JuanCastillo29/space-mission-intelligence-data huggingface dataset:
The science operations of Gaia are conducted by the Science Operations Centre (SOC) located at the European Space Astronomy Centre (ESAC) in Villafranca del Castillo, Spain. The Gaia SOC is an integral part of the Gaia DPAC (Sect. 7) and therefore has, in addition to the classical roles outlined in this section, a number of other responsibilities towards DPAC. These other responsibilities are
– forming the main hub among the six data processing centres (DPCs; Sect. 7.4) of DPAC;
– providing system architecture and support functions for DPAC and hosting the main database (MDB) of the mission;
– operating the daily pipeline (Sect. 7.1) and disseminating all data products to other DPCs (hub-and-spokes topology);
– operating and co-developing the astrometric global iterative solution (AGIS; Sect. 7.2);
– operating and co-developing the mission archive (Sect. 7.3).
Article number, page 19 of 36 on all daily processing systems (Sect.

7.1), and provide advice to the project scientist and mission manager on identified improvements.
6.4. Payload calibrations and special operations Although Gaia is essentially a self-calibrating mission, which means that the required instrument calibrations are derived from the science data themselves (Sect. 3.1), dedicated payload calibrations are periodically performed (Crowley et al. 2016b) in close collaboration with the DPAC payload experts group. These calibrations include the following.
1. Post-scan-pixel acquisitions for serial-CTI monitoring (Kohley et al. 2014): This calibration uses SIF data (Sect. 3.3.8) with the VPUs in operational mode but with the SM object detection disabled; one virtual object (Sect. 3.3.8) is defined to trigger the image-area readout. 2. Virtual object acquisitions for CCD-PEM non-uniformity monitoring (Sect. 3.3.2): This calibration uses virtual objects with the VPUs in operational mode but with the SM object detection disabled. In addition, SIF is activated for short times to verify the TDI-gate settings; 3. SIF acquisitions for flat-band, voltage-shift monitoring induced by ionising radiation damage (Kohley et al. 2014): This calibration exclusively uses SIF data with the VPUs in service mode. 4. Charge-injection calibration in the sky mappers for monitoring the radiation state of these detectors: This calibration exclusively uses SIF data with the VPUs in operational mode.

[Figure: Fig. 9. Evolution of the astrometric image quality (averaged over all AF CCDs) as a function of time as quantified by the Cramér-Rao centroiding diagnostic. A lower value indicates a better image quality and centroiding performance. Red lines refer to the preceding field of view; blue lines to the following field of view. The discontinuities refer to the decontaminations (dotted vertical lines) and refocussings (dashed blue and red lines; Sect. 6.4). OBMT stands for onboard mission timeline in units of six-hour revolutions since launch (Sect. 5.3.3). Gaia Data Release 1 is based on data covering the interval OBMT = 1078.3795– 2751.3518, delimited by dashed black lines.]
. 2016), that is sophisticated and close to real time. This system is deployed in the daily pipeline. First Look operates on IDT and spacecraft housekeeping data and produces thousands of summary diagnostic quantities as well as daily instrument calibrations and the second on-ground attitude reconstruction (OGA-2). These diagnostics are reported daily and inspected by qualified First-Look scientists to identify possible actions to improve the performance of the spacecraft and/or of the on-ground data processing. A group of DPAC-wide payload experts provide expertise in interpreting First-Look diagnostics, monitor the health of the satellite based Article number, page 20 of 36

Across-scan sample

[Figure: Fig. 11. Example sky-mapper SIF image of a dense area (part of the globular cluster ω Cen). The image shows 6000 × 1000 samples (each composed of 2 × 2 pixels) only, corresponding to 11′.8 × 5′.8 on the sky. The inset shows a 60 × 20 sample (7′′ × 7′′) zoom of the central part of the cluster.]
astrometry with standard errors of a few dozen µas. A virtual
object-based scheme to acquire full focal plane transit data for
the brightest 175 stars in the sky is currently in preparation.

