ivo://\getConfig{ivoa}{authority}/~?\rdId/
{
"G": 25.6884,
"BP": 25.3514,
"RP": 24.7619,
}
Selections from Gaia Data Release 2 (DR2)
This schema contains data re-published from the official
Gaia mirrors (such as ivo://uni-heidelberg.de/gaia/tap) either to
support combining its data with local tables (the various Xlite tables)
or to make the data more accessible to VO clients (e.g., epoch fluxes).
Other Gaia-related data is found in, among others, the gdr2dist, gdr3mock,
gdr3spec, gedr3auto, gedr3dist, gedr3mock, and gedr3spur schemas.
GAIA Collaboration
Catalog
Optical
2018-03-13T08:44:00Z
50
stars
surveys
astrometry
proper-motions
time-domain-astronomy
Gaia
Added a column with the RUWE as a consistency measure.
If you use public Gaia DR2 data in a paper, please take note of
`ESAC's guide`_ on how to acknowledge and cite it.
.. _ESAC's guide: http://gea.esac.esa.int/archive/documentation/GDR2/Miscellaneous/sec_credit_and_citation_instructions/
2015-06-01 2015-06-01
0/0-11
1.986e-19 4.966e-19
Position ICRS BARYCENTER SPHER3 Epoch J2015.5 "ra" "dec" "parallax"
Velocity "pmra" "pmdec" 0
10000
10000
10000
5000
band, ucd
rp, R
bp, B
5000
This is the “gaussianized chi-square”, which for good fits should
approximately follow a normal distribution with zero mean value
and unit standard deviation. Values exceeding, say, +3 thus indicate
a bad fit to the data.
This statistic is computed according to the formula
gof = sqrt(9ν/2) [(χ²/ν)^(1/3) + 2/(9ν) − 1],
where χ² (``astrometric_chi2_al``) is the AL chi-square statistic and
ν = (``astrometric_n_good_obs_al`` − 5) is the number of degrees of
freedom for a source update. Here, 5 is the number of astrometric
parameters. Note that only “good” (i.e. not strongly downweighted)
observations are included in χ² and ν.
An alternative indicator of bad fits is the astrometric excess noise. In
AGIS the source update deals with bad fits by adding astrometric excess
noise to the formal observation noise. This reduces the weight of the
observations and inflates the covariance of the estimated astrometric
parameters correspondingly. However, the chi-square values used to
calculate ``astrometric_gof_al`` do not take into account the astrometric
excess noise, and ``astrometric_gof_al`` can therefore always be used as
a goodness-of-fit indicator of the source solution in AGIS.
For the contents of Gaia DR2,
the source ID consists of a 64-bit integer, least
significant bit = 1 and most significant bit = 64, comprising:
* a HEALPix index number (sky pixel) in bits 36 - 63; by definition the
smallest HEALPix index number is zero.
* a 2-bit Data Processing Centre code in bits 33 - 35; for example
MOD(source_id / 4294967296, 8) can be used to distinguish between
sources initialised via the Initial Gaia Source List by the Torino DPC
(code = 0) and sources otherwise detected and assigned by Gaia
observations (code > 0)
* a 25-bit plus 7 bit sequence number within the HEALPix pixel in bits 1
to 32 split into:
* a 25 bit running number in bits 8 - 32; the running numbers are
defined to be positive, i.e. never zero (except in the case of forced
empty windows)
* a 7-bit component number in bits 1 - 7
This means that the HEALpix index level 12 of a given source is contained
in the most significant bits. HEALpix index of 12 and lower levels can
thus be retrieved as follows:
* HEALpix level 12 = source_id / 34359738368
* HEALpix level 11 = source_id / 137438953472
* HEALpix level 10 = source_id / 549755813888
* HEALpix level n = source_id / 2^35 * 4^(12 - level).
