Cool White Dwarfs from the SuperCOSMOS and Sloan. Digital Sky Surveys. N. C. Hambly. Wide Field Astronomy Unit, Institute for Astronomy, School of Physics,.
14th European Workshop on White Dwarfs ASP Conference Series, Vol. 334, 2005 D. Koester, S. Moehler
Cool White Dwarfs from the SuperCOSMOS and Sloan Digital Sky Surveys N. C. Hambly Wide Field Astronomy Unit, Institute for Astronomy, School of Physics, University of Edinburgh, Royal Observatory, Blackford Hill, Edinburgh EH9 3HJ, United Kingdom A. P. Digby and B. R. Oppenheimer Astrophysics Department, American Museum of Natural History, New York NY10024, United States of America Abstract. We have used datamining techniques in the SuperCOSMOS Science Archive1 to obtain a large, well defined proper motion and magnitude selected sample of cool white dwarfs. Using accurate 5–colour photometry from the Sloan Digital Sky Survey DR1 and SuperCOSMOS Sky Survey photometry and astrometry, we demonstrate the power of reduced proper motion in obtaining a sample of > 700 white dwarfs. We examine the characteristics of these objects in various two–colour diagrams in conjunction with new model atmosphere predictions recently computed in the SDSS photometric system. Ultimately, we intend to analyse these data with techniques similar to those already used to examine the subdwarf luminosity function (Digby et al. 2003). In this way, we aim to decompose the contribution of thin disk, thick disk and spheroid white dwarfs in the sample to enable computation of accurate luminosity functions for those respective populations.
1.
Introduction
Being the most numerous remnants of their progenitor population, cool white dwarfs (WDs) are important in a number of topical areas of astrophysical research. With mass–to–light ratios of order 10 4 M /L , they are indeed very ‘dark’ matter (Salim et al. 2004); cool WDs are becoming widely used as cosmic chronometers (Smith et al. 2003). Furthermore, the atmospheres of individual cool WDs provide laboratories for the study of astrophysical plasmas under extreme pressure (Kowalski & Saumon 2004). While hot WDs are easily identified via their blue colours in purely photometric surveys – eg. the Palomar Green Survey, Green et al. (1986) and the Sloan Digital Sky Survey, Abazajian et al. (2004) – cool WDs are not so easily detected since they have colours similar to those of the bulk of normal Galactic dwarfs and subdwarfs. However, the technique of ‘reduced proper motion’ (RPM) provides a statistical measure of the relative brightness of the stars in samples for which proper motions are known (eg. Evans 1992, and references therein). Hence, the combination of modern, 1
http://surveys.roe.ac.uk/ssa
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deep photometric surveys like the SDSS with legacy astrometric data enables large scale cool WD surveys. 2.
The SuperCOSMOS Science Archive
The SuperCOSMOS Sky Survey (SSS) is a multi–epoch, multi–colour Schmidt plate digitisation programme (Hambly et al. 2001, and references therein). This provides the necessary astrometric information to enable rough luminosity class assignment for all stellar objects that have significant proper motions (eg. Digby et al. 2003). Recently, the SSS data and several other large imaging datasets (2MASS, USNOB and SDSS) have been loaded into a relational database management system with a data model and user interface specifically designed for generalised datamining applications (Hambly et al. 2004). This public service is known as the SuperCOSMOS Science Archive (SSA; for further information see http://surveys.roe.ac.uk/ssa), and cool WD sample selection is an excellent example to illustrate the versatility of this system. Features of the system that enable datamining (particularly by non–expert users) include provision of Structured Query Language (SQL) interfaces; high speed performance via good hardware design (the 1 billion–row SSS dataset can be trawled in just 15 minutes); and very fast execution for queries predicated on common attributes via good indexing strategies. The following SQL shows an example script that trawls the SSA for stars with significant proper motions: SELECT sss.ra, sss.dec, muacosd, mud, u, g, r, i, z FROM ReliableStars as sss, CrossNeighboursDR1 as x, BESTDR1..PhotoObj as dr1 WHERE (square(muacosd)+square(mud)) > 5*sqrt(square(muacosd*sigmuacosd)+square(mud*sigmud)) and sss.objID=ssaID and dr1.objID=sdssID and sdssPrimary=1 and distanceMins in (SELECT min(distanceMins) FROM