Articles from Data

  • Gaia DR2: A light version and light curves

    2018-05-07 Markus Demleitner
    screenshot: topcat and matplotlib

    Topcat is doing datalink, and our little python script has plotted a two-color time series of RMC 18 (or so I think).

    If anyone ever writes a history of the VO, the second data release of Gaia on April 25, 2018 will probably mark its coming-of-age – at least if you, like me, consider the Registry the central element of the VO. It was spectacular to view the spike of tens of Registry queries per second right around 12:00 CEST, the moment the various TAP services handing out the data made it public (with great aplomb, of course).

    In GAVO's Data Center we also carry Gaia DR2 data. Our host institute, the Zentrum für Astronomie in Heidelberg, also has a dedicated Gaia server. This gives relieves us from having to be a true mirror of the upstream data release. And since the source catalog has lots and lots of columns that most users will not be using most of the time, we figured a “light” version of the source catalog might fill an interesting ecological niche: Behold gaia.dr2light on the GAVO DC TAP service, containing essentially just the basic astrometric parameters and the diagonal of the covariance matrix.

    That has two advantages: Result sets with SELECT * are a lot less unwieldy (but: just don't do this with Gaia DR2), and, more importantly, a lighter table puts less load on the server. You see, conventional databases read entire rows when processing data, and having just 30% of the columns means we will be 3 times faster on I/O-bound tasks (assuming the same hardware, of course). Hence, and contrary to several other DR2-carrying sites, you can perform full sequential scans before timing out on our TAP service on gaia.dr2light. If, on the other hand, you need to do debugging or full-covariance-matrix error calculations: The full DR2 gaia_source table is available in many places in the VO. Just use the Registry.

    Photometry via TAP

    A piece of Gaia DR2 that's not available in this form anywhere else is the lightcurves; that's per-transit photometry in the G, BP, and RP band for about 0.5 million objects that the reduction system classified as variable. ESAC publishes these through datalink from within their gaia_source table, and what you get back is a VOTable that has the photometry in the three bands interleaved.

    I figured it might be useful if that data were available in a TAP-queriable table with lightcurves in the database. And that's how gaia.dr2epochflux came into being. In there, you have three triples of arrays: the epochs (g_transit_time, bp_obs_time, and rp_obs_time), the fluxes (g_transit_flux, bp_flux, and rp_flux), and their errors (you can probably guess their names). So, to retrieve G lightcurves where available together with a gaia_source query of your liking, you could write something like:

    SELECT g.*, g_transit_time, g_transit_flux
FROM gaia.dr2light AS g
LEFT OUTER JOIN gaia.dr2epochflux
USING (source_id)
WHERE ...whatever...

    – the LEFT OUTER JOIN arranges things such that the g_transit_time and g_transit_flux columns simply are NULL when there are no lightcurves; with a normal (“inner”) join, rows without lightcurves would not be returned in such a query.

    To give you an idea of what you can do with this, suppose you would like to discover new variable blue supergiants in the Gaia data (who knows – you might discover the precursor of the next nearby supernova!). You could start with establishing color cuts and train your favourite machine learning device on light curves of variable blue supergiants. Here's how to get (and, for simplicity, plot) time series of stars classified as blue supergiants by Simbad for which Gaia DR2 lightcurves are available, using pyvo and a little async trick:

    from matplotlib import pyplot as plt
import pyvo

def main():
  simbad = pyvo.dal.TAPService(
    "http://simbad.u-strasbg.fr:80/simbad/sim-tap")
  gavodc = pyvo.dal.TAPService("http://dc.g-vo.org/tap")

  # Get blue supergiants from Simbad
  simjob = simbad.submit_job("""
    select main_id, ra, dec
    from basic
    where otype='BlueSG*'""")
  simjob.run()

  # Get lightcurves from Gaia
  try:
    simjob.wait()
    time_series = gavodc.run_sync("""
      SELECT b.*, bp_obs_time, bp_flux, rp_obs_time, rp_flux
      FROM (SELECT
         main_id, source_id, g.ra, g.dec
         FROM
        gaia.dr2light as g
         JOIN TAP_UPLOAD.t1 AS tc
         ON (0.002>DISTANCE(tc.ra, tc.dec, g.ra, g.dec))
      OFFSET 0) AS b
      JOIN gaia.dr2epochflux
      USING (source_id)
      """,
      uploads={"t1": simjob.result_uri})
  finally:
    simjob.delete()

  # Now plot one after the other
  for row in time_series.table:
    plt.plot(row["bp_obs_time"], row["bp_flux"])
    plt.plot(row["rp_obs_time"], row["rp_flux"])
    plt.show(block=False)
    raw_input("{}; press return for next...".format(row["main_id"]))
    plt.cla()

if __name__=="__main__":
  main()

    If you bother to read the code, you'll notice that we transfer the Simbad result directly to the GAVO data center without first downloading it. That's fairly boring in this case, where the table is small. But if you have a narrow pipe for one reason or another and some 105 rows, passing around async result URLs is a useful trick.

