot subdwarfs: 03:10:31.02a –68:36:03.39a 11.42b 11.38b DA3c 03:48:50.20d –00:58:31.20d 13.79e 14.06e DA2c 04:12:43.55f +11:51:49.00f 13.79g 13.88g DA3c 08:41:32.56h –32:56:34.90h 12.07e 11.85e DAi 09:21:19.18a +81:43:27.64a 11.60g 11.94g Oj 10:00:47.37k –07:33:30.50k 13.34l 13.58l Bk 10:39:36.74a +43:06:09.25a 10.84g 11.18g DOj 11:45:42.92a –64:50:29.46a 11.69e 11.50e DQ6i 12:37:23.52a +25:03:59.87a 10.22g 10.51g Oj 12:41:51.79a +17:31:19.75a 11.48g 11.82g Oj 13:23:35.26a +36:07:59.51a 11.38g 11.67g Oj 13:38:50.47a +70:17:07.62a 12.68g 12.77g DA3j 16:23:33.84a –39:13:46.16a 10.89b 11.02b DA2c 19:20:34.93a –07:40:00.05a 12.35ee 12.29ee DBQA5 20:10:56.85a –30:13:06.64a 12.27e 12.21e DA4m 21:52:25.38a +02:23:19.56a 12.73e 12.74e DA3c 23:19:35.44n –17:05:28.40n 14.13g 14.11g DB3i 23:19:58.40a –05:09:56.21a 11.53g 11.83g Oo HD 37725 HILT 600 Feige 56 SA 105-448 HD 121968 CD-32 9927 LTT 6248 1743045 1805292 1812095 BD+28 4211 Other hot stars (O, B, and A): 05:41:54.37p +29:17:50.93p 8.12gg 06:45:13.37p +02:08:14.70p 10.62b 12:06:47.23a +11:40:12.64a 10.93b 13:37:47.07p –00:37:33.02p 9.44r 13:58:51.17a –02:54:52.32a 10.08r 14:11:46.32p –33:03:14.30p 10.84u 15 38 59.66v –28 35 36.87v 12.29e 17:43:04.48f +66:55:01.60f 13.80w 18:05:29.28f +64:27:52.00f 12.50w 18:12:09.57f +63:29:42.30f 11.90w 21:51:11.02a +28:51:50:36a 10.17g 8.31gg 10.44b 11.06b 9.19r 10.26r 10.44u 11.80e 13.52w 12.06w 11.80w 10.51g A3hh B1q B5pb A3s B1t A0o Ab A5x A6w A5w Opj Cooler stars (F, G, and K): CD-34 241y 00:41:46.92p –33:39:08.51p 11.71b 11.23b Fb LTT 1020 01:54:50.27v –27:28:35.74v 12.06e 11.51e Gb LTT 1788 03:48:22.67n -39:08:37.20n 13.61e 13.15e Fb LTT 2415 05:56:24.74a –27:51:32.35a 12.60e 12.20e Gi LTT 3864 10:32:13.60v –35:37:41.80v 12.65e 12.17e Fb SA 105-663 13:37:30.34a –00:13:17.37a 9.10s 8.76s Fz P 41-C 14:51:57.99aa +71:43:17.38aa 12.84bb 12.16bb G0cc SA 107-544 15:36:48.10p –00:15:07.11p 9.44s 9.04s F3z P 177-D 15:59:13.57f +47:36:41.90f 13.96dd 13.36dd G0cc P 330-E 16:31:33.82f +30:08:46.50f 13.52dd 12.92dd G0cc KF08T3 17:55:16.23f +66:10:11.70f 14.30cc 13.50cc K0x KF06T1 17:57:58.49f +66:52:29.40f 14.50cc 13.52cc K1x KF06T2 17:58:37.99f +66:46:52.20f 15.10cc 13.80cc K1x KF01T5 18:04:03.80x +66:55:43.00x ... 13.56x K1x LTT 7379 18:36:25.95a –44:18:36.94a 10.83e 10.22e G0b BD+17 4708 22:11:31.37a +18:05:34.17a 9.91g 9.46g F8cc LTT 9239 22:52:41.03v –20:35:32.89v 12.67e 12.07e Fb a Perryman et al. b Hamuy et al. (1997); (1992); c Bica, Bonatto, & Giovannini d Hawarden et al. (1996); (2001); e Landolt (1992); f 2MASS (Cutri et al. 2003); g Landolt & Uomoto h Bakos, Sahu, & Németh i Bessell (2007); (2002); (1999); j Turnshek et al. (1990); k Østensen et al. (2010); l Colina & Bohlin (1994); m Holberg, Oswalt, & Sion (2002); n Pokorny, Jones, & Hambly (2003); o Stone & Baldwin (1983); p Hog et al. (1998); q Stone (1977); r Stritzinger et al. (2005); s Landolt (1983); t Sembach & Savage (1992); u Kilkenny & Menzies (1989); v Salim & Gould (2003); w Bohlin et al. (2011); x Reach et al. (2005); y This star was wrongly identified by Hamuy et al. (1992) as LTT 377; the case is discussed in dez aa 6 E. Pancino et al. • “The HST/STIS Next Generation Spectral Library” (NGSL, Gregg et al. 2004)15 containing 378 bright stars covering a wide range in abundance, effective temperature and luminosity; • the catalogues from “The M dwarf planet search programme at the ESO VLT + UVES. A search for terrestrial planets in the habitable zone of M dwarfs” (Zechmeister et al. 2009) and from “Rotational Velocities for M Dwarfs” (Jenkins et al. 2009), particularly useful for the selection of red stars; • the “Medium-resolution Isaac Newton Telescope Library of Empirical Spectra (MILES)”16 (Sánchez-Blázquez et al. 2006) database containing 985 spectra obtained at the 2.5 m Isaac Newton Telescope (INT) covering the range 3525–7500 Å; • “SEGUE: A Spectroscopic Survey of 240,000 Stars with g=14–20” (Yanny et al. 2009), containing ≃240 000 moderateresolution spectra from 3900 to 9000 Å of fainter Milky Way stars (14.0 6 g 6 20.3) of a wide variety of spectral types and classes. In particular, we made use of the re-analysis by Tsalmantza & Bailer-Jones (2009) and Tsalmantza et al. (2012) to select a few suitably bright stars; • “The Ecliptic Poles Catalogue Version 1.1” (Altman & Bastian 2009), a preliminary version of the photometric catalogue that will be used by Gaia in the initial observation phases, containing 150 000 stars down to V≃22 mag, in a region of approximately 1 deg2 around the Northern and Southern Ecliptic poles; • The WD online catalogue maintained by A. Kawka17 , and information from Kawka et al. (2007); • A provisional list of targets for the ACCESS mission (Kaiser et al. 2007, 2010), provided by M. E. Kaiser (2010, private communication). Other references can be found in Table 4. All the lists were merged and cross-checked to eliminate redundant entries. The clean list (≃13 500 stars) was then used to extract a subsample (≃300 stars) according to the criteria outlined above. During the course of the survey, we rejected a few of the original ≃300 candidates because they were found to be binaries, variables, or they showed close companions on the basis of our literature monitoring and/or of our data. The rejection procedure, along with a few interesting cases, is described in Section 6. A few more candidates may be rejected during the course of the campaign, and some candidates might be added if needed by the Gaia photometric pipeline, once it is running on real data. 4 THE SURVEY Our survey is split into two campaigns, the main campaign dedicated to obtaining spectrophotometry of all our candidate SPSS, and the auxiliary campaign dedicated to monitoring the constancy of our SPSS on relevant timescales. 