Note that several systematics have been found for both Gaia G and
BP photometry on the level of 10s of mmags. See
:bibcode:`2018A&A...616A..17A` and :bibcode:`2018A&A...616A...4E`
for a description of the problem as well as some proposed empirical
corrections. For sources brighter than 16.5, see also
:bibcode:`2018MNRAS.479L.102C` and :bibcode:`2018A&A...617A.138W`,
where the latter also discusses systematics in BP.
Gaia DR2 source catalogue "light"
This is a “light” version of the full Gaia DR2 gaia_source table,
containing the original astrometric and photmetric columns with just
enough additional information to let careful researchers notice when data
is becomes uncertain and the full error model should be consulted. The
full DR2 is available from numerous places in the VO (in particular from
the TAP services ivo://uni-heidelberg.de/gaia/tap and
ivo://esavo/gaia/tap).
This table also includes a column containing the Renormalized Unit Weight
Error RUWE (GAIA-C3-TN-LU-LL-124-01), a robust measure for the
consistency of the solution.
On this TAP service, there is the table gdr2dist.main containing
distances computed by Bailer-Jones et al (:bibcode:`2018AJ....156...58B`).
If in doubt, use these instead of the parallaxes provided here.
Gaia Data Release 1 (DR1) gaia_source
real
`_). This is
essentially the astrometric_chi2 from the DR2 release, but corrected
for its strong dependency on magnitude and colour. For quality
cuts, this is the recommended quantity to use. Lindegren suggests
using RUWE<1.4 as a quality cut for “good” solutions based on
the shape of the distribution.
The values given here were calculated independently of DPAC and do
not use interpolation on the grids provided by Lindegren et al. Thus,
minor differences from the “official” RUWE values are expected.
As Lindegren et al point out, such differences should not matter
in science applications, though.
]]>
An internal table temporarily used to
insert RUWEs into dr2light. Nonexisting almost always.
10000
Gaia DR2 epoch fluxes
A table of the light curves released with Gaia DR2 (about half a million
in total). In each Gaia band (G, BP, RP), we give epochs, fluxes and
their errors in arrays. We do not include the quality flags (DR2: “may
be safely ignored for many general purpose applications”). You can
access them through the associated datalink service if you select
source_id. You will usually join this table with gaia.dr2light.
We have also removed all entries with NaN observation times; hence,
the array lengths in the different bands can be significantly different,
and the indices in transit_ids do not always correspond to the
indices in the time series.
Furthermore, we only give fluxes and their errors here rather than
magnitudes. Fluxes can be turned into magnitude using::
mag = -2.5 log10(flux)+zero point,
where the zero points assumed for Gaia DR2 are
25.6884±0.0018 in G, 25.3514±0.0014 in BP, and
24.7619±0.0019 in RP (VEGAMAG).
tsdl
source_id
band, ucd
rp, R
bp, B
This is the Gaia DR source id. You will usually use this
no pull in object metadata from dr2light, like this::
SELECT ...
FROM gaia.dr2epochflux
JOIN gaia.dr2light
USING (source_id)
WHERE ...
Times here are computed as follows: First, the observation time is
converted from On-board Mission Time (OBMT) into Julian date in TCB
(Temps Coordonnée Barycentrique). Next, a correction is applied for the
light-travel time to the Solar system barycentre, resulting in
Barycentric Julian Date (BJD). Finally, an offset of 2,455,197.5 days is
applied (corresponding to a reference time $T_0$ at 2010-01-01T00:00:00)
to have a conveniently small numerical value. Although the centroiding
time accuracy of the individual CCD observations is (much) below 1~ms
(e.g. in BP and RP), the G band observation time is averaged over
typically 9 CCD observations taken in a time range of about 44sec.
