<?xml version='1.0'?><?xml-stylesheet href='/static/xsl/oai.xsl' type='text/xsl'?><ri:Resource created="2021-07-22T10:43:47Z" status="active" updated="2024-02-06T08:59:18Z" version="1.2" xmlns:ri="http://www.ivoa.net/xml/RegistryInterface/v1.0" xmlns:vr="http://www.ivoa.net/xml/VOResource/v1.0" xmlns:vs="http://www.ivoa.net/xml/VODataService/v1.1" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.ivoa.net/xml/RegistryInterface/v1.0 http://vo.ari.uni-heidelberg.de/docs/schemata/RegistryInterface.xsd http://www.ivoa.net/xml/VOResource/v1.0 http://vo.ari.uni-heidelberg.de/docs/schemata/VOResource.xsd http://www.ivoa.net/xml/VODataService/v1.1 http://vo.ari.uni-heidelberg.de/docs/schemata/VODataService.xsd" xsi:type="vs:DataService"><title>GDR2AP VAEX HDF5 download</title><identifier>ivo://org.gavo.dc/gdr2ap/q/download</identifier><curation><publisher>The GAVO DC team</publisher><creator><name>Fouesneau, M.</name></creator><creator><name>Andrae, R.</name></creator><creator><name>Dharmawardena, T.</name></creator><creator><name>Rybizki, J.</name></creator><creator><name>Bailer-Jones, C. A. L.</name></creator><creator><name>Demleitner, M.</name></creator><date role="Updated">2022-05-04T09:51:29Z</date><contact><name>GAVO Data Centre Team</name><address>Mönchhofstrasse 12-14, D-69120 Heidelberg</address><email>gavo@ari.uni-heidelberg.de</email><telephone>+49 6221 54 1837</telephone></contact></curation><content><subject>stellar-properties</subject><subject>milky-way-galaxy</subject><subject>interstellar-dust</subject><description> We estimated the stellar astrophysical parameters of 120 million
stars over the entire sky that have Gaia parallax and photometry from
Gaia DR2, 2MASS, and AllWISE. We provide estimates of log age, log
mass, log temperature, log luminosity, log surface gravity, distance
modulus, dust extinction (A0), and average grain size (R0) along the
lines of sight. In contrast with other catalogs, we do not use a
Galactic model as prior but weakly informative ones. Our estimate and
uncertainties are quantiles, so they are invariant under monotonic
transformations (e.g., log, exp). This means that one can use our
median estimate to obtain the median distance or temperature, for
instance, and likewise for the uncertainties.</description><source format="bibcode">2022arXiv220103252F</source><referenceURL>http://dc.g-vo.org/gdr2ap/q/download/info</referenceURL><type>Catalog</type><contentLevel>Research</contentLevel></content><instrument>Gaia</instrument><coverage><spatial>0/0-11</spatial><temporal>57174 57174</temporal><spectral>1.986e-19 4.966e-19</spectral><footprint ivo-id="ivo://ivoa.net/std/moc">http://dc.g-vo.org/gdr2ap/q/download/coverage</footprint><waveband>Optical</waveband></coverage></ri:Resource>