Table information for 'xpparams2.covs'

General

Table Description:

A table of covariances (as upper triangle matrices) for the stellar parameters in xpparams2.main). This is intended for use as in:

SELECT ..., cov_triu
FROM xpparams2.main NATURAL JOIN xpparams2.covs
WHERE ...

If you only need particular elements of the covariance matrix, you can subscript cov_triu, perhaps as in cov_triu[4] AS teff_xi_cov.

This table is available for ADQL queries and through the TAP endpoint.

Resource Description:

We measure the extinction curves of 220 million stars with Gaia XP spectra and near-infrared photometry from 2MASS and WISE. We use a data-driven model that is developed from 2023MNRAS.524.1855Z, with variable extinction curves, to determine stellar parameters and extinction curves simultaneously.

For bulk downloads and the trained model, see https://doi.org/10.5281/zenodo.10719756.

For a list of all services and tables belonging to this table's resource, see Information on resource 'Three-dimensional maps of the interstellar dust extinction curve within the Milky Way galaxy'

Resource Reference URL: Resource info

Citing this table

This table has an associated publication. If you use data from it, it may be appropriate to reference 2025Sci...387.1209Z (ADS BibTeX entry for the publication) either in addition to or instead of the service reference.

To cite the table as such, we suggest the following BibTeX entry:

@MISC{vo:xpparams2_covs,
  year=2025,
  title={Three-dimensional maps of the interstellar dust extinction curve
within the Milky Way galaxy},
  author={Zhang, X. and Green, G.},
  url={http://dc.g-vo.org/tableinfo/xpparams2.covs},
  howpublished={{VO} resource provided by the {GAVO} Data Center}
}

Resource Documentation

Example: A Dust Map

As an example of what one can do with this data, consider the following ADQL query to generate an all-sky maps of the mean extinction and means of ξ and R(55) for stars at a distance between 400 pc and 600 pc:

SELECT
        source_id/140737488355328 AS hpx,
        avg(ext/(err_ext*err_ext+0.0001)) AS mean_ext_num,
        avg(1/(err_ext*err_ext+0.0001)) AS mean_ext_denom,
        avg(xi/(err_xi*err_xi+0.0001)) as mean_xi_num,
        avg(1/(err_xi*err_xi+0.0001)) as mean_xi_denom,
        avg(r55*r55*r55/(err_r55*err_r55+0.0001)) as mean_inv_r55_num,
        avg(r55*r55*r55*r55/(err_r55*err_r55+0.0001)) as mean_inv_r55_denom
FROM xpparams2.main
WHERE
        mod_parallax BETWEEN 1.67 and 2.5
        AND quality_flags < 8
        AND mod_parallax/err_mod_parallax > 5
        AND err_ext < 0.5
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. Note that we calculate means weighted by the inverse variance for robustness.

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 such a plot of mean_ext_num/mean_ext_denom:

/static/img/xpparams2_ext_map.png

With this data, you can also get an idea of what sort of dust there is via the R(55) (or, if you prefer, ξ) parameter. Because 1/R(55) is better behaved than R(55) (as the extinction curve flattens and then tilts slightly blueward, the parameter R(55) first goes to infinity and then discontinuously jumps to negative infinity), we calculate the inverse-variance-weighted mean of 1/R(55). Below are plots of ξ (top) and R(55) (calculated by inverting the 1/R(55) map):

/static/img/xpparams2_xi_map.png /static/img/xpparams2_r55_map.png

Note that in regions of low extinction, the measurements of ξ (and consequently R(55)) are noisy, and should be treated with caution.

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 xpparams2.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 (crowded) 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:

/static/img/xpparams-omegacen.png

Converting between ξ and R(55)

We have used C source code to turn the ξ as used by 2025Sci...387.1209Z into the more common slope parameter R(55). Since it may be useful in other contexts, too, here is extinction_curve.c and extinction_curve.h.

Columns

Sorted by DB column index. [Sort alphabetically]

NameTable Head DescriptionUnitUCD
source_id Source Id Gaia DR3 unique source identifier. You can match this against gaia.dr3lite on this TAP service. N/A meta.id;meta.main
triu Cov Upper triangle of the covariance matrix of the stellar parameters from xpparams2.main. The variables are, in sequence: T_eff, [Fe/H], logg, ξ, E, and parallax. N/A stat.covariance

Columns that are parts of indices are marked like this.

Other

The following services may use the data contained in this table:

VOResource

VO nerds may sometimes need VOResource XML for this table.