4.1 Main campaign Classical spectrophotometry (Bessell 1999) would clearly be the best approach to obtain absolutely calibrated flux spectra if we had a dedicated telescope. However, a pure spectrophotometric approach would require too much time, given that we need high S/N 15 16 17 http://archive.stsci.edu/prepds/stisngsl/ http://www.ucm.es/info/Astrof/miles/miles.html http://sunstel.asu.cas.cz/∼kawka/Mainbase.html of 300 stars, in photometric sky conditions, which are rare except maybe in a few sites. We thus decided for a combined approach (Bellazzini et al. 2006), in which spectra are obtained even if the sky is non-photometric18 , providing the correct spectral shape of our SPSS (what we will call “relative flux calibration”). Then, imaging in photometric conditions and in three bands (generally B, V, and R, but sometimes also I and, more rarely, U) is obtained and calibrated magnitudes are used to scale the spectra to the correct zeropoint (“absolute flux calibration by comparison”). The calibrated magnitudes of SPSS will be obtained through at least three independent observations in photometric conditions. Our sample contains some photometric standards from Landolt (1992), Landolt & Uomoto (2007), and a few secondary Stetson standards19 (see Stetson 2000, and online updates). By comparing the obtained magnitudes and synthetic magnitudes derived from the relatively calibrated spectra, we can obtain the necessary zeropoint corrections to correct our spectral flux calibration. To those spectra obtained in photometric conditions (at the moment approximately 20–25% of the total) we will apply the classical method, and this control sample will allow us to check the validity of the combined spectroscopy plus photometry approach. 4.2 Constancy monitoring This kind of monitoring is necessary for a few reasons. Even stars used for years as spectrophotometric standards were found to vary when dedicated studies have been performed (see e.g., G24-9, that was found to be an eclipsing binary by Landolt & Uomoto 2007), and even stars that are apparently safe may show unexpected variations. Our own survey has already found a few variables and suspected variables, including one of the CALSPEC standards (Section 6.3). White dwarfs may show variability with (multi-)periods from about 1 to 20 min and amplitudes from about 1-2% up to 30%, i.e., ZZ Ceti type variability. We have tried to exclude stars within the instability strips for DAV (Castanheira et al. 2007), DBV, and DOV but in many cases the existing information was not sufficient (or sufficiently accurate) to firmly establish the constant nature of a given WD. Hence, many of our candidate SPSS needed to be monitored for constancy on short timescales, of the order of 1–2 h. Similar considerations are valid for hot subdwarfs (Kilkenny 2007). Also redder stars are often found to be variable: for example K stars have shown variability of 5-10% with periods of the order of days to tens of days (Eyer & Grenon 1997). In addition, binary systems are frequent and eclipsing binaries can be found at all spectral types. Their periods can span a range from a few hours to hundreds of days, most of them having P ≃ 1-10 days, (Dvorak 2004). Thus, in addition to our short term monitoring, we are observing all our SPSS on longer timescales, of about 3 yrs, with a random phase sampling approximately 4 times a year, which should be enough to detect variability, although not for a proper characterization of these newly discovered variables. Unlike the short-term monitoring, the long-term monitoring can be picked up by Gaia once it starts operations. Gaia data will help in the characterization and parametriza- 18 The cloud coverage must produce grey extinction variations, i.e., the extinction must not alter significantly the spectral shape. This condition is almost always verified in the case of veils or thin clouds (Oke 1990; Pakštiene & Solheim 2003), and can be checked a posteriori for each observing night. 19 http://www4.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/community/STETSON/ c 2012 RAS, MNRAS 000, 1–16 Gaia SPSS survey. I. Preliminary results. 7 tion of the detected variables by providing, on average, ≃80 sets of spectra and integrated magnitudes in its 5 years of operation. We use relative photometry measurements, with respect to field stars, for both our short (1–2 h) and long (3 yrs) term monitoring campaigns, aiming at excluding all stars with a variability larger than ±5 mmag, approximately. Obviously, as soon as a target is recognized as variable, it is excluded from our candidate list, but we are aware that some characterization of the variability is of scientific value, so whenever possible, we follow-up our new variable stars with imaging, more detailed lightcurves and, when necessary, spectroscopy. 4.3 Observing facilities and status We have considered a long list of available facilities in both hemispheres (Federici et al. 2006; Altavilla et al. 2010a). The eligible instruments must be capable of obtaining low resolution spectroscopy – with the characteristics described in Section 3 – and Johnson-Cousins photometry. At least one site in the North and one in the South with a high probability of having photometric sky conditions were necessary. We eventually selected six facilities: • EFOSC2@NTT at the ESO La Silla Observatory, Chile, our Southern facility for spectroscopy and absolute photometry, and for some constancy monitoring; • ROSS@REM at the ESO La Silla Observatory, Chile, our Southern facility for relative photometry; • CAFOS@2.2m at the Calar Alto Observatory, Spain, one of our Northern spectrographs and imagers, for absolute and relative (spectro)photometry; • DOLoRES@TNG at the Roque de Los Muchachos in La Palma, Spain, one of our Northern spectrographs and imagers, for absolute and relative (spectro)photometry; • LaRuca@1.5m at the San Pedro Mártir Observatory, Mexico, our Northern source of absolute and relative photometry; • BFOSC@Cassini in Loiano, Italy, providing a few spectra and more relative photometry in the Northern hemisphere. Given the diversity of instruments and observing conditions, we enforced a set of strict observing protocols (Pancino et al. 2008, 2009, 2011), concerning all aspects of the photometry and spectroscopy observations, including requirements about the calibration strategy, and on-the-fly quality control of data acquired at the telescope (see also Sections 4.1 and 4.2). Observations started in 2007. At the time of writing, the survey has been awarded more than 400 nights of observing time, both in visitor and service mode, of which 25–35% was lost due to bad weather or technical reasons, or was of non-optimal quality. The main campaign should be completed within 2012, with the last ESO run assigned in July 2012 and the last Calar Alto run in May 2012. The short-term variability monitoring is 85% complete and the long-term monitoring will take more time and will probably be completed around 2013–2014. Figure 4. Second-order contamination on DOLoRes@TNG spectra of a blue star (left panel) and a red star (right panel); the black lines are the corrected spectra, while the red lines above, starting at about 9500 Å, show the contaminated spectra. the analysis is advancing for short-term (1–2 h) constancy monitoring (≃35% complete) and less complete for spectroscopy (≃20%), absolute photometry (just started). Long-term (3 yrs) constancy monitoring observations are still incomplete. 