JuanCastillo29/space-mission-intelligence-data huggingface dataset:
.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).

SpT B0V B5V A0V A5V F0V G0V G5V K0V K1IIIMP K4V K1III a 0.90 0.90 1.00 1.15 1.15 1.15 1.15 1.15 1.15 1.15 1.15 b 50.00 26.00 5.50 4.00 1.50 0.70 0.60 0.50 0.39 0.29 0.21 V −I [mag] -0.31 -0.08 0.01 0.16 0.38 0.67 0.74 0.87 0.99 1.23 1.04 V −GRVS [mag] -0.35 -0.08 0.02 0.19 0.46 0.80 0.87 1.03 1.17 1.45 1.24
cross-correlation method finds the best match of the observed spectrum within a set of predefined synthetic spectra with different atmospheric parameters and subsequently assigns the astrophysical parameters of the best-fit template to the observed target.

For the few million brightest targets, single field-of-view transit spectra will be used to derive associated epoch radial velocities.

For this subset of objects also, the radial velocities of the components of (double-lined) spectroscopic binaries will be estimated using TODCOR (Zucker & Mazeh 1994).

The radial-velocity performance is normally quantified through the sky-average, end-of-mission radial-velocity robust formal error, σv,rad, in units of km s−1 and including 20% margin (Sect. 8).

This error depends on spectral type and magnitude and, for GRVS up to ∼16 mag, is suitably parameterised by
σv,rad [km s−1] = σfloor + b exp(a[V −12.7]), (14)
where a and b are spectral-type dependent constants (Tab. 2) and V denotes Johnson V magnitude.

The bright-star performance is limited by a noise floor σfloor.

Recent investigations based on preliminary assessments of real data suggest that the bright-star
noise floor will ultimately be at the level of 0.5 km s−1, and pos-
sibly better.

Systematics in the radial velocities are expected to
be kept under control to within a few 100 m s−1.]

As described in Recio-Blanco et al. (2016), stellar parametrisation will be performed on the RVS spectra of individual stars with GRVS <∼14.5 mag. Stars with GRVS <∼12.5 mag are efficiently parametrised, including reliable estimations of the αelement abundances with respect to iron. Typical internal errors for FGK metal-rich (−0.5 <∼[M/H] <∼0.25 dex) and intermediate-metallicity (−1.25 <∼[M/H] <∼−0.5 dex) stars (dwarfs and giants) are around 40 K in Teff, 0.10 dex in log10(g), 0.04 dex in [M/H], and 0.03 dex in [α/Fe] at GRVS = 10.3 mag. These errors degrade to 155 K in Teff, 0.15 dex in log10(g), 0.10 dex in [M/H], and 0.1 dex in [α/Fe] at GRVS ∼12 mag. Similar errors in Teff and [M/H] are found for A-type stars, while the surface-gravity derivation is more precise (errors of 0.07 and 0.12 dex at GRVS = 12.6 and 13.4 mag, respectively). For the faintest stars, with GRVS >∼13–14 mag, the input of effective temperature derived from the BP/RP spectro-photometry will allow the final, RVS-based parametrisation to be improved.
Article number, page 27 of 36

8.4.
Gaia is the space-astrometry mission of the European Space Agency which, after successful commissioning, started scientific operations in mid-2014. The primary science goal of Gaia is to examine the kinematical, dynamical, and chemical structure and evolution of our Milky Way. In addition, the data of Gaia will have a strong impact on many other areas of astrophysical research, including stellar evolution and physics, star formation, stellar variability, the distance scale, multiple stars, exoplanets, solar system bodies, unresolved galaxies and quasars, and fundamental physics. With a focal plane containing more than 100 CCD detectors, Gaia surveys the heavens and repeatedly observes all objects down to G ≈20.7 mag during its five-year nominal lifetime. The science data of Gaia comprise absolute astrometry (positions, proper motions, and parallaxes), broadband photometry in the unfiltered G band, low-resolution blue and red (spectro-)photometry (BP and RP), and integrated GBP of the spacecraft and the mission ultimately allows, after five years, stellar parallaxes (distances)
to be measured with stan-
dard errors less than 10 µas for stars brighter than G ≈ 13 mag,
around 30 µas for stars around G ≈ 15 mag, and around 600 µas
around G ≈ 20 mag. End-of-life photometric standard errors are
in the milli-magnitude regime. The spectroscopic data allow the
measurement of (mission-averaged) radial velocities with stan-
The Gaia Data Processing and Analysis Consortium (DPAC) is
responsible for the processing and calibration of the Gaia data.
The first intermediate release of Gaia data (Gaia Collaboration
et al. 2016) comprises astrometry (Lindegren et al. 2016), pho-
tometry (van Leeuwen et al. 2016), and variability (Eyer et al.
2016); later releases will include BP/RP and RVS data. The val-
idation of the data is described in Arenou et al. (2016) and the
Gaia Archive is described in Salgado et al. (2016).