This removes NaN obs times and their associated flux/errors
import numpy as np
import io
def _parse(s):
return np.genfromtxt(
io.BytesIO(s),
delimiter=',',
autostrip=True,
dtype=np.double)
stuff = vars[dates_name][1:-1].strip()
if not stuff:
vars[fluxes_name] = vars[errors_name] = vars[dates_name] = None
return
dates = _parse(stuff)
vars[fluxes_name] = _parse(vars[fluxes_name][1:-1])[dates==dates]
vars[errors_name] = _parse(vars[errors_name][1:-1])[dates==dates]
vars[dates_name] = dates[dates==dates]
[int(a) for a in @transit_id[1:-1].split(",")]
"bp_obs_time"
"bp_flux"
"bp_flux_error"
"rp_obs_time"
"rp_flux"
"rp_flux_error"
"g_transit_time"
"g_transit_flux"
"g_transit_flux_error"
With data relase 2, the Gaia DPAC released lightcurves for objects
considered variable. This is such a lightcurve (with invalid
points removed), for the \band_human band.
Note that the magnitudes in phot are blindly converted
to flux using the zero point given in the metadata. If
flux_error/flux<0.1, you can use 1.09*flux_error/flux as a good
estimate for the error; else the distribution is so skewed that you
should work with fluxes rather than magnitude.
The zero point in the magnitude metadata has been taken from
the SVO filter profile service.
//timeseries#phot-0
(phot:PhotCal) {
filterIdentifier: "\band_human"
zeroPointFlux: "\zero_point"
magnitudeSystem: Vega
effectiveWavelength: "\effective_wavelength"
value: @flux
}
(ivoa:Measurement) {
value: @flux
statError: @flux_error
}
(ds:Dataset) {
dataProductType: TIMESERIES
title: @title
curation:
(ds:Curation) {
calibLevel: 1
publisher:
(ds:Publisher) {
name: "GAVO Data Center"
publisherId: "ivo://org.gavo.dc"
}
}
target:
(ds:AstroTarget) {
position: [@ra @dec @ssa_location]
}
}
band_short, band_human, band_ucd, effective_wavelength, zero_point
G, GAIA/GAIA2.G, em.opt, 6.23e-7, 2836.53
BP, GAIA/GAIA2.Gbp, em.opt.B, 5.05e-7, 3399.69
RP, GAIA/GAIA2.Grp, em.opt.R, 7.73e-7, 2489.74
epName, fluxName, errorName = {
"G": ("g_transit_time", "g_transit_flux", "g_transit_flux_error"),
"BP": ("bp_obs_time", "bp_flux", "bp_flux_error"),
"RP": ("rp_obs_time", "rp_flux", "rp_flux_error"),
}[self.sourceToken.band]
zeropoint = \zeropoints[self.sourceToken.band]
epochs, fluxes, errors = (
self.sourceToken.row[epName],
self.sourceToken.row[fluxName],
self.sourceToken.row[errorName])
labels = ["obs_time", "flux", "flux_error"]
for tup in zip([e+2455197.5 for e in epochs], fluxes, errors):
if tup[0]==tup[0]:
res = dict(zip(labels, tup))
res["phot"] = -2.5*math.log10(res["flux"])+zeropoint
yield res
with base.getTableConn() as conn:
res = list(conn.queryToDicts(
"SELECT * FROM gaia.dr2light WHERE source_id=%(source_id)s"
" LIMIT 2",
{"source_id": self.sourceToken.sourceId}))
ts_data = res[0]
ts_data["band"] = self.sourceToken.band
ts_data["ssa_location"] = pgsphere.SPoint.fromDegrees(
ts_data["ra"], ts_data["dec"])
return ts_data
Gaia DR2 time series datalink
This service gives photometric
time series for the roughly 500000 objects for which Gaia DR2
gives epoch photometry. Note that this is different from
the ESAC datalink service in that it gives split time series
for the three Gaia bands. There is a link to the original
ESAC time series in our table, though. The ids passed to
this service are Gaia DR2 source ids.