5.1 Familiarization plans We obtained our data from a variety of instruments, that also were upgraded or modified during the course of the observations, for example a few CCDs were substituted by new and better CCDs. A strict characterization of the used instruments was needed, requiring additional calibration data, taken during daytime, twilight, and also nighttime. We called these technical projects “familiarization plans” (Altavilla et al. 2011; Marinoni et al. 2012b). Their results will be published in subsequent technical papers of this series, and they can be roughly summarized as follows: • CCD familiarization plan, containing a study of the dark and bias frames stability; the shutter characterization (shutter times and delays); and the study of the linearity of all employed CCDs; • Instrument familiarization plan studying the stability of imaging and spectroscopy flats, the study of fringing, and the lamp flexures of the employed spectrographs; • Site familiarization plan (in preparation), providing extinction curves, extinction coefficients, colour terms, and a study of the effect of “calima”20 on the spectral shape. As a results of these studies, specific recommendations for observations and data treatment were defined. 5.2 Pre-reductions Data reductions were performed mostly with IRAF21 and IRAFbased pipelines. The detailed data reduction protocols are described in Gaia technical reports (Marinoni et al. 2012a,c; Cocozza et al. 2012; Altavilla et al. 2012b). 5 DATA TREATMENT AND DATA PRODUCTS The required precision and accuracy of the SPSS calibration imposes the adoption of strict protocols of instrument characterization, data reduction, quality control, and data analysis. We will briefly outline below our data treatment methods, while more details will be published in future papers of the series, presenting our data products. At the time of writing, reductions are ongoing: prereduction of the obtained data is more than 50% complete, while c 2012 RAS, MNRAS 000, 1–16 20 Calima is a dust wind originating in the Sahara air layer, which often affects observations in the Canary Islands. 21 IRAF is the Image Reduction and Analysis Facility, a general purpose software system for the reduction and analysis of astronomical data. IRAF is written and supported by the IRAF programming group at the National Optical Astronomy Observatories (NOAO) in Tucson, Arizona. NOAO is operated by the Association of Universities for Research in Astronomy (AURA), Inc. under cooperative agreement with the National Science Foundation 8 E. Pancino et al. For imaging, we pre-reduced the frames with standard techniques, and then performed aperture photometry with SExtractor (Bertin & Arnouts 1996). SExtractor also provides many useful parameters that we will use for a semi-automated quality control (QC) of each reduced frame, allowing to identify saturated or too faint SPSS, or frames that do not contain enough good reference stars in the field to perform relative photometry. Reduced frames that pass QC and their respective photometric catalogues are stored in our local archive. Spectroscopic reductions are less automated, relying mostly on the standard IRAF longslit package and tasks. Spectra are prereduced, extracted and wavelength calibrated. Spectrophotometry is obtained with a wide slit (5–6 times the seeing, at least; generally the widest available slits are 10” or 12”). Narrow slit spectra are also observed (typically with a slit of 1” to 2.5”, depending on the instrument), to obtain a better wavelength calibration. In some cases (slit larger than 1.5 times the seeing), we will attempt to correct the narrow slit spectra for differential light losses; tests show that this can be done in most cases with a third order polynomial fit. The corrected narrow slit spectra will thus add to the S/N of wide slit spectra, and will also help in beating down the fringing, because the fringing patterns of wide and narrow slit spectra are different22 . Extracted and wavelength calibrated 1D spectra are stored locally for future processing, if they pass some basic QC (not saturated or too faint, no close companions in the slit, and so on). 5.3 Higher level spectra treatment After spectra are extracted and wavelength calibrated, they are corrected for telluric absorption features and for second-order contamination (see below). The blue and red spectral ranges, that are observed separately with the available instruments, are joined after performing a relative calibration using the available Pillar or Primary observations taken on the same night at different airmasses. To illustrate the quality of the reduction procedures, we show in Figure 4 our second-order contamination correction for a blue and a red star. The effect arises when light from blue wavelengths, from the second dispersed order of a particular grism or grating, falls on the red wavelengths of the first dispersed order. Such contamination usually happens when the instrument has no cross-disperser. Of the instruments we use (Section 4.3), only EFOSC2@NTT and DOLoRes@TNG present significant contamination. To map the blue light falling onto our red spectra, we adapted a method proposed by Sánchez-Blázquez et al. (2006) and applied it to dedicated observations (Altavilla et al. 2012a). Our wavelength maps generally allow us to recover the correct spectral shape to within a few percent, as tested on a few CALSPEC standards observed with both TNG and NTT. If the spectra were observed in photometric conditions, after the above manipulations the flux calibration is complete and ready to be checked. Otherwise, the shape of the spectrum is recovered, but an additional zeropoint correction is required. Different levels of intermediate data products are stored after basic QC, including spectra with and without telluric correction or second-order contamination correction. 