Acknowledgements.

This work has made use of results from the European Space Agency (ESA) space mission Gaia, the data from which were processed by the Gaia Data Processing and Analysis Consortium (DPAC).

Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement.

The Gaia mission website is http: //www.cosmos.esa.int/gaia.

The authors are current or past members of the ESA and Airbus DS Gaia mission teams and of the Gaia DPAC.
2; the Agenzia Spaziale Italiana (ASI) through grants I/037/08/0, I/058/10/0, 2014-025-R.0, and 2014-025-R.1.2015 to INAF and contracts I/008/10/0 and 2013/030/I.0 to ALTEC S.p.A.; the Italian Istituto Nazionale di Astrofisica (INAF); the Netherlands Organisation for Scientific Research (NWO) through grant NWO-M-614.061.414 and through a VICI grant to A.

Helmi; the Netherlands Research School for
Astronomy (NOVA); the Polish National Science Centre through HARMONIA grant 2015/18/M/ST9/00544; the Portugese Fundação para a Ciência e a Tecnologia (FCT) through grants PTDC/CTE-SPA/118692/2010, PDCTE/CTEAST/81711/2003, and SFRH/BPD/74697/2010; the Strategic Programmes PEst-OE/AMB/UI4006/2011 for SIM, UID/FIS/00099/2013 for CENTRA, and UID/EEA/00066/2013 for UNINOVA; the Slovenian Research Agency; the Spanish Ministry of Economy MINECO-FEDER through grants AyA2014-55216, AyA2011-24052, ESP2013-48318-C2-R, and ESP2014-55996-C2-R and MDM-2014-0369 of ICCUB (Unidad de Excelencia María de Maeztu); the Swedish National Space Board (SNSB/Rymdstyrelsen); the Swiss State Secretariat for Education, Research, and Innovation through the ESA PRODEX programme, the Mesures d’Accompagnement, and the Activités Nationales Complémentaires; the Swiss National Science Foundation, including an Early Postdoc.Mobility fellowship; the United Kingdom Rutherford Appleton Laboratory; the United Kingdom Science and Technology Facilities Council (STFC) through grants PP/C506756/1 and ST/I00047X/1; and the United Kingdom Space Agency (UKSA) through grants ST/K000578/1 and ST/N000978/1.

The GBOT programme uses observations collected at (i) the European Organisation for Astronomical Research in the Southern Hemisphere with the VLT Survey Telescope (VST), under ESO programmes 092.B-0165, 093.B-0236, 094.B-0181, 095.B-0046, 096.B-0162, and 097.B-0304, (ii) the Liverpool Telescope, which is operated on the island of La Palma by Liverpool John Moores University in the Spanish Observatorio del Roque de los Muchachos of the Instituto de Astrofísica de Canarias with financial support from the United Kingdom Science and Technology Facilities Council, and (iii) telescopes of the Las Cumbres Observatory Global Telescope Network.

In addition to the authors of this paper, there are numerous people who have made essential contributions to Gaia, for instance those employed in the design, manufacturing, integration, and testing of the spacecraft and its modules, subsystems, and units.