from gavo import svcs
class TSDescriptor(ProductDescriptor):
def __init__(self, pubDID):
# accept both naked sourceIds and proper pubDIDs here
# pubDIDs can end with source_id (all bands)
# and source_id/bandpass (just the referenced band)
try:
if pubDID.startswith("\pubDIDBase"):
self.pubDID = pubDID
parts = pubDID.split("/")
if parts[-1] in ["BP", "RP", "G"]:
self.sourceId = int(parts[-2])
self.metaBandpass = parts[-1]
else:
self.sourceId = int(parts[-1])
self.metaBandpass = None
else:
self.pubDID = "\pubDIDBase%s"%pubDID
self.sourceId = int(pubDID)
self.metaBandpass = None
except ValueError:
raise svcs.UnknownURI(
"%s does not look like a Gaia DR2 publisher DID"
" from us."%pubDID)
self.suppressAutoLinks = True
with base.getTableConn() as conn:
res = list(conn.queryToDicts(
"SELECT * FROM \schema.dr2epochflux"
" WHERE source_id=%(source_id)s",
{"source_id": int(self.sourceId)}))
if not res:
raise svcs.UnknownURI(
"Gaia DR2 has no epoch photometry for %s"%self.sourceId)
self.row = res[0]
return TSDescriptor(pubDID)
if descriptor.pubDID is None:
# We're generating meta for an in-SSA-response block
# and can't link to the original ESA data in this way.
return
yield descriptor.makeLink(
"http://geadata.esac.esa.int/data-server/data?"
"RETRIEVAL_TYPE=epoch_photometry&ID=%s"%descriptor.sourceId,
description="Merged-band custom time series from ESAC",
contentType="application/x-votable+xml",
contentLength=15000)
if descriptor.pubDID is None:
# We're generating meta for an in-SSA-response block
# and can't vary band in this way.
return
for band in ["G", "BP", "RP"]:
yield descriptor.makeLink(
makeAbsoluteURL("\rdId/tsdl/dlget?ID=%s&BANDPASS=%s"%(
descriptor.pubDID, band)),
description="%s time series for Gaia DR2 %s."%(
band, descriptor.sourceId),
semantics="#this"
if descriptor.metaBandpass is None
or descriptor.metaBandpass==band
else "#coderived",
contentType="application/x-votable+xml",
contentLength=15000,
contentQualifier="#timeseries")
# interpreted by the first data function
yield MS(InputKey, name="BANDPASS", type="text",
description="Gaia bandpass to generate the time series for.",
required=True, multiplicity="single",
values=MS(Values, options=[MS(Option, content_=b)
for b in ["G", "BP", "RP"]]))
from gavo import rsc
descriptor.band = args["BANDPASS"]
dd = rd.getById("make_instance")
descriptor.data = rsc.Data.createWithTable(dd,
rd.getById("instance_"+descriptor.band))
descriptor.data = rsc.makeData(
dd,
data=descriptor.data,
forceSource=descriptor)
from gavo import formats
return (base.votableType,
formats.getFormatted("vodml", descriptor.data))
Gaia DR2 Timeseries SSA Table
This table contains about 1.5 Million photometric timeseries
for roughly 0.5 Million objects. Photometry is available in the Gaia
G, BP, and RP bands for epochs between 2014-07-25 and 2016-05-25.
The spectra are available in VOTable format with the timeseries
annotation proposed in the Nadvornik et al IVOA note.