5.4 Absolute and relative photometry Photometry observations are taken in the form of a night point (absolute or relative, depending on sky conditions) or a time series. The night point is a triplet of images in each of three filters (B, V, R, and sometimes also I or U) taken consecutively. A series lasts at least one hour, contains at least 30 exposures, and is taken with the bluest available filter (B in most cases, except for REM, where we use V). The SExtractor catalogues are cross-matched with CataXcorr23 to identify the SPSS and the reliable reference stars in the surrounding field. Absolute photometry is then performed in a standard way, using observations of two or three standard fields (Landolt 1992) at different airmasses during the night. Observations of the same SPSS are taken repeatedly at different times and, when possible, different sites, to be able to identify any hidden systematics. Some stars in our candidates list are spectrophotometric standards (Landolt 1992; Landolt & Uomoto 2007; Stetson 2000) that will be used to check the quality of our measurements. The final calibrated magnitudes will be used to correct the zero-point of spectra observed in non-photometric (but grey absorption) sky conditions, as explained later. Relative photometry is performed using the difference between the SPSS and the available field stars (at least two are required) magnitudes. Reference stars must be non saturated, not too faint, present in all frames, and non variable. Some preliminary results of this procedure are discussed in Section 6. The target precision of at least 10 mmag, necessary to meet our calibration requirements (Section 3), is generally always reached with BFOSC, EFOSC2, LaRuca, DOLoRes, and CAFOS, and most of the times also withROSS@REM, the robotic telescope in La Silla. The final data products of the photometry procedure are absolute magnitudes and differential lightcurves (on 1–2 h and 3 yr timescales) with their respective uncertainties. 5.5 Final flux tables All the relatively (if the night was non-photometric but grey) and absolutely (if the night was photometric) calibrated spectra will now have the correct spectral shape. The absolutely calibrated spectra obtained in different nights or with different telescopes for each star will be compared to study hidden systematics (if any). Some of our targets belong to widely used spectrophotometric datasets (see Section 3), and will be our anchor point to check our flux scale and to find potential problems. The relatively calibrated spectra will need a zeropoint correction. We will thus use the version including telluric absorption features to derive synthetic B, V, and R (and if available, I and U) magnitudes, and compare them with our calibrated magnitudes (see previous section) to apply the necessary correction. Once this procedure will be completed, all the spectra obtained for each SPSS will be combined in one single spectrum: our final product. It will be necessary in many cases to use synthetic spectra to calibrate the noisy edges, or the reddest wavelength ranges, if they will not be properly cleaned from reddening. As an example of the data quality, we show in Figure 5 a test performed to refine our reduction procedures, where a portion of 22 The fringing pattern in the extracted spectra is a combined 1D result of a 2D pattern, in an aperture that covers a different CCD region in the wide and in the narrow slit spectra. Thus, the 1D fringing pattern of these two kinds of spectra will be different. 23 CataXcorr is part of a package dedicated to catalogue cross-matching and astrometry, developed by P. Montegriffo at the Bologna Observatory (INAF). c 2012 RAS, MNRAS 000, 1–16 Gaia SPSS survey. I. Preliminary results. 9 Figure 6. Correct identifications of two candidate SPSS that were wrongly identified in the literature. Left panel: the case of LTT 377, which was confused with CD -34 241; the image is 15’ wide, North is up and East is left. Right panel: the case of WD 0204-306, which was associated with LP 88523 instead of LP 885-22; the image is 7’ wide, North is up and East on the left. Figure 5. Top panel: comparison of our preliminary spectrum of HZ 44 (thick black line) with the CALSPEC tabulated spectrum (thick red line) in a region where we found a discrepancy (marked by the two arrows), where small ≃0.5–1.0% jumps in the CALSPEC spectrum are probably due to a mismatch of two different spectra. Bottom panel: ratio between our spectrum and the CALSPEC spectrum; perfect agreement (red line) and ±1% agreement (dotted red lines, our requirement) are marked. on the spectra, and we will use it to correct for residuals from the joining of different spectral pieces, sky subtraction, telluric features correction, fringing, and imperfections at the spectral extremities, where the S/N ratio is generally lower. Also, the use of models will allow us to characterize our targets, thus providing spectral types, effective temperatures, gravities, metallicities, and reddening. 