CrossNeighboursDR1 WHERE ssaID=x.ssaID) Note the following features: i) the SSA includes prejoining information between the SSS and 2MASS/USNOB/SDSS datasets via tables of ‘cross–neighbours’ which makes joint querying applications straightforward; ii) the final subquery SELECT in the outer WHERE clause selects the nearest neighbour in cases where there is more than one SDSS source near an SSS source; iii) a 5σ cut on proper motions is employed to give good statistical luminosity discrimination via reduced proper motion (see later); and finally iv) the database ‘view’ ReliableStars is defined within the SSA and is used in the above query – this produces a relatively clean sample of well–defined stellar objects from the SSS data. The above query runs in ∼ 10 minutes and returns ∼ 64 × 10 3 rows of data; the resulting dataset is made–to–order rather than being a one–off prepared dataset and can be tuned to any particular application. This opens up datamining possibilities for the user, as opposed to using a prepared dataset like those of Gould & Kollmeier (2004) or Munn et al. (2004) which employ joins of USNOB and SDSS catalogue products (see also papers by von Hippel and Kilic in these proceedings).
Cool WDs from the SuperCOSMOS Science Archive 3.
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Defining a Clean, Complete Sample of WDs
The major problem with using the digitised photographic legacy surveys is that of spurious object contamination, particularly when attempting proper motion surveys to the plate limits (R ∼ 20). The great advantage of joining SSS (or USNOB) catalogues to the SDSS data is that it enables elimination of such spurious detections via consistency checks on position and colour and a robust determination of the error distributions in the photographic image parameters. We have used SQL queries that select control samples with relaxed selection criteria on photographic image morphology (eg. image ellipticity and profile shape), and also a non–proper motion selected control sample, in order to investigate detection completeness and the error distributions in photographic parameters. In this way, we can maximise completeness and minimise spurious object contamination in our final proper motion selected sample. This final selection has cuts on ellipticity and profile shape as a function of magnitude; a photographic magnitude cut of R59F =19.4; consistency checks in photographic colour with the SDSS data via the relations (g − r) = −0.146 + 0.754(BJ − R59F ) (r − i) = −0.144 + 0.836(R59F − IN ), allowing for random errors as a function of magnitude; and finally consistency of proper motion corrected positions with the SDSS measurements at the appropriate epoch. Figure 1 shows a number–magnitude histogram for this sample in terms of SDSS r magnitude; this is log–linear between r ∼ 15 and r = 19.4, which defines the 100% complete magnitude selection range. Figure 2 shows a logarithmic cumulative proper motion number count histogram. For a sample of stars with uniform distribution in space and a given distribution in velocity, this should show a gradient of −3 since proper motion is inversely proportional to distance. Departures from this value are clearly discernible at low and high proper motions. At low proper motion, we expect to lose objects because of the cut on 5σ measurements; additionally, at large distances the assumption of uniform distribution breaks down as population scale heights come into play. At high proper motions, we expect the SSS data to be incomplete due to the limits on pairing (6 arcsec) between individual passband detections on plates separated by several decades in epoch. Figure 3 shows an RPM diagram for the proper motion selected sample; the locus of WDs stands out clearly from the scatter of dwarfs and subdwarfs. The dashed line indicates the selection of WDs by Hr and colour; a similar plot in Hr versus (g − r) is also used to select WDs where a candidate must lie below the selection line in both diagrams to be considered a WD. This procedure yields a sample of 714 WD candidates. 4.
Properties of the WD Sample
Figure 4 shows a (g − r) versus (r − z) two colour diagram for the RPM selected WDs. We also show model atmosphere computations (D. Saumon, priv. comm.) in the SDSS passbands for canonical log g = 8, pure–H atmosphere WDs with
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Figure 1. Number–magnitude counts for the magnitude limited sample selected from the SSS/SDSS data.