    In this particular case the whole thing returns just four stars, so perhaps that's not a terribly useful target for your learning machine. But this piece of code should get you started to where there's more data.

    You should read the column descriptions and footnotes in the query results (or from the reference URL) – this tells you how to interpret the times and how to make magnitudes from the fluxes if you must. You probably can't hear it any more, but just in case: If you can, process fluxes rather than magnitudes from Gaia, because the errors are painful to interpret in magnitudes when the fluxes are small (try it!).

    Note how the photometry data is stored in arrays in the database, and that VOTables can just transport these. The bad news is that support for manipulating arrays in ADQL is pretty much zero at this point; this means that, when you have trained your ML device, you'll probably have to still download lots and lots of light curves rather than write some elegant ADQL to do the filtering server-side. However, I'd be highly interested to work out how some tastefully chosen user defined functions might enable offloading at least a good deal of that analysis to the database. So – if you know what you'd like to do, by all means let me know. Perhaps there's something I can do for you.

    Incidentally, I'll talk a bit more about ADQL arrays in a blog post coming up in a few weeks (I think). Don't miss it, subscribe to our feed).

    SSAP and Obscore

    If you're fed up with bleeding-edge tech, the light curves are also available through good old SSAP and Obscore. To use that, just get Splat (or another SSA client, preferably with a bit of time series support). Look for a Gaia DR2 time series service (you may have to update the service list before you find it), enter (in keeping with our LBV theme) S Dor as position and hit “Lookup” followed by “Send Query”. Just click on any result to just view the time series – and then apply Splat's rich tool set to it.

    Update (8.5.2018): Clusters

    Here's another quick application – how about looking for variable stars in clusters? This piece of ADQL should get you started:

    SELECT TOP 100
  source_id, ra, dec, parallax, g.pmra, g.pmdec,
  m.name, m.pmra AS c_pmra, m.pmde AS c_pmde,
  m.e_pm AS c_e_pm,
  1/dist AS cluster_parallax
FROM
  gaia.dr2epochflux
  JOIN gaia.dr2light AS g USING (source_id)
  JOIN mwsc.main AS m
  ON (1=CONTAINS(
    POINT(g.ra, g.dec),
    CIRCLE(m.raj2000, m.dej2000, rcluster)))
WHERE IN_UNIT(pmdec, 'deg/yr') BETWEEN m.pmde-m.e_pm*3 AND m.pmde+m.e_pm*3

    – yes, you'll want to constrain pmra, too, and the distance, and properly deal with error and all. But you get simple lightcurves for free. Just add them in the SELECT clause!

  • The Earth is Our Telescope

    2017-07-31 Markus Demleitner
    Antares 2007-2012 neutrino coverage

    The coverage of the 2007-2012 Antares neutrinos, with positional uncertainties scaled by three.

    At our Heidelberg data center, we have have already published some neutrino data, for instance the Amanda-II neutrino candiates, the IceCube-40 neutrino candidates, and the 2007-2010 Antares results.

    That latter project has now given us updated data, for the first time including timestamps, available as the Antares service.

    Now, if you look at the coverage (above), you'll notice at least two things: For one, there's no data around the north pole. That's because the instrument sits beyond the waters of the Mediterranean sea, not far from where some of you may now enjoy your vacation. And it is using the Earth as its filter – it's measuring particles as they come ”up” and discards anything that goes “down”. Yes, neutrinos are strange beasts.

    The second somewhat unusual thing is that the positional uncertainties are huge compared to what we're used to from optical catalogs: a degree is not uncommon (we've scaled the error circles by a factor of 3 in the image above, though). And that requires some extra care when working with the data.

    In our table, we have a column origin_est that actually contains circles. Hence, to find images of the “strongest” neutrinos in our obscore table, you could write:

    SELECT * FROM
ivoa.obscore AS o
JOIN (
  SELECT top 10 * FROM antares.data
  ORDER BY n_hits desc
) AS n
ON 1=INTERSECTS(
  s_region,
  origin_est)

    in a query to our TAP service.

    But of course, this only gets really exciting when you can hope that perhaps that neutrino was emitted by some violent event that may have been observed serendipitously by someone else. That query then is (and we're using all the neutrinos now):

    SELECT * FROM
ivoa.obscore AS o
JOIN antares.data as n
ON
   epoch_mjd between t_min-0.01 and t_max+0.01
  AND
    dataproduct_type='image'
  AND
    1=INTERSECTS(origin_est, s_region)

    On our data center, this doesn't yield anything at the moment (it does, though, if you do away with the spatial constraint, which frankly suprised me a bit). But then if you went and ran this query against obscore services of active observatories? And perhaps had your computer try and figure out whether anything unusual is seen on whatever you find?

