20.2.8 mcmc_samples_msc

This is the DataLink table hosting Monte-Carlo Markov Chain (MCMC) samples for the posterior probability distribution of all parameters derived from the Multiple Source Classifier (MSC, see Section 11.3.5 in the online documentation). 100 random MCMC samples are provided for each source.
The table considers the following parameters:

• •

  effective temperature $T_{\mathrm{eff},1}$ of the primary star

• •

  effective temperature $T_{\mathrm{eff},2}$ of the secondary star

• •

  surface gravity $\log g_1$ of the primary star

• •

  surface gravity $\log g_2$ of the secondary star

• •

  monochromatic extinction at 541.4 nm $A_0$

• •

  metallicity [M/H]

• •

  distance

• •

  logarithmic posterior probability

• •

  logarithmic likelihood

Note this table is not available through the main archive TAP interface. Data are delivered via the Massive Data service indexed by the VO Datalink protocol and described in Chapter 18. For example this can be actioned in the archive user interface by querying the main source catalogue gaia_source and selecting has_mcmc_msc = 't'.

Columns description:

source_id : Unique source identifier (unique within a particular Data Release) (long)

A unique single numerical identifier of the source obtained from gaia_source (for a detailed description see gaia_source.source_id).

solution_id : Solution Identifier (long)

All Gaia data processed by the Data Processing and Analysis Consortium comes tagged with a solution identifier. This is a numeric field attached to each table row that can be used to unequivocally identify the version of all the subsystems that were used in the generation of the data as well as the input data used. It is mainly for internal DPAC use but is included in the published data releases to enable end users to examine the provenance of processed data products. To decode a given solution ID visit https://gaia.esac.esa.int/decoder/solnDecoder.jsp

nsamples : Number of samples in the chain from MSC (short)

The sample size is 100 for all sources.

teff1 : MCMC samples for $T_{\mathrm{eff},1}$ of primary from MSC (float[nsamples] array, Temperature[K])

MCMC samples for posterior probability distribution of the primary star effective temperature from MSC.

teff2 : MCMC samples for $T_{\mathrm{eff},2}$ of secondary from MSC (float[nsamples] array, Temperature[K])

MCMC samples for posterior probability distribution of the secondary star effective temperature from MSC.

logg1 : MCMC samples for $\log g_1$ of primary from MSC (float[nsamples] array, GravitySurface[log cgs])

MCMC samples for posterior probability distribution of the stellar surface gravity of the primary star from MSC.

logg2 : MCMC samples for $\log g_2$ of secondary from MSC (float[nsamples] array, GravitySurface[log cgs])

MCMC samples for posterior probability distribution of the stellar surface gravity of the secondary star from MSC.

azero : MCMC samples for extinction $A_0$ from MSC (float[nsamples] array, Magnitude[mag])

MCMC samples for posterior probability distribution of monochromatic extinction $A_0$ at 541.4 nm from MSC. NB: This is the extinction parameter in the adopted Fitzpatrick extinction law (Fitzpatrick 1999, see Section 11.2.3 of the online documentation).

mh : MCMC samples for the metallicity from MSC (float[nsamples] array, Abundances[dex])

MCMC samples for posterior probability distribution of metallicity [M/H] from MSC.

distancepc : MCMC samples for distance from MSC (float[nsamples] array, Length & Distance[pc])

MCMC samples for posterior probability distribution of the distance from MSC.

log_pos : MCMC samples for the log-posterior from MSC (float[nsamples] array)

Logarithmic posterior probability of MCMC samples from MSC. This can be used in order to replace the MSC priors by a user-chosen prior using importance sampling (for details see Section 11.3.3 in the online documentation).

log_lik : MCMC samples for the log-likelihood from MSC (float[nsamples] array)

Logarithmic likelihood of MCMC samples from MSC. This can be used in order to replace the MSC priors by a user-chosen prior using importance sampling (for details see Section 11.3.3 in the online documentation).