Figure 2. Cumulative proper motion counts for the proper motion selected sample of stars.
7000 ≥ Teff /K ≥ 3000 (solid line and circles); the coolest model also has model predictions showing the effects of adding in helium to the level of a pure helium composition (dashed line and crosses). The predicted colours move to blue (r−z) colours due to the now well known effects of H 2 CIA (eg. Gates et al. 2004, and references therein). The object at (r − z) ∼ −1.0 is the previously discovered ultracool WD SDSS 1337 (Harris et al. 2001). We have made a preliminary determination of effective temperatures from pure–H atmospheric model predictions of the ugriz colours of each WD. The results are shown in Figure 5, where we plot the effective temperature and χ 2ν fit statistic of the best fit model. At temperatures above ∼ 6500 K the fits degrade owing to the importance of Balmer line blanketing which is not incorporated in the models, these being aimed at cooler WD studies. At low temperatures, fits obviously need to be done with various H/He compositions in order to more accurately account for the effects of H2 CIA (note that in Figure 4 there appear to be a couple of ultracool objects at (g − r) ∼ +1.2 that may have He–dominated atmospheres).
Cool WDs from the SuperCOSMOS Science Archive
Figure 3. stars.
Reduced proper motion diagram for all proper motion selected
Figure 4. Two–colour diagram for the RPM selected WDs, along with model predicted colours (see text).
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Figure 5. Chi–squared statistic versus best fit effective temperatures for pure–H models.
5.
Future work
The combination of SDSS photometry and legacy photographic plate astrometry is clearly a powerful tool for stellar astronomy. In the near future, we plan to expand this survey by incorporating SDSS DR2 and future releases into the SSA along with astrometry from the northern POSS plate collections. More sophisticated model atmosphere comparisons will be done in order to better model the SEDs of the cool WDs and hence predict their atmospheric parameters and distances. This will then allow decomposition of the sample into different kinematic populations, and will enable computation of luminosity functions with carefully modelled selection and precise analysis along the lines of the subdwarf luminosity function analysis presented in Digby et al. (2003). References Abazajian, K., Adelman–McCarthy, J.K., Ag¨ ueros, M.A., et al. 2004, AJ, 128, 502 Digby, A.P., Hambly, N.C., Cooke, J.A., Reid, I.N., & Cannon, R.D. 2003, MNRAS, 344, 583 Evans, D.W. 1992, MNRAS, 255, 521 Gates, E., Gyuk, G., Harris, H.C., et al. 2004, ApJ, 612, 129 Gould, A., & Kollmeier, J.A. 2004, ApJS, 152, 103 Green, R.F., Schmidt, M., & Liebert, J. 1986, ApJS, 61, 305 Hambly, N.C., MacGillivray, H.T., Read, M.A. et al. 2001, MNRAS, 326, 1279 Hambly, N.C, Read, M.A., Mann, R.G., et al. 2004, in ASP. Conf. Ser. Vol. 314, Proceedings of the 13th meeting on Astronomical Data Analysis Software and Systems, ed. F. Ochsenbein, M.G. Allen, & D. Egret (San Francisco: ASP), 137 Harris, H.C., Hansen, B.M.S., Liebert, J., et al. 2001, ApJ, 549, 109 Kowalski, P.M., & Saumon, D. 2004, ApJ, 607, 970 Munn, J.E., Monet, D.G., Levine, S.E., et al. 2004, AJ, 127, 3034 Salim, S., Rich., R.M., Hansen, B.M., et al. 2004, ApJ, 601, 1075 Smith, J.A., Oswalt, T.D., & Wood, M.A. 2003, in White Dwarfs: Proceedings of the conference held at the Astronomical Observatory of Capodimonte, Napoli, Italy, ed. D. de Martino, R. Silvotti, J.–E. Solheim, & R. Kalytis (Dordrecht: Kluwer), 399