6 PRELIMINARY RESULTS the spectrum of HZ44 observed in a photometric night is compared with the CALSPEC flux table. We point out that this preliminary reduction did not include the proper extinction curve, but a tabulated curve from Sánchez et al. (2007); the telluric absorption features were not removed (we will use procedures similar to that by Bessell 1999); the red wavelengths are affected by fringing that we will beat down by combining observations from different telescopes whenever possible; and the extremes of the wavelength range are affected by poor S/N, so that we will have to use synthetic spectra to calibrate those extremes. Even with these limitations, we were able to meet the requirements (Section 3), because the residuals between our spectrum and the CALSPEC tabulated one were on average lower than 1%, with the exception of the low S/N red edge and of the telluric absorption bands. However, some unsatisfactory jumps appeared in the comparison, between 4000 and 6000 Å, where our spectra have the highest S/N. As shown in Figure 5 (top panel) and already noted by Bohlin, Dickinson, & Calzetti (2001), the jumps were due to a (minor) problem in the CALSPEC spectrum, probably where two pieces of the spectrum were joined. Thus we were able to identify a defect in the CALSPEC spectrum of the order of 1-2%, approximately, meeting the requirements (Section 3). Similar results were obtained on test reductions of other SPSS (observed with TNG, NTT, and CAHA): GD71, GD153, and G191-B2B, our Pillars, which have the best literature data available. To produce our final flux tables, we will need to adjust model spectra24 to our observed spectra (as done by, e.g., Bohlin 2007). This technique has proven useful to identify and fix minor problems 24 We will make use of both atmosphere models from, e.g., the MARCS, Kurucz, TLUSTY, and Tübingen sets (Gustafsson et al. 2008; c 2012 RAS, MNRAS 000, 1–16 We discuss in the following sections some preliminary results of our survey: a few interesting cases of problematic candidates are described, and a list of notable rejected SPSS candidates can be found in Table 3; two stars showed variability larger than ±5 mmag in our short-term constancy monitoring. 6.1 Identification and literature problems Identification problems are common, especially when large databases are automatically matched (as done within SIMBAD, for example), and when stars have large proper motions. We found our first case when a discrepancy became evident between the LTT 37725 literature spectrum (Hamuy et al. 1992, 1994) and our observed spectrum, which was more consistent with an F type rather than the expected K spectral type. We contacted the SIMBAD and ESO staff, because their sites reported the information from Hamuy et al. (1994) as well, and we concluded that the ESO standard was not LTT 377, but another star named CD 34 241, of spectral type F. This was confirmed by older literature papers like Luyten (1957), where LTT 377 was identified as CD 34 239, and by literature proper motions and coordinates. We could trace back the error to Stone & Baldwin (1983), where the wrong association was probably done for the first time, and then propagated down to SIMBAD and ESO. The correct identification of Castelli & Kurucz 2003; Rauch & Deetjen 2003; Lanz & Hubeny 2003, 2007) or spectral libraries (e.g., Sordo & Munari 2006; Ringat 2012). 25 At the moment of writing, the SIMBAD database has been updated and now the correct identification is reported. 10 E. Pancino et al. Table 3. Notable rejected SPSS candidates Star RA (J2000) (hh:mm:ss) WD 0406+592 04:10:51.70a G 192-41 06:44:26.34b WD 1148-230 11:50:38.80a 1740346 17:40:34.68b WD 1911+135 19:13:38.68b WD 1943+163 19:45:31.77b WD 2046+396 20:48:08.18f WD 2058+181 21:01:16.49b WD 2256+313 22:58:39.44b Dec (J2000) (dd:pp:ss) +59:25:05.00a +50:33:55.90b –23:20:34.00a +65:27:14.80b +13:36:27.70b +16:27:39.60b +39:51:37.33f +18 20 55.30b +31:34:48.90b B V Type Reason for rejection (mag) (mag) 14.30a 13.91c 11.49a 12.68e 14.12a 13.96a 14.10a 15.01a 14.90g 14.40a 13.16c 11.76a 12.48e 14.00a 13.99a 14.43a 15.00a 13.96a DAa Gd DAa A5e DA3a DA2a DA1a DA4a — Two close visual companions detected Suspected variable Two sets of coordinates and magnitudes in literature (see text) Variable, probably of δ Scuti type (CALSPEC standard) Crowded field Crowded field Crowded field One close visual companion detected Fainter than expected (see text, Oswalt, Hintzen, & Luyten 1988) a McCook & Sion (1999); b from 2MASS (Cutri et al. 2003); c Kharchenko (2001); d approximate spectral type from T eff by Carney et al. (1994); e Bohlin & Cohen (2008); f UCAC3 (Zacharias et al. 2009); g USNO-B catalogue (Monet et al. 2003). Figure 7. The unsolved case of WD 1148-230. The finding chart on the left shows the star that in SIMBAD is associated to WD 1148-230, at the coordinates reported by 2MASS (Cutri et al. 2003), the one on the right the star corresponding to the WD 1148-230 coordinates by McCook & Sion (1999) and Stys et al. (2000). Both images are 10’ wide, North is up and East is left. both stars is shown in Figure 6 (left panel)26 . We decided to keep both stars in our candidates lists (see Tables 2 and 4). A similar case was WD 0204-306 for which we obtained an unexpectedly red spectrum. We traced literature identifications back to Reid (1996), who correctly identified WD 0204-306 as associated with LP 885-23 (an M star) in a binary system, with a separation of 73”. At some point, the two stars got confused and in SIMBAD WD 0204-306 (a white dwarf) was cross-identified with LP 885-23 (an M star). Given the reported distance between the two stars, we identified WD 0204-306 as LP 885-22, as shown in Figure 6. Also in this case, having observations of both stars, we kept both in our Secondary SPSS candidates. The mistake was reported to the SIMBAD staff and now the database is corrected. A more critical example was WD 1148-230 (Figure 7), having very different coordinates in the McCook & Sion (1999) catalogue (coming from Stys et al. 2000, and reporting R.A.=11:50:38.8 h and Dec=–23:20:34 deg) and in SIMBAD. The SIMBAD coordinates were from the 2MASS catalogue (Cutri et al. 2003, reporting 26 The black and white finding charts in Figures 6 and 7 were created with the ESO SkyCat tool and images from the Digitized Sky Survey. SkyCat was developed by ESO’s Data Management and Very Large Telescope (VLT) Project divisions with contributions from the Canadian Astronomical Data Center (CADC). Figure 8. Image cutout of candidate WD 0406+592 (left panel) observed with DOLoRes@TNG in the R band, showing two close companions; similarly, a cutout of candidate WD 2058+181 (right panel), observed in San Pedro Mártir in the R band, shows a close companion. R.A.