tsdl
ssa_pubDID
//ssap#mixc
//obscore#publishSSAPMIXC
TimeInterval TCB BARYCENTER "time_min" "time_max"
-
SELECT *
FROM gaia.dr2light
JOIN gaia.dr2epochflux
USING (source_id)
with base.getTableConn() as conn:
for row in conn.queryToDicts(self.sourceToken):
yield row
@accrefBase = makeAbsoluteURL("\rdId/tsdl/dlget?ID=%s&"%(
@source_id))
@bandpass = "Gaia G"
@times = @g_transit_time
@bandpass_lo = 325e-9
@bandpass_mid = 600e-9
@bandpass_hi = 1000e-9
yield row
@bandpass = "Gaia BP"
@times = @bp_obs_time
@bandpass_lo = 325e-9
@bandpass_mid = 500e-9
@bandpass_hi = 670e-9
yield row
@bandpass = "Gaia RP"
@times = @rp_obs_time
@bandpass_lo = 625e-9
@bandpass_mid = 750e-9
@bandpass_hi = 1000e-9
yield row
"\rdId/%s/%s"%(
@source_id, @bandpass.split()[-1])
@accrefBase+"BANDPASS="+@bandpass.split()[-1]
10000
"application/x-votable+xml"
"\schema.dr2epochflux"
makeAbsoluteURL("\rdId/preview/qp/%s/%s"%(
@bandpass.split()[-1], @source_id))
"image/png"
if @times is None:
raise IgnoreThisRow()
@times = [v for v in @times if v==v]
@time_min = min(@times)+2455197.5-stc.JD_MJD
@time_max = max(@times)+2455197.5-stc.JD_MJD
@ra
@bandpass
datetime.datetime(2018, 4, 25)
(@time_max+@time_min)/2
@dec
"Gaia DR2 %s lightcurve for %s"%(
@bandpass, @source_id)
len(@times)
\standardPubDID
@bandpass_lo
@bandpass_hi
"V*"
"Gaia DR2 %s"%@source_id
@time_max-@time_min
5e-7
"Gaia DR2"
"archival"
"survey"
"timeseries"
"2018gdr2.reptE...5B"
"ABSOLUTE"
"ABSOLUTE"
GDR2 TS SSAP
survey
archival
MAXREC=1
query
timeseries
Gaia DR2 light curves SSA
This service exposes about 0.5 million
light curves of stars classified as variable by the Gaia analysis
system through the VO SSAP protocol. The lightcurves are
published per-band and are also available through obscore.
parsed = pql.PQLFloatPar.fromLiteral(
inPars.get("TIME", None), "TIME")
yield parsed.getSQLForInterval(
"time_min", "time_max", outPars)
GDR2light SCS
Gaia DR2-light Cone Search
testQuery.ra: 312.76222908
testQuery.dec: 6.690190
testQuery.sr: 0.0001
Gaia DR2 epoch photometry preview maker"
spec_id
band, sourceId = inputTable.args["spec_id"].split("/")
timeCol, fluxCol = {
"G": ("g_transit_time", "g_transit_flux"),
"RP": ("rp_obs_time", "rp_flux"),
"BP": ("bp_obs_time", "bp_flux"),}[band]
sourceId = int(sourceId)
with base.getTableConn() as conn:
res = list(conn.query(f"SELECT {timeCol}, {fluxCol}"
" FROM \schema.dr2epochflux"
" WHERE source_id=%(sourceId)s",
locals()))
if not res:
raise svcs.UnknownURI("No such spectrum known here")
return ("image/png", SpectralPreviewMaker.get2DPlot(
list(zip(res[0][0], res[0][1])), linear=True))
/tap/sync
row = self.getFirstVOTableRow()
self.assertEqual(len(row), 27)
self.assertEqual(row["radial_velocity"], None)
self.assertAlmostEqual(row["ra"], 312.866560795216)
self.assertAlmostEqual(row["ruwe"], 1.0473799705505, places=6)
/tap/sync
row = self.getFirstVOTableRow()
self.assertEqual(len(row), 12)
self.assertEqual(len(row["bp_obs_time"]), len(row["bp_flux"]))
self.assertEqual(len(row["bp_obs_time"]), len(row["bp_flux_error"]))
# if the following is larger than 47, NaN-removal is broken
self.assertEqual(len(row["bp_obs_time"]), 47)
self.assertEqual(len(row["rp_obs_time"]), 48)
self.assertEqual(len(row["rp_obs_time"]), len(row["rp_flux"]))
self.assertEqual(len(row["rp_obs_time"]), len(row["rp_flux_error"]))
self.assertEqual(len(row["g_transit_time"]),
len(row["g_transit_flux"]))
self.assertEqual(len(row["g_transit_flux"]),
len(row["g_transit_flux_error"]))
self.assertEqual(row["rp_obs_time"][0], 1761.3505379502)
for val in row["bp_obs_time"]+row["rp_obs_time"]+row["g_transit_time"]:
assert(val==val)
tsdl/dlmeta
bySemantics = {}
for row in self.getVOTableRows():
bySemantics.setdefault(row["semantics"], []).append(row)
self.assertEqual([
r["access_url"].split("q2/tsdl/")[-1]
for r in bySemantics["#this"]], [
"dlget?ID=ivo://org.gavo.dc/~?gaia/q2/1737549120045621120&BANDPASS=G",
"dlget?ID=ivo://org.gavo.dc/~?gaia/q2/1737549120045621120&BANDPASS=BP",
"dlget?ID=ivo://org.gavo.dc/~?gaia/q2/1737549120045621120&BANDPASS=RP"])
self.assertEqual(
bySemantics["#this"][0]["content_type"],
"application/x-votable+xml")
self.assertEqual(
set(r["content_qualifier"] for r in bySemantics["#this"]),
{'#timeseries'})
tsdl/dlmeta
bySemantics = {}
for row in self.getVOTableRows():
bySemantics.setdefault(row["semantics"], []).append(row)
self.assertEqual([
r["access_url"].split("q2/tsdl/")[-1]
for r in bySemantics["#this"]], [
"dlget?ID=ivo://org.gavo.dc/~?gaia/q2/1737549120045621120/BP&BANDPASS=BP"])
self.assertEqual([
r["access_url"].split("q2/tsdl/")[-1]
for r in bySemantics["#coderived"]], [
"dlget?ID=ivo://org.gavo.dc/~?gaia/q2/1737549120045621120/BP&BANDPASS=G",
"dlget?ID=ivo://org.gavo.dc/~?gaia/q2/1737549120045621120/BP&BANDPASS=RP"])
tsdl/dlget
self.assertXpath("//m:INSTANCE[@dmtype='votable:SpaceFrame']/m:ATTRIBUTE[@dmrole='epoch']",
{"value": "J2015.5", "dmtype": "ivoa:string"})
self.assertXpath(
"//v:PARAM[@ID=//m:ATTRIBUTE[@dmrole='longitude']/@ref]",
{"value": "312.995282218611", "ucd": "pos.eq.ra"})
self.assertXpath("//v:TR[1]/v:TD[1]", {
None: "2456958.850349069"})
self.assertXpath("//v:FIELD[@name='flux']",
{"ucd": "phot.flux;em.opt.B"})
self.assertFalse(b"NaN" in self.data)
tsdl/dlget
self.assertXpath("//m:VODML/m:TEMPLATES/m:INSTANCE["
"@dmtype='votable:Coords'][1]//m:ATTRIBUTE[@dmrole='epoch']/@value",
{None: "J2015.5"})
self.assertXpath(
"//v:PARAM[@ID=//m:ATTRIBUTE[@dmrole='longitude']/@ref]",
{"value": "312.995282218611", "ucd": "pos.eq.ra"})
self.assertXpath("//v:FIELD[@name='flux']",
{"ucd": "phot.flux;em.opt.R"})
self.assertXpath("//v:FIELD[@name='phot']",
{"ucd": "phot.mag;em.opt.R"})
self.assertXpath("//v:TR[1]/v:TD[1]", {
None: "2456958.850435664"})
self.assertXpath("//v:TR[1]/v:TD[2]", {
None: EqualingRE(r"16.9429\\d*")})
ssa/ssap.xml
# The TIME condition should filter out the G time series (which
# starts a bit earlier).
rows = self.getVOTableRows()
self.assertEqual(len(rows), 2) # if 3, then TIME matching is broken
self.assertEqual(rows[0]["ssa_targname"],
'Gaia DR2 1737539915930483712')
self.assertEqual(rows[0]["ssa_length"], 21)