Many of those will remain unnamed yet spent countless hours, occasionally during nights, weekends, and public holidays, in cold offices and dark clean rooms.
o, Carlos Casas, Francesco Castellini, David Chapmann, Nebil Cinar, Mark Clements, Giovanni Colangelo, Ana Colorado McEvoy, Vincente Companys, Federico Cordero, Sylvain Damiani, Paolo de Meo, Fabio de Santis, Fabienne Delhaise, Gianpiero Di Girolamo, Yannis Diamantidis, John Dodsworth, Ernesto Dölling, Jane Douglas, Jean Doutreleau, Dominic Doyle, Mark Drapes, Frank Dreger, Peter Droll, Gerhard Drolshagen, Bret D

urrett, Christina Eilers, Yannick Enginger, Alessandro Ercolani, Robert Ernst, Hugh Evans, Fabio Favata, Stefano Ferreri, Daniel Firre, Michael Flegel, Melanie Flentge, Alan Flowers, Jens Freihöfer, César Gómez Hernández, Juan Manuel Garcia, Wahida Gasti, José Gavira, Frank GeerArticle number, page 29 of 36 ling, Franck Germes, Gottlob Gienger, Bénédicte Girouart, Bernard Godard, Nick Godfrey, Roy Gouka, Cosimo Greco, Robert Guilanya, Kester Habermann, Manfred Hadwiger, Ian Harrison, Angela Head, Martin Hechler, Kjeld Hjortnaes, Jacolien Hoek, Frank Hoffmann, Justin Howard, Arjan Hulsbosch, José Jiménez, Simon Kellett, Andrea Kerruish, Kevin Kewin, Oliver Kiddle, Sabine Kielbassa, Volker Kirschner, Holger Krag, Benoiît Lainé, Markus Landgraf, Mathias Lauer, Robert Launer, Santiago Llorente, Guillermo Lorenzo, James Madison, Filip Marinic, Arturo Martín Polegre, Ander Martínez, Marco Massaro, Luca Michienzi, Ali Mohammadzadeh, Richard Morgan-Owen, Prisca Mühlmann, Michael Müller, Pablo Munoz, Petteri Nieminen, Alfred Nillies, Wilfried Nzoubou, Alistair O’Connell, Oscar Pace, Mohini Parameswaran, Ramon Pardo, Taniya Parikh, Panos Partheniou, Dario Pellegrinetti, José-Louis Pellon-Bailon, Michael Perryman, Christian Philippe, Alex Popescu, Alfonso Rivero, Andrew Robson, Gerd Rössling, Martina Rossmann, Markus Rückert, Jamie Salt, Giovanni Santin, Rui Santos, Stefano Scaglioni, Melanie Schabe, Dominic Schäfer, Micha Schmidt, Rudolf Schmidt, Jean Schütz, Klaus-Jürgen Schulz, Julia Schwartz, Andreas Scior, Jörg Seifert, Gunther Sessler, Felicity Sheasby, Heike Sillack, Swamy Siram, Claudio Sollazzo, Steven Straw, Mark Thompson, Raffaele Tosellini, Irren Tsu-Silva, Livio Tucci, Aileen Urwin, Jean-Baptiste Valet, Helma van de Kamp-Glasbergen, Martin Vannier, Kees van ’t Klooster, Enrico Vassallo, David Verrier, Sam Verstaen, Rüdiger Vetter, José Villalvilla, Raffaele Vitulli, Mildred Vögele, Sergio Volonté, Catherine Watson, Karsten Weber, Gavin Williams, Alistair Winton, Michael Witting, Peter Wright, Karlie Yeung, Igor Zayer, and from CERN:

Vincenzo Innocente. We thank the referee, Joss Bland-Hawthorn, for constructive feedback that helped to clarify several items in the text.
Paolo Haffoud1,a), François Poulet1, Mathieu Vincendon1, Gianrico Filacchione2, Alessandra Barbis3, Pierre Guiot1, Benoit Lecomte1, Yves Langevin1, Giuseppe Piccioni2, Cydalise Dumesnil1, Sébastien Rodriguez4, John Carter1, Stefani Stefania2, Leonardo Tommasi3, Federico Tosi2, Cédric Pilorget1 1Institut d’Astrophysique Spatial, CNRS-Université Paris-Saclay, Orsay, 91400, France 2Istituto di Astrofisica e Planetologia Spaziali, Istituto Nazionale di Astrofisica, Rome, 00133, Italy 3Leonardo Company, via delle Officine Galileo, 1, Campi Bisenzio, 50013, Italy 4Institut de Physique du Globe de Paris, CNRS-Université Paris-Cité, Paris, 75005, France a) The author to whom correspondence may be addressed: paolo.haffoud@universite-paris-saclay.fr
The Moons And Jupiter Imaging Spectrometer (MAJIS) is the visible and near-infrared imaging spectrometer onboard ESA’s Jupiter Icy Moons Explorer (JUICE) mission. Before its integration into the spacecraft, the instrument undergoes an extensive ground calibration to establish its baseline performances. This process prepares the imaging spectrometer for flight operations by characterizing the behavior of the instrument under various operative conditions and uncovering instrumental distortions that may depend on instrumental commands. Two steps of the on-ground calibration campaigns were held at the instrument level to produce the data. Additional in-flight measurements have recently been obtained after launch during the Near-Earth Commissioning Phase. In this article, we present the analyses of these datasets, focusing on the characterization of the spectral performances. First, we describe and analyze the spectral calibration datasets obtained using both monochromatic sources and polychromatic sources coupled with solid and gas samples. Then, we derive the spectral sampling and the spectral response function over the entire field of view. These spectral characteristics are quantified for various operational parameters of MAJIS, such as temperature and spectral binning. The derived on-ground performances are then compared with in-flight measurements obtained after launch and presented in the framework of the MAJIS performance requirements.
INTRODUCTION

The Jupiter Icy Moons Explorer mission (JUICE) was launched in April 2023 towards the Jovian system, which will be reached in July 2031. JUICE will first tour the Jovian system for 3.5 years, during which 35 flybys of the 3 Galilean Moons (Callisto, Ganymede, and Europa) will be performed before entering orbit around Ganymede at the end of 2034 for about nine months1,2,3.
The Moons And Jupiter Imaging Spectrometer (MAJIS) will acquire hyperspectral data cubes of Jupiter's atmosphere and the surfaces and exospheres of the three icy moons to determine their composition and identify various chemical species4. Of particular interest are the study of Ganymede’s ice-shell and exosphere, the investigation of the chemistry of surface material and active processes on Europa, and the monitoring of the Jupiter atmosphere with unprecedented temporal coverage and spatial and spectral resolutions in the near-infrared wavelength range.

The MAJIS science goals and science operations and whole instrument design have previously been described in details4. We present below the necessary elements which are relevant for spectral calibration.
Two calibration setups were used to establish the performances of MAJIS, including the spectral ones. The LDO setup aimed to provide a limited characterization of the performances of MAJIS OH prior to shipping for full calibration. The IAS setup was designed to calibrate the instrument thoroughly and validate that all performance requirements were met. The key features differentiating both calibration setups are summarized in Table II.
Table II. Main specific features of the calibration setups

|  | Source(s) | MAJIS OH temperatures explored (K) | FOV exploration | Spectral exploration | Controlled environment | Illuminating beam size |
| --- | --- | --- | --- | --- | --- | --- |
| LDO | Monochromator (QTH / IR emitter) | 110, 130, 150 | Small | Small | OH inside TVC + Non-flight acquisition chain + Source mounted on a hexapod outside | Limited to entrance pupil |
| IAS | OP1: Monochromator | 126, 137 | Small | Small | OH mounted on a hexapod inside TVC | Covers the whole |

|  | (QTH, blackbody) |  |  |  | + Flight acquisition chain + Calibration setup optical paths in a flushed (N ) 2 environment | entrance baffle |
| --- | --- | --- | --- | --- | --- | --- |
|  | OP3: QTH + integrating sphere (extended source with gas) |  | Wide | Wide |  |  |
|  | OP5: QTH (Reflected light on solid sample) |  | Wide | Wide |  |  |

For the spectral calibration, the LDO setup dataset was mainly used to provide additional monochromatic scans at wavelengths that were not explored during the full calibration. A few LDO scans covered a spectral range also encompassed by the IAS dataset. In addition to monochromatic scans, the IAS dataset provided atmospheric signatