2023-06-12T11:51:04Z 20 Parameters of 220 million stars from Gaia BP/RP (XP) spectra We present astrophysical parameters of 220 million stars, based on Gaia XP spectra and near-infrared photometry from 2MASS and WISE. Instead of using ab initio stellar models, we develop a data-driven model of Gaia XP spectra as a function of the stellar parameters, with a few straightforward built-in physical assumptions. This resource is a VO re-publication of the resulting catalog of stellar parameters. For bulk downloads, the covariances, the trained model, and more, see https://zenodo.org/record/7811871. stellar-properties milky-way-galaxy sky-surveys Zhang, Xiangyu Green, G.M. Rix, Hans-Walter Gaia RP/BP Spectrometer Gaia 2023MNRAS.524.1855Z Research Catalog Optical Unveiling the Milky Way dust extinction curve in 3D 5 and err_ext < 0.2 group by hpx This exploits the fact that Gaia source ids can be `converted to HEALPixes`_ to produce a map and shows how to do useful quality cuts that allow relatively careless use of the data. If you use TOPCAT to execute this on the GAVO DC TAP service (select Asynchronous mode and make sure you set Max Rows to something more than 50000, because that is how many pixels our map will have), you can do a sphere plot, then add a Healpix control. In it, select the table resulting from this query, manually set the HEALPix level to 6 and configure the Axis to Aitoff projection in the Galactic system to arrive at a plot like this: .. _converted to HEALPixes: https://dc.g-vo.org/gedr3dist/q/cone/info#note-id .. image:: /static/img/xpparams-dustmap.png Example: Metallicity of a Globular Cluster ------------------------------------------ Try the following query yielding data on Omega Cen and illustrating how to match with the local Gaia DR3 catalogue in order to constrain proper motions:: SELECT xpp.*, g.pmra, g.pmdec, g.phot_g_mean_mag, g.phot_bp_mean_mag, g.phot_rp_mean_mag FROM xpparams.main as xpp JOIN gaia.edr3lite as g USING (source_id) WHERE distance(xpp.ra, xpp.dec, 201.697, -47.479472)<0.5 AND distance(g.pmra, g.pmdec, -3.24, -6.73)<1.25 AND xpp.quality_flags < 8 AND xpp.mod_parallax/xpp.err_mod_parallax > 5. AND xpp.feh_confidence > 0.5 AND xpp.err_fe_h < 0.2 Again in TOPCAT, try a 3d plot of RA, Dec and 1/mod_parallax. You will want to manually cut the parallax axis a bit to get rid of (presumably spurious) background stars. Make fe_h the aux axis in the “Form“ tab. If you look “from above“, you will see that the quality cuts have punched a hole into the cluster. If you look from the side, you can see that the cluster stars are (by and large) rather metal-poor compared to foreground stars: .. image:: /static/img/xpparams-omegacen.png ]]> source_id 10000 This table is originally at https://zenodo.org/record/7811871/files/extinction_curve.txt?download=1. ========== ====================== wavelength extinction_curve 392.0 4.004029750823975 402.0 3.932934284210205 412.0 3.8616459369659424 422.0 3.790104627609253 432.0 3.66058611869812 442.0 3.6425302028656006 452.0 3.5208635330200195 462.0 3.42183256149292 472.0 3.3702077865600586 482.0 3.31054949760437 492.0 3.2349464893341064 502.0 3.103348970413208 512.0 3.028446674346924 522.0 2.913299798965454 532.0 2.8309452533721924 542.0 2.766111135482788 552.0 2.658158779144287 562.0 2.5784859657287598 572.0 2.5361363887786865 582.0 2.4805185794830322 592.0 2.412776470184326 602.0 2.356668472290039 612.0 2.313209056854248 622.0 2.272326707839966 632.0 2.2307708263397217 642.0 2.1915647983551025 652.0 2.156630516052246 662.0 2.1017379760742188 672.0 2.040684938430786 682.0 2.0002477169036865 692.0 1.9592469930648804 702.0 1.9099318981170654 712.0 1.8628478050231934 722.0 1.8250858783721924 732.0 1.7893791198730469 742.0 1.7572511434555054 752.0 1.7151219844818115 762.0 1.7081632614135742 772.0 1.6803325414657593 782.0 1.6145453453063965 792.0 1.566452145576477 802.0 1.5282924175262451 812.0 1.4971987009048462 822.0 1.4678758382797241 832.0 1.4417716264724731 842.0 1.4134937524795532 852.0 1.3825805187225342 862.0 1.3539363145828247 872.0 1.3050405979156494 882.0 1.2772356271743774 892.0 1.2389837503433228 902.0 1.2139592170715332 912.0 1.193802833557129 922.0 1.155206322669983 932.0 1.143110990524292 942.0 1.113704800605774 952.0 1.075573444366455 962.0 1.0743821859359741 972.0 1.0339452028274536 982.0 0.9810613989830017 992.0 1.0053104162216187 1235.0 0.6821472644805908 1662.0 0.4082775115966797 2159.0 0.2695426046848297 3352.6 0.13694290816783905 4602.8 0.09808244556188583 ========== ====================== / XP ap-pars 81.1106109 2.77958655 0.001 if inPars[inputKeys[0].name] is None: if inputKeys[0].xtype=="interval": inPars["quality_flags"] = [0, 8] else: inPars["quality_flags"] = "< 8" yield base.getSQLForField(inputKeys[0], inPars, outPars) cone/scs.xml rows = self.getVOTableRows() # the following assertion is for the default quality cut to be # active (without it, it's 17 rows) self.assertEqual(len(rows), 16) self.assertAlmostEqual(rows[0]["teff"]-4593.015, 0, 3) self.assertAlmostEqual(rows[1]["logg"], 4.4473667, 6) self.assertEqual(rows[2]["source_id"], "3235947833912912896") cone/form self.assertHasStrings( # the following assertion is for quality_flags < 8 being evaluated "Matched: 16", "4593.015", # Teff of 0 "81.4371948", # RA of 1 "32.41", # dist of 2 )