=11:50:06.09 h and Dec=–23:16:14.0 deg). Magnitudes were also significantly different. Unlike in the previous cases, we had insufficient literature information to confirm one or the other identification, so we decided to reject this SPSS candidate, although we suspect that the mistake resides in the SIMBAD automatic association between WD 1148-230 by Stys et al. (2000) and the 2MASS catalogue. Finally, we report on the case of WD 2256+313, which was reported to have V=13.96 mag (Silvestri et al. 2002; Monet et al. 2003), but when observed in San Pedro Mártir appeared to be much fainter than that (and of uncertain spectral type, see also Oswalt, Hintzen, & Luyten 1988), possibly with V>15 mag, so was removed from our candidates list. 6.2 Crowded fields and visual companions In a few cases candidates that appeared relatively isolated on the available finding charts turned out to be in a crowded area where no aperture photometry or reliable wide slit spectroscopy could be performed from the ground, or showed previously unseen companions. Generally, stars with high proper motion could appear isolated in some past finding chart, but later moved too close to another star to be safely observed from the ground. One example of candidate which appeared relatively isolated judging from the McCook & Sion (1999) finding charts, but turned out to be in a crowded field when observed at San Pedro Mártir was WD 1911+135, that was promptly rejected, together with c 2012 RAS, MNRAS 000, 1–16 Gaia SPSS survey. I. Preliminary results. Figure 9. Our best lightcurve for the CALSPEC standard 1740346 (obtained with BFOSC in Loiano on 1 September 2010), originally one of our Primary SPSS candidates. The average of all field-stars magnitude differences (i.e., zero) is marked with a solid line, while the ±1, 2, and 3 σ variations are marked with dotted lines. WD 1943+163 and WD 2046+396. Examples of candidates showing the presence of previously unknown and relatively bright companions were WD 0406+592 and WD 2058+181 (Figure 8). These stars do not have a particularly high proper motion, and appeared easy to identify on the corresponding finding charts, so we did not expect them to show close visual companions, when observed from the TNG and San Pedro Mártir, respectively. Both stars were rejected. 6.3 Variability Our auxiliary campaign started giving results as far as the shortterm constancy monitoring (1–2 h) is concerned. The ability of one lightcurve to detect magnitude variations is measured using the spread of reference star’s magnitude differences. These appear as 1, 2, and 3 σ limits in Figure 9, where we present the differential lightcurves our only confirmed variable star. Star 1740346, one of the currently used CALSPEC standards and one of our Primary SPSS candidates, showed variability with an amplitude of 10 ± 0.8 mmag in B band when observed with BFOSC@Cassini in Loiano, on 1 September 2010; with DOLoRes@TNG, on 31 September 2009; and with BFOSC@Cassini, on 26 May 2009. The variability period is 50 min, approximately, thus showing properties typical of δ Scuti variables. A preliminary determination of 1740346 parameters can be found in Marinoni (2011), using literature data and stellar models, resulting in a mass of ≃1.3 M⊙ , an effective temperature of ≃8300 K, and a distance of ≃750 pc. These parameters are also compatible with a δ Scuti type star. We are gathering detailed follow-up observations and a complete characterization of star 1740346 will be the subject of a forthcoming paper (Marinoni et al., in preparation). The differential lightcurve is presented in Figure 9 (top panel). 7 SUMMARY AND CONCLUSIONS We have described a large (more than 400 nights) ground-based survey which started in 2007 and is expected to end in 2013–2014, aimed at building a grid of SPSS for the flux calibration of Gaia c 2012 RAS, MNRAS 000, 1–16 11 spectra and magnitudes. The technical complexity of Gaia requires a large (≃200) set of SPSS flux tables, calibrated in flux with high precision (≃1%) and accuracy (≃3% with respect to Vega), and covering a range of spectral types. SPSS candidates need to be monitored for constancy (within ±5 mmag) to ensure the quoted precision in the final calibration. We discussed the adopted calibration strategy, the selection requirements and a list of candidate SPSS. A brief overview of the adopted data reduction and analysis procedures was also presented, and more details will be discussed in a series of future papers dealing with all technical aspects, data products, photometric catalogues, flux tables, and lightcurves. Some preliminary results were presented, showing the data quality, a few problematic cases of candidate SPSS that were rejected because of identification problems, close companions, and variability. In particular, we detected a new variable star, a CALSPEC standard which is most probably a δ Scuti variable; follow-up observations for its characterization are ongoing. All data products will be eventually made public together with each Gaia data release, within the framework of the DPAC (Data Processing and Analysis Consortium) publication policies. At the moment the accumulated data and literature information are stored locally and can be accessed upon request. ACKNOWLEDGMENTS We would like to acknowledge the support of the INAF (Istituto Nazionale di Astrofisica) and specifically of the Bologna Observatory; of the ASI (Agenzia Spaziale Italiana) under contracts to INAF I/037/08/0 and I/058/10/0, dedicated to the Gaia mission, and the Italian participation to DPAC (Data Analysis and Processing Consortium). This work was supported by the /MICINN/ (Spanish Ministry of Science and Innovation) — FEDER through grant AYA2009-14648-C02-01 and CONSOLIDER CSD2007-00050. EP acknowledges the hospitality os the ASDC (ASI Sciece Data Center), where part of this work was carried out. We warmly thank the technical staff of the San Pedro Mártir, Calar Alto, Loiano, La Silla NTT and REM, and Roque de Los Muchachos TNG observatories. We made use of the following softwares and online databases (in alphabetical order): 2MASS, CALSPEC, CataXcorr, ESO-DSS, ESO Skycat tool, IRAF, Kawka webpage, MILES, NGSL, SAOImage DS9, SDSS and SEGUE, SExtractor, SIMBAD, SuperMongo, UCAC3, USNO catalogues, Villanova White Dwarf Catalogue. We thank G. S. Aldering, M. .A. Barstow, M. E. Kayser, and A. Korn for sharing their information with us. We also thank M. Bessell, who was the referee of this paper and provided extremely useful comments not only to improve the paper, but for the whole project. 