100 50 0 50 100 Along-scan sample

[Figure: Fig. 10. Example sky-mapper SIF image of a very bright star (HIP 48036, R Leo, a Mira-type variable star). The image shows the central 200 × 200 samples (each composed of 2 × 2 pixels) only. The colour scale is logarithmic and denotes counts, with white indicating saturation. The large, asymmetric saturation blob and the saw-edge pattern to the saturation are not optical properties of the point spread function but the result of known saturation behaviour of the readout node. The contours, evenly spaced in log-intensity space, indicate a model point spread function, aligned by eye, for a solar-type star in the astrometric field. Differences between the data and the model are due to the different spectral type, different part of the focal plane (sky mapper versus astrometric field), different across-scan motion, and the absence of read-out effects, saturation, charge-transfer inefficiency, and a high-frequency, wave-front-error feature of the primary mirror (quilting effect, resulting in diagonal spikes) in the model.]
the entrance port of the integrating sphere. The integrating sphere of OP3 thus provides a homogeneous beam on its output port, with calibrated probes controlling the flux level of the QTH. For the spectral calibration, this optical path was used without N2 flushing to obtain transmission spectra through the ambient air, which hence exhibited water vapor spectral features. The
N2 flush was also replaced by a CO2 flush to obtain measurements of the CO2 absorptions (Section II.C.2.a).
The OP5 path uses another QTH source to illuminate solid samples in reflection. The reflected flux is then directed toward the same collimator used for OP1 using dedicated optics. MAJIS then images the samples. Solid samples relevant for the spectral calibration are 1) a Wavelength Calibration Standard (WCS) with multiple absorption features over the VISNIR, and 2) a calcite pressed-pellet with complementary absorption bands. Due to thermal constraints, data are limited to wavelengths < 3000 nm (samples are not cooled).
The ICU sub-system is mounted inside the entrance baffle of MAJIS. It comprises a VISNIR incandescent lamp and an IR MEMS source illuminating a common diffusive coating14. The signal of the two sources is collected by rotating the internal scan mirror in correspondence with the ICU boresight. Each source is equipped with a spectral filter to provide reference spectral signatures on the MAJIS signals. Specifically, the VISNIR source uses a didymium filter, and the IR source a polystyrene filter12. The primary purpose of the ICU is to monitor the evolution of the spectral performances of the instrument during in-flight operations. ICU measurements were acquired during the LDO and IAS campaigns to provide reference data for the ground and pre-flight performances of the instrument.
The astrometric crowding limit of Gaia is around 1 050 000 ob-
Sect. 8.4 for more details). At such (and) high(er) densities, win-
dow overlap and truncation is common and, because the onboard
detection prioritises bright stars, faint-star completeness is im-
pacted. In order to deblend the data observed in crowded regions,
in particular for the BP/RP photometry and RVS spectroscopy, it
is mandatory to have knowledge about the positions and fluxes
of all (contaminating) sources in the field, down to a limit that
is a few magnitudes fainter than the survey limit. The source
environment analysis package (Sect. 7.2; Harrison 2011) pro-
vides this information, combining the one-dimensional, along-
scan Gaia data for each source obtained under different orienta-
tion angles into a two-dimensional image of its immediate sur-
roundings. In order to support the deblending in extremely dense
areas with high scientific importance, in particular Baade’s Win-
dow into the Galactic bulge and the ω Centauri globular cluster
(NGC5139), special sky-mapper SIF data are acquired for these
areas (Fig. 11). These data facilitate reaching a fainter complete-
ness limit than the onboard detection limit, for instance 2 mag
deeper in the core of ω Cen. The reduction and analysis of these
(undersampled) data are special, off-line activities that are not
yet operational. The ultimate aim also is to derive astrometry
and photometry from the SIF data for faint stars not entering the
nominal data reduction of Gaia because of crowding. Also, an
extension of the dense area SIF scheme to cover the Sgr I bulge
window, the Large and Small Magellanic Clouds, and four other
globular clusters (M22 = NGC6656, M4 = NGC6121, NGC104
= 47 Tuc, and NGC4372) was activated in mid-2016.
The onboard detection is effective at the bright end down to mag-
nitude G ∼ 3 mag: the detection efficiency is ∼94% at G = 3 mag
and drops rapidly for brighter stars to below 10% for G = 2 mag.
The 230 brightest stars in the sky (G < 3 mag, loosely re-
ferred to as very bright stars) receive a special treatment to en-
sure complete sky coverage at the bright end (Martín-Fleitas
et al. 2014; Sahlmann et al. 2016). Using the Gaia observing
schedule tool (GOST; https://gaia.esac.esa.int/gost/
index.jsp), their transit times and across-scan transit positions
are predicted, based on propagated Hipparcos astrometry and
the operational scanning law, and SIF data are acquired for these
stars in the sky mapper (SM) and subsequently downloaded.
These data comprise raw, two-dimensional images (2540 × 983
samples, each composed of 2 × 2 pixels, integrated over 2.85 s),
which are heavily saturated in the stellar core (Fig. 10). The re-
duction and analysis of these data are special, off-line activities,
which are not yet operational. The ultimate scientific quality of
these data will primarily depend on the achievable quality of the
calibration of the sky-mapper detectors and point spread func-
tions and, in particular, at large distances from the stellar core far
beyond the extension of regular SM windows, which are needed
to avoid saturation. Because centroiding of these images in the
uncalibrated detector frame can be carried out to within 50 µas,
it is expected that a single-measurement precision of 100 µas
through standard stars.