    We think that would be really nifty, and right after we've published a first version of our little pyVO course (which is a bit on the back burner, but watch this space), we'll probably work that out as a proper pyVO use case.

    And meanwhile: In case you'll be standing on the shores of the Mediterranean this summer, enjoy the view and think of the monster deep down in there waiting for neutrinos to detect – and eventually drop into our data center.

  • See Who's Kinking the Sky

    2017-03-14 Markus Demleitner

    A new arrival in the GAVO Data Center is UCAC5, another example of a slew of new catalogs combining pre-existing astrometry with Gaia DR1, just like the HSOY catalog we've featured here a couple of weeks back.

    That's a nice opportunity to show how to use ADQL's JOIN operator for something else than the well-known CONTAINS-type crossmatch. Since both UCAC5 and HSOY reference Gaia DR1, both have, for each object, a notion which element of the Gaia source catalog they correspond to. For HSOY, that's the gaia_id column, in UCAC5, it's just source_id. Hence, to compare results from both efforts, all you have to do is to join on source_id=gaia_id (you can save yourself the explicit table references here because the column names are unique to each table.

    So, if you want to compare proper motions, all you need to do is to point your favourite TAP client's interface to http://dc.g-vo.org/tap and run:

    SELECT
    in_unit(avg(uc.pmra-hsoy.pmra), 'mas/yr') AS pmradiff,
    in_unit(avg(uc.pmde-hsoy.pmde), 'mas/yr') AS pmdediff,
    count(*) as n,
    ivo_healpix_index (6, raj2000, dej2000) AS hpx
    FROM hsoy.main AS hsoy
    JOIN ucac5.main as uc
    ON (uc.source_id=hsoy.gaia_id)
    WHERE comp IS NULL    -- hsoy junk filter
    AND clone IS NULL     -- again, hsoy junk filter
    GROUP BY hpx

    (see Taylor et al's All of the Sky if you're unsure what do make of the healpix/GROUP BY magic).

    Of course, the fact that both tables are in the same service helps, but with a bit of upload magic you could do about the same analysis across TAP services.

    Just so there's a colourful image in this post, too, here's what this query shows for the differences in proper motion in RA:

    (equatorial coordinates, and the aux axis is a bit cropped here; try for yourself to see how things look for PM in declination or when plotted in galactic coordinates).

    What does this image mean? Well, it means that probably both UCAC5 and HSOY would still putt kinks into the sky if you wait long enough.

    In the brightest and darkest points, if you waited 250 years, the coordinate system induced by each catalog on the sky would be off by 1 arcsec with respect to the other (on a sphere, that means there's kinks somewhere). It may seem amazing that there's agreement to at least this level between the two catalogs – mind you, 1 arcsec is still more than 100 times smaller than you could see by eye; you'd have to go back to the Mesolithic age to have the slightest chance of spotting the disagreement without serious optical aids. But when Gaia DR2 will come around (hopefully around April 2018), our sky will be more stable even than that.

    Of course, both UCAC5 and HSOY are, indirectly, standing on the shoulders of the same giant, namely Hipparcos and Tycho, so the agreement may be less surprising, and we strongly suspect that a similar image will look a whole lot less pleasant when Gaia has straightened out the sky, in particular towards weaker stars.

    But still: do you want to bet if UCAC5 or HSOY will turn out to be closer to a non-kinking sky? Let us know. Qualifications („For bright stars...”) are allowed.

  • PPMXL+Gaia DR1=HSOY in the Heidelberg Data Center

    2017-01-17 Markus Demleitner

    The stunning precision of Gaia's astrometry is already apparent in the first release of the data obtained by the satellite, available since last September. However, apart from the small TGAS subset (objects already observed by the 90ies HIPPARCOS astrometry satellite) there is no information on the objects' proper motions in DR1.

    Until Gaia-quality proper motions will become available in DR2, the HSOY catalog – described in Altmann et al's paper Hot Stuff for One Year (HSOY) freshly up in arXiv and online at http://dc.g-vo.org/hsoy – can help if you can live with somewhat lesser-quality kinematics.

    It derives proper motions for roughly half a billion stars from PPMXL and Gaia DR1, which already gives an unprecedented source for 4D astrometry around J2015. And you can start working with it right now. The catalog is available in GAVO's Heidelberg data center (TAP access URL: http://dc.g-vo.org/tap; there's also an SCS service). Fire up your favourite TAP or SCS client (our preference: TOPCAT) and search for HSOY.

    An all-sky heatmap showing much larger errors south of -30 deg.

    Oh, and in case you're new to the whole TAP/ADQL game: There's our ADQL introduction, and if you're at a German astronomical institution, we'd be happy to hold one of our VO Days at your institute – just drop us a mail.

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