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Preliminary results. 15 Table 4: Secondary SPSS candidates Star WD 2359-434 WD 0004+330 SDSS 03932 WD 0009+501 WD 0018-267 SDSS 03532 WD 0038+555 LTT 377 WD 0046+051 WD 0047-524 WD 0050-332 WD 0104-331 WD 0106-358 WD 0109-264 WD 0123-262 G245-31 WD 0134+833 GJ70 G72-34 WD 0147+674 WD 0148+467 WD 0204-306∗ LP885-23∗ WD 0214+568 WD 0227+050 WD 0302+621 WD 0316-849 G174-44 HG7-15 WD 0435-088 WD 0446-789 WD 0447+176 WD 0455-282 WD 0501-289 G191-52 U1050-027792 WD 0552-041 HD 270422 HD 270477 HD 271747 WD1234+481 SA 104-428 RA (J2000) (hh:mm:ss) 00:02:10.771 00:07:32.261 00:07:52.223 00:12:14.801 00:21:30.731 00:24:38.623 00:41:21.991 00:41:30.476 00:49:09.906 00:50:03.681 00:53:17.441 01:06:46.861 01:08:20.802 01:12:11.659 01:25:24.451 01:38:39.391 01:41:28.741 01:43:20.186 01:46:03.661 01:51:10.291 01:52:02.966 02:07:02.281 02:07:06.331 02:17:33.521 02:30:16.626 03:06:16.691 03:09:59.8914 03:17:23.311 03:48:11.866 04:37:47.4217 04:43:46.471 04:50:13.526 04:57:13.902 05:03:55.512 05:44:43.551 05:52:18.181 05:55:09.5317 05:56:47.7414 05:59:33.3614 05:59:58.629 12:36:45.181 12:41:41.281 1 Dec (J2000) (dd:pp:ss) –43:09:56.021 +33:17:27.601 +14:30:24.723 +50:25:21.401 –26:26:11.461 –01:11:39.753 +55:50:08.401 –33:37:32.036 +05:23:19.016 –52:08:15.601 –32:59:56.601 –32:53:12.451 –35:34:43.002 –26:13:27.699 –26:00:43.901 +69:38:01.501 +83:34:58.901 +04:19:17.976 +35:54:49.401 +67:39:31.301 +47:00:06.656 –30:23:32.201 –30:24:22.901 +57:06:47.501 +05:15:50.686 +62:22:22.681 –84:43:21.1414 +52:17:42.401 +07:08:46.476 –08:49:10.7017 –78:51:50.401 +17:42:06.216 –28:07:54.002 –28:54:34.572 +56:15:30.801 +15:51:52.701 –04:10:07.1017 –66:39:05.2714 –67:01:13.7214 –66:06:08.919 +47:55:22.341 –00:26:26.201 B (mag) 13.122 13.572 15.374 14.782 — 15.144 14.102 11.977 12.932 14.192 13.112 13.282 14.542 12.912 15.352 15.2610 12.882 12.457 13.8410 14.172 12.502 — — 13.562 12.757 15.172 11.6214 14.4915 12.1129 14.102 13.362 12.6317 13.632 13.552 14.0215 14.4221 15.502 10.929 10.739 11.829 14.092 13.5832 V Type Star RA (J2000) (mag) (hh:mm:ss) 13.052 DA52 HD 271759 06:00:41.3414 13.852 DA12 HD 271783 06:02:11.369 15.074 A05 HIP 28618 06:02:27.886 14.362 DA82 WD0604-203 06:06:13.391 13.802 DA92 WD0621-376 06:23:12.631 15.044 A05 WD0644+375 06:47:37.996 14.082 DQ52 WD0646-253 06:48:56.091 10.537 K98 G193-26 07:03:26.291 2 2 12.39 DZ7 WD0713+584 07:17:36.266 2 2 14.20 DA2 WD0721-276 07:23:20.101 13.362 DA12 WD0749-383 07:51:32.5825 13.572 DAZ32 G251-54 08:11:06.246 2 2 14.72 DA2 GJ2066 08:16:07.986 2 2 13.15 DA1 G114-25 08:59:03.376 14.952 DC2 WD0859-039 09:02:17.301 14.5010 K11 WD0912+536 09:15:56.231 2 2 13.11 DA2 WD0943+441 09:46:39.081 7 12 10.92 M2 G43-5 09:49:51.599 12.9810 K11 WD0954-710 09:55:22.891 14.422 DA22 G236-30 10:28:48.371 2 2 12.44 DA3 WD1029+537 10:32:10.2631 13 2 16.18 DA WD1031-114 10:33:42.7625 13.0613 M013 WD1034+001 10:37:03.811 13.682 DA22 WD1041+580 10:44:46.1033 12.807 DA32 WD1053-550 10:55:13.541 14.952 DA4/62 WD1056-384 10:58:20.111 10.5514 DAH2 G146-76 10:59:57.489 15 16 13.75 K0 WD1104+602 11:07:42.801 29 58 10.65 M1 WD1105-048 11:07:59.951 13.772 DQ72 G10-4 11:10:60.006 2 2 13.47 DA3 G254-24 11:32:23.316 18 19 12.65 sdO WD1134+300 11:37:05.106 2 2 13.95 DA1 SDSS 09310 11:38:02.623 13.902 DO2 G10-54 11:49:48.201 15 20 13.26 G WD1153-484 11:56:11.431 21 13.70 — WD1210+533 12:13:24.641 2 2 14.45 DC/DZ WD1211-169 12:14:10.5314 10.059 G022 GJ459.3 12:19:24.096 9 22 10.28 F8 SDSS 12720 12:22:41.663 9 22 11.29 G0 WD1223-659 12:26:42.021 2 2 14.42 DA1 WD2047+372 20:49:06.691 12.6332 G838 WD2111+498 21:12:44.051 Dec (J2000) (dd:pp:ss) –66:03:14.0314 –66:34:59.139 –66:47:28.686 –20:21:07.201 –37:41:28.011 +37:30:57.076 –25:23:47.001 +54:52:06.001 +58:24:20.516 –27:47:21.601 –38:28:36.4125 +79:54:29.576 +01:18:09.266 –06:23:46.196 –04:06:55.451 +53:25:24.901 +43:54:52.371 +06:36:35.649 –71:18:08.311 +62:59:45.001 +53:29:36.4031 –11:41:38.3525 –00:08:19.301 +57:44:35.0033 –55:19:05.201 –38:44:25.101 +44:46:43.759 +59:58:29.901 –05:09:25.901 +06:25:11.516 +76:39:18.036 +29:47:58.346 +57:29:23.893 +06:08:52.141 –48:40:03.181 +53:03:57.361 –17:14:20.1914 +28:22:56.526 +42:24:43.663 –66:12:18.701 +37:28:13.901 +50:06:17.801 B (mag) 11.009 12.639 12.209 11.7523 11.762 11.992 13.302 13.5924 12.069 13.502 13.532 10.5826 11.637 12.5227 13.022 14.192 13.192 12.9029 13.602 13.6215 14.182 12.852 12.8632 14.372 14.422 13.8634 11.159 13.782 13.0932 12.1335 12.1836 12.4134 15.244 13.1737 12.652 13.782 11.0415 12.0626 15.184 14.372 13.072 12.842 V (mag) 11.209 12.239 12.309 11.8023 12.092 12.082 13.402 13.0224 12.029 13.402 13.662 10.0126 10.097 11.9727 13.192 13.852 13.122 12.4829 13.482 12.8715 14.462 13.032 13.2332 14.602 14.322 14.0534 10.479 13.802 13.0632 11.4135 11.5336 12.4934 14.994 12.5737 12.852 14.122 10.1315 10.6226 15.044 13.972 12.932 13.082 Type A222 F522 B822 DA23 DA12 DA22 DA22 G20 DA42 DA12 DA2 G026 M212 F728 DA22 DB/DC2 DA42 K30 DA42 G515 DA12 DA22 DOZ12 DA12 DA42 DA22 G/K 20 DA32 DA32 K20 G016 DA22 A0/F35 G20 DA22 DAO12 DAH2 M226 A0/F25 DA72 DA32 DA12 2MASS survey (Cutri et al. 2003); 2 McCook & Sion (1999) compilation and online updates; 3 SDSS seventh data release (Abazajian et al. 2009); 4 SDSS, derived with the SEGUE pipeline (Lee et al. 2008) and the transformations by (Lupton 2005); 5 SDSS, derived with the SEGUE pipeline (Lee et al. 2008); 6 van Leeuwen (2007); 7 Koen et al. (2010); 8 Gray et al. (2006); 9 Tycho-2 catalogue of bright sources (Høg et al. 2000); 10 Carney & Latham (1987); 11 from Teff by Laird, Carney, & Latham (1988); 12 Jenkins et al. (2009); 13 Garcés et al. (2011); 14 Hog et al. (1998), for approximate Johnson magnitudes the formulae V=VT–0.090*(BT–VT) and B–V=0.850*(BT–VT) where used; 15 Kharchenko (2001); 16 Bidelman (1985); 17 Salim & Gould (2003); 18 “Subdwarf database” (Østensen 2006); 19 misclassified as a WD by McCook & Sion (1999) according to Stroeer et al. (2007); 20 from Teff by Carney et al. (1994); 21 Galadı́-Enrı́quez et al. (2000)); 22 Henry Draper Catalogue (Cannon & Pickering 1993); 23 Caballero & Solano (2007); 24 Carney et al. (1994); 25 UCAC3 (Zacharias et al. 2009); 26 Hipparcos input catalogue (Turon et al. 1993); 27 Marshall (2007); 28 Cenarro et al. (2007); 29 Lépine & Shara (2005); 30 from Teff by Latham et al. (2002); 31 Bicay et al. (2000); 32 Landolt (1992); 33 Zickgraf et al. (2003); 34 Landolt & Uomoto (2007); 35 Giclas, Burnham, & Thomas (1971); 36 Ivanov (2008); 37 Mermilliod (1994); 38 Buscombe & Foster (1995); 39 Drilling & Landolt (1979); 40 Pesch (1976); 41 van Altena, Lee, & Hoffleit (1995); 42 Kharchenko & Roeser (2009); 43 7th SDSS photometric data release (Adelman-McCarthy & et al. 2009); 44 Tanabé et al. (2008); 45 Downes et al. (2001); 46 Monet et al. (2003); 47 Zapatero Osorio & Martı́n (2004); 48 Greenstein (1984); 49 Zacharias et al. (2005); 50 Malina et al. (1994); 51 Fleming et al. (1996); 52 Koester et al. (2001); 53 Roeser & Bastian (1988); 54 Wegner (1973); 55 Vennes et al. (1997); 56 Lee (1984); 57 Stetson standard in M 5 (Stetson 2000); data available at http://cadcwww.dao.nrc.ca/community/STETSON/standards; 58 Endl et al. (2006); ∗ possible identification problem, see also Section 6.1. c 2012 RAS, MNRAS 000, 1–16 16 E. Pancino et al. Table 4: continued. Star RA (J2000) (hh:mm:ss) SA 104-490 12:44:33.461 G14-24 13:02:01.581 GJ2097 13:07:04.3125 SDSS 08393 13:10:32.073 GJ507.1 13:19:40.136 WD1319+466 13:21:15.081 WD1323-514 13:26:09.651 WD1327-083 13:30:13.646 GJ521 13:39:24.106 WD1408+323 14:10:26.951 SDSS 09626 14:29:51.063 GJ570.2 14:57:32.306 G15-10 15:09:46.026 WD1509+322 15:11:27.661 M5-S1490 15:17:38.6457 G167-50 15:35:31.551 G179-54 15:46:08.251 G224-83 15:46:14.681 WD1553+353 15:55:01.991 G16-20 15:58:18.629 WD1606+422 16:08:22.201 WD1615-154 16:17:55.261 GJ625 16:25:24.626 G180-58 16:28:16.876 WD1626+368 16:28:25.031 WD1637+335 16:39:27.8325 SDSS 13028 16:40:24.183 WD1659-531 17:02:56.3343 G139-16 17:09:47.381 G170-47 17:32:41.636 2MASS J175713 17:57:13.251 TYC4213-617 18:00:02.1414 BD+661071 18:02:10.9214 G184-17 18:40:29.271 WD1837-619 18:42:29.7345 G184-20 18:43:52.501 WD1845+019 18:47:39.081 WD1900+705 19:00:10.251 GJ745A 19:07:05.566 GJ745B 19:07:13.206 WD1918+725 19:18:10.52 WD1914-598 19:18:44.841 WD1919+145 19:21:40.402 WD1936+327 19:38:28.211 G23-14 19:51:49.616 WD2000-561.1 20:04:18.002 WD2004-605 20:09:05.2451 WD2014-575 20:18:54.9052 WD2028+390 20:29:56.161 WD2032+248 20:34:21.886 WD2034-532 20:38:16.841 G24-25 20:40:16.109 WD2039-202 20:42:34.756 SDSS 14511 20:42:42.403 WD2039-682 20:44:21.4754 SDSS 15724 20:47:38.193 Dec (J2000) (dd:pp:ss) –00:25:51.701 –02:05:21.421 +20:48:38.5425 +54:18:33.663 +33:20:47.496 +46:23:23.681 –51:41:35.781 –08:34:29.496 +46:11:11.376 +32:08:36.101 +39:28:25.433 +31:23:44.616 –04:45:06.616 +32:04:17.801 +02:02:25.6057 +27:51:02.201 +39:14:16.401 +62:26:39.601 +35:13:28.701 +02:03:06.119 +42:05:43.201 –15:35:51.901 +54:18:14.776 +44:40:38.286 +36:46:15.401 +33:25:22.3025 +24:02:14.913 –53:14:36.6343 +08:04:25.501 +23:44:11.646 +67:03:40.901 +66:45:54.9614 +66:12:26.3914 +19:36:06.651 –61:51:45.1045 +16:00:34.201 +01:57:35.621 +70:39:51.241 +20:53:16.976 +20:52:37.246 +72:37:24.002 –59:46:33.801 +14:40:43.002 +32:53:19.901 +05:36:45.846 –56:02:47.002 –60:25:41.6051 –57:21:34.0052 +39:13:32.001 +25:03:49.726 –53:04:25.401 +00:33:19.749 –20:04:35.956 –00:34:03.713 –68:05:21.3054 –06:32:13.113 B (mag) 13.0732 13.5227 14.1040 15.304 12.1029 14.552 14.602 12.407 11.509 13.962 15.234 12.6829 12.6726 14.202 15.0857 14.2515 13.9042 13.8615 14.642 11.3442 13.932 13.222 11.809 11.8729 14.022 14.852 15.454 13.572 13.3124 9.549 11.911 11.249 10.939 14.9027 15.012 13.3746 12.732 13.242 12.407 12.387 14.7048 14.342 13.072 13.462 11.4249 — 13.102 13.402 13.222 11.477 14.412 11.239 12.327 15.344 13.192 15.064 V Type (mag) 12.5732 G339 12.8127 K020 12.5440 M112 15.084 A0/F35 10.5729 M212 14.552 DA32 14.602 DA22 12.347 DA42 10.269 M212 13.972 DA32 14.994 A05 11.5429 M212 12.0126 G241 14.112 DA32 14.1057 — 13.5015 G42 13.4142 F42 12.6715 K42 14.752 DA22 10.7542 K20 13.822 DA42 13.422 DA22 10.179 M212 11.1229 G/K20 13.832 DZA62 14.652 DA52 15.264 A05 13.472 DA42 12.6124 K20 8.949 G028 12.011 A344 10.689 — 10.529 F542 14.0827 K20 14.902 DC52 12.6147 G20 12.952 DA22 13.192 DAP49 10.777 M212 10.777 M212 15.1248 DA22 14.392 DA2 13.012 DA32 13.582 DA22 11.0249 G526 15.2050 DA12 13.402 DA12 13.702 DA22 13.372 DA22 11.557 DA22 14.462 DB42 10.619 G053 12.407 DA32 15.114 A0/F05 13.252 DA32 14.874 A0/F25 Star RA (J2000) (hh:mm:ss) WD2105-820 21:13:13.902 WD2111+261 21:13:45.931 WD2117+539 21:18:56.279 WD2115-560 21:19:36.521 WD2122+282 21:24:58.302 WD2136+828 21:33:43.251 WD2134+218 21:36:36.302 WD2140+207 21:42:42.001 WD2147+280 21:49:54.531 WD2152-548 21:56:21.271 GJ851 22:11:30.096 WD2211-495 22:14:11.919 WD2216-657 22:19:48.351 GJ863 22:33:02.236 SDSS 14276 22:42:04.173 WD2251-634 22:55:10.002 WD2309+105 23:12:21.6225 G190-15 23:13:38.826 SDSS 00832 23:30:24.903 WD2329+407 23:31:35.651 WD2331-475 23:34:02.201 G241-64 23:41:24.491 G171-15 23:45:02.719 WD2352+401 23:54:56.251 Dec (J2000) (dd:pp:ss) –81:49:04.002 +26:21:33.201 +54:12:41.259 –55:50:14.201 +28:26:05.002 +83:03:32.401 +22:04:33.002 +20:59:58.241 +28:16:59.801 –54:38:23.001 +18:25:34.296 –49:19:27.269 –65:29:18.111 +09:22:40.706 +13:20:28.613 –63:10:27.002 +10:47:04.2525 +39:25:02.596 –00:09:34.903 +41:01:30.701 –47:14:26.501 +59:24:34.901 +44:40:03.609 +40:27:30.101 B (mag) 13.822 14.922 12.402 14.432 13.802 13.012 14.412 13.402 14.662 13.802 11.377 11.372 14.572 11.917 14.484 — 12.7832 11.5729 15.154 13.852 13.153 13.4515 12.009 15.132 V (mag) 13.612 14.682 12.332 14.282 14.002 13.021 14.452 13.242 14.682 14.302 10.237 11.712 14.432 10.747 14.324 14.282 13.0932 10.9829 14.994 13.822 13.442 12.7015 11.759 14.942 Type DA52 DA62 DA32 DAZ52 DA055 DA32 DA32 DQ62 DB42 DA12 M212 DA12 DZ52 M012 A05 DA2 DA12 F628 A05 DA32 DA12 K20 G056 DQ62 c 2012 RAS, MNRAS 000, 1–16

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The Gaia spectrophotometric standard stars survey - I. Preliminary results

The Gaia spectrophotometric standard stars survey - I. Preliminary results

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