In preparation of the Gaia mission, therefore, extensive observational campaigns to collect the relevant spectro-photometric data took place (Pancino et al. 2012) as well as monitoring campaigns to establish the photometric stability of candidate standard stars (Altavilla et al. 2015).

For the RVS, a compilation of radial-velocity standards was undertaken (Crifo et al. 2010; Soubiran et al. 2013), while for the solar system object processing, data on solar analogues had to be compiled to calibrate the interpretation of the Gaia photometry.

A set of ∼30 Gaia FGK benchmark stars have been extensively studied to provide a reference for the astrophysical parameter inference system (Jofré et al. 2014; Blanco-Cuaresma et al. 2014; Heiter et al. 2015; Jofré et al. 2015; Hawkins et al. 2016).

One of the largest auxiliary observation programmes is the GBOT effort already mentioned in Sect. 5.3.2.

Data publication. All the results from the DPAC processing are published as Gaia data releases and this is a specific task within the consortium. It encompasses the extensive scientific validation of the data (using the combination of all DPAC results), the documentation of the data processing and the results thereof, the incorporation of the data into a publicly accessible archive, and the provision of various tools to enhance the interrogation and scientific exploitation of the Gaia data, including pre-computed cross-matches to other large surveys (Marrese et al. 2016). For Gaia DR1, the validation and publication of the data is summarised in Arenou et al. (2016); Salgado et al. (2016).
7.4. Organisation The DPAC is a large entity in charge of a set of complex, large, and interdependent data processing tasks. The DPAC is internally organised into a set of relatively independently operating units, called coordination units (CUs), which roughly correspond to the cyclic data processing tasks outlined above, with some units also in charge of daily data processing. The actual processing takes place on hardware spread around six data processing centres in Europe (located at ESAC, Toulouse, Cambridge, Genève, Barcelona, and Torino), which communicate through the main database located at ESAC (hub-and-spokes topology). The main database also houses the DPAC end data products from which the public data releases are produced. The DPAC is managed at the top level by an executive body consisting of the leaders of the coordination units and representatives from the data processing centres. The Executive is supported in its tasks by the DPAC project office (PO). More details on the DPAC organisational structure can be found in Mignard et al. (2008); O’Mullane et al. (2009); Mercier et al. (2010).
that is sometimes observed with the non-optimal TDI gate (Sect. 3.3.2).

The max function in Eq. (5) hides these complications and simply returns a constant, bright-star parallax noise floor, at σϖ = 7 µas, for stars with 3 ≤G ≤12.09 mag (assuming a G2V spectral type with V −I = 0.75 mag).

Reaching this performance, however, will require full control of all error sources and fully (iteratively) calibrated instrument and attitude models, which can only reasonably be expected in the final data release.

For stars at the very faint end (around G ≈20.5 mag and fainter), the number of transits resulting in science data being available on ground may be reduced disproportionately as a result of onboard priority management and data deletion (Sects. 3.3.9 and 5.3.1), onboard magnitude-estimation errors (Sect. 8.4), and finite onboard detection and confirmation probabilities (Sect. 3.3.8).

Their standard errors can hence be larger than predicted through this model.

For sky-averaged position errors σ0 [µas] at mid-epoch (i.e. the middle of the observation interval) and for (annual) properArticle number, page 25 of 36 motion errors σµ [µas yr−1], the following relations can be used:

σ0 = 0.743σϖ; σα∗= 0.787σϖ; σδ = 0.699σϖ; σµ = 0.526σϖ; σµα∗= 0.556σϖ; σµδ = 0.496σϖ, (7)
where the asterisk denotes true arcs on the sky (e.g. σα∗= σα cos δ).

The expected astrometric correlations between the five astrometric parameters were discussed in detail by Holl & Lindegren (2012) and Holl et al. (2012a).

The standard errors vary over the sky as a result of the scanning law (Sect. 5.2).

The main variation is with ecliptic latitude β.

