**Table Description:**

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

**Resource Description:**

This is a re-publication the Gaia DR3 RP/BP spectra in the IVOA Spectral Data Model. It presents the continous spectra in sampled form, using a Monte Carlo scheme to decorrelate errors, elaborated in this resource's reference URL. The underlying tables are also available for querying through TAP, which opens some powerful methods for mass-analysing the data.

For a list of **all services and tables** belonging
to this table's
resource, see Information on resource 'Gaia DR3 RP/BP (XP) Monte Carlo sampled spectra'

This table has an **associated publication**. If you use data from it,
it may be appropriate to reference
2022arXiv220800211G
(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:gdr3spec_spectra, year=2022, title={Monte Carlo sampled DR3 XP spectra}, author={Demleitner, M. and Andrae, R.}, url={http://dc.zah.uni-heidelberg.de/tableinfo/gdr3spec.spectra}, howpublished={{VO} resource provided by the {GAVO} Data Center}, doi = {10.21938/W:hWbPah3wBabuqewQULTA} }

In Gaia's DR3, most BP/RP spectra come in “continuous” form only, that is, as coefficients of Gauss-Hermite polynomials. These can be turned into a “sampled” representation using GaiaXPy; however, since the errors are given in the form of covariance matrices for the polynomial coefficients, the errors in the resulting spectra are strongly correleated, which can sometimes result in artefacts in the signal.

To get approximately decorrelated errors and hence sampled spectra usable
with less caution, we apply a scheme of Monte Carlo-sampling different
realisations from the error model of the coefficients. Specifically, given
the covariance matrix *C* defined through the Xp_coefficient_errors
and Xp_coefficient_correlations column in the DR3
xp_continuous_mean_spectrum table, and noting that for a unit
normal-distributed random variable *u*

⟨*u*.*u*^{T}⟩ = 1

holds (⟨*x*⟩ denotes the expectation), we can re-write
the covariance matrix using a Cholesky decomposition into *LL*^{T} as

Hence, *x* = *L**u* is a realisation of the errors satisfying the
covariance matrix. To come up with a sampled spectrum, we now
draw (in this case) 10 samples of the coefficients and have GaiaXPy
convert them to a sampled spectrum.

The source code we used for that is dr3_to_mcsampled.py.

To be on the conservative side of the resolution and the bandwidth, and also to keep storage requirements modest, we have chosen a relatively rough grid over the optical band, that is, bins of 10 nm over the spectral range between 400 and 800 nm.

Sorted by DB column index. [Sort alphabetically]

Name | Table Head | Description | Unit | UCD |
---|---|---|---|---|

source_id | Source Id | Gaia DR3 unique source identifier. Note that this *cannot* be matched against the DR1 or DR2 source_ids. [Note id] | N/A | meta.id;meta.main |

flux | Flux | mean BP + RP combined spectrum flux | W.m**-2.nm**-1 | phot.flux;em.opt |

flux_error | Flux_error | mean BP + RP combined spectrum flux error | W.m**-2.nm**-1 | stat.error;phot.flux;em.opt |

Columns that are parts of indices are marked like this.

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

VO nerds may sometimes need VOResource XML for this table.

**Copyright and such:**

This data is derived from public Gaia DR3 data. Please take note of ESAC's guide on how to acknowledge and cite Gaia results.