The mean, i.e. ecliptic longitude averaged, variations with β are provided in Tab. 1.

The (approximate) ecliptic latitude can be calculated from the equatorial coordinates (α, δ) or the galactic coordinates (l, b) using
sin β ≈ 0.9175 sin δ −0.3978 cos δ sin α ≈ 0.4971 sin b + 0.8677 cos b sin(l −6◦.38). (8)
The performance equations presented here refer to the standard errors, i.e. the precision of the astrometry.

An assessment of (residual) systematic errors in the astrometry, linking to its accuracy and in particular to the parallax zero point, is much more difficult to provide.

For astrometry, a potential contributor to systematic parallax errors are unmodelled, Sun-synchronous basic angle variations (Sect. 3.3.4).

The metrology data derived from the basic angle monitor should ultimately allow, after careful calibration (Lindegren et al. 2016), the limitation of possible systematic effects in the final data release to µas levels.
8.2.

Photometry For median straylight conditions over a spacecraft rotation period, the single field-of-view transit photometric standard error σG, in units of mag and including 20% margin (Sect. 8), of the G-band photometry is parametrised well by
σG[mag] = 1.2 10−3 � 0.04895z2 12 + 1.8633z12 + 0.0001985 �1/2 , (9)
where zx(G) is defined in Eq. (5).

As for astrometry (Sect. 8.1), the bright-star errors were set to a constant noise floor.
the single field-of-view transit photometric standard error σG, in units of mag and including 20% margin (Sect. 8), of the G-band photometry is parametrised well by
σG[mag] = 1.2 10−3 � 0.04895z2 12 + 1.8633z12 + 0.0001985 �1/2 , (9)
where zx(G) is defined in Eq. (5).

As for astrometry (Sect. 8.1), the bright-star errors were set to a constant noise floor.

For the integrated BP and RP bands, a suitable parametrisation of the single field-of-view transit photometric standard errors σBP/RP, in units of mag and including 20% margin, for median straylight conditions depends on G and on a V −I colour term,
σBP/RP [mag] = 10−3 �
10aBP/RPz2 11 + 10bBP/RPz11 + 10cBP/RP�1/2 ,
(10)
where = −0.000562(V −I)3 + 0.044390(V −I)2 + +0.355123(V −I) + 1.043270;
aBP = −0.000400(V −I)3 + 0.018878(V −I)2 + +0.195768(V −I) + 1.465592;
bBP = +0.000262(V −I)3 + 0.060769(V −I)2 + −0.205807(V −I) −1.866968;
cBP = −0.007597(V −I)3 + 0.114126(V −I)2 + −0.636628(V −I) + 1.615927;
aRP = −0.003803(V −I)3 + 0.057112(V −I)2 + −0.318499(V −I) + 1.783906;
bRP = −0.001923(V −I)3 + 0.027352(V −I)2 + −0.091569(V −I) −3.042268. (11)
cRP
Resulting end-of-mission, median-straylight photometric errors can be estimated by division of the single field-of-view transit photometric standard errors from Eqs. (9) and (10) by the square root of the number of transits Nobs, after taking an appropriate calibration error σcal (at field-of-view transit level) into account as follows:
� (σG/1.2)2 + σ2
G,cal
σG,end−of−mission
= 1.2
; (12)
Nobs
� (σBP/RP/1.2)2 + σ2
BP/RP, cal
σBP/RP, end−of−mission
= 1.2
,(13)
Nobs
where Nobs = Nobs(β) is given in Tab. 1 and the factors 1.2 refer to the 20% science margin (Sect. 8).

A realistic estimate of the field-of-view transit level calibration error is σG,cal = 1 millimag (Evans et al. 2016) and σBP/RP, cal = 5 milli-mag.

As described in Bailer-Jones et al. (2013; see also Liu et al. 2012), the BP/RP spectro-photometric data, sometimes in combination with the astrometric and spectroscopic data, allow one to classify objects and to estimate their astrophysical parameters.

The accuracy of the estimation of the astrophysical parameters depends in general on G and on the value of the astrophysical parameters themselves; in addition, the strong and ubiquitous degeneracy between effective temperature and extinction limits the accuracy with which either parameter can be estimated, in particular for faint stars (e.g.

Bailer-Jones 2011).

As an example, for FGKM stars at G = 15 mag with less than two magnitudes extinction, effective temperature Teff can be estimated to 75–250 K, extinction to 0.06–0.15 mag, surface gravity log10(g) to 0.2–0.5 dex, and metallicity [Fe/H] to 0.1–0.3 dex, where the ranges delimit optimistic and pessimistic estimates in view of template mismatch and calibration errors.
8.3.

Spectroscopy Spectroscopy is being collected for a subset of the astrometric and photometric data to derive radial velocities and to perform stellar parametrisation.
mag with less than two magnitudes extinction, effective temperature Teff can be estimated to 75–250 K, extinction to 0.06–0.15 mag, surface gravity log10(g) to 0.2–0.5 dex, and metallicity [Fe/H] to 0.1–0.3 dex, where the ranges delimit optimistic and pessimistic estimates in view of template mismatch and calibration errors.
8.3.

Spectroscopy Spectroscopy is being collected for a subset of the astrometric and photometric data to derive radial velocities and to perform stellar parametrisation.

For the vast majority of stars, namely those that are faint, the individual transit spectra are too noisy to derive transit-level radial velocities.

As a result, a single, end-ofmission composite spectrum is first reconstructed by co-adding all spectra collected during all RVS CCD crossings throughout the mission lifetime.

A single, mission-averaged radial velocity is then extracted from this end-of-mission composite spectrum by cross-correlation with a synthetic template spectrum.

The Article number, page 26 of 36

[Table: Table 1.

Numerical factor to be applied to the sky-averaged astrometric standard errors of Eqs. (4) and (7) for the five astrometric parameters as a function of ecliptic latitude β, including the effect of the variation of the end-of-mission number of transits over the sky.

The quantity Nobs in column 4 denotes the end-of-mission number of focal plane passages for AF, BP, and RP (both fields of view combined; recall that Gaia Data Release 1 is based on 14 months of data, corresponding on average to 16 field-of-view transits).

For RVS, the number of focal plane transits is a factor 4/7 = 0.57 smaller (Sect. 3.3.7).

The transit numbers in column 4 are based on an assumed 6% dead time (data loss).
-mission number of transits over the sky.

The quantity Nobs in column 4 denotes the end-of-mission number of focal plane passages for AF, BP, and RP (both fields of view combined; recall that Gaia Data Release 1 is based on 14 months of data, corresponding on average to 16 field-of-view transits).

For RVS, the number of focal plane transits is a factor 4/7 = 0.57 smaller (Sect. 3.3.7).

The transit numbers in column 4 are based on an assumed 6% dead time (data loss).

For the faintest objects (G ≳20 mag or GRVS ≳14 mag), the actual losses are larger (Sect. 5.3.1).
| sin β| βmin [◦] βmax [◦] Nobs
α∗ δ ϖ
µα∗ µδ
0.025 0.0 2.9 61 1.026 0.756 1.180 0.725 0.542 0.075 2.9 5.7 61 1.021 0.757 1.180 0.722 0.542 0.125 5.7 8.6 62 1.002 0.751 1.169 0.710 0.537 0.175 8.6 11.5 62 0.993 0.752 1.167 0.703 0.539 0.225 11.5 14.5 63 0.973 0.751 1.158 0.689 0.538 0.275 14.5 17.5 65 0.952 0.742 1.143 0.673 0.533 0.325 17.5 20.5 66 0.934 0.740 1.136 0.662 0.533 0.375 20.5 23.6 68 0.901 0.730 1.119 0.640 0.525 0.425 23.6 26.7 71 0.861 0.718 1.098 0.614 0.515 0.475 26.7 30.0 75 0.819 0.705 1.072 0.584 0.506 0.525 30.0 33.4 80 0.765 0.691 1.043 0.548 0.493 0.575 33.4 36.9 87 0.701 0.673 1.009 0.500 0.477 0.625 36.9 40.5 98 0.631 0.650 0.970 0.541 0.461 0.675 40.5 44.4 122 0.535 0.621 0.922 0.381 0.437 0.725 44.4 48.6 144 0.469 0.607 0.850 0.327 0.423 0.775 48.6 53.1 106 0.554 0.636 0.808 0.386 0.443 0.825 53.1 58.2 93 0.603 0.654 0.779 0.422 0.456 0.875 58.2 64.2 85 0.641 0.669 0.755 0.447 0.467 0.925 64.2 71.8 80 0.668 0.680 0.731 0.466 0.473 0.975 71.8 90.0 75 0.688 0.706 0.713 0.481 0.490 Sky-average 0.0 90.0 81 0.787 0.699 1.000 0.556 0.496
Table 2.

Parameters for the RVS performance model defined in Eq. (14).

MP stands for metal poor ([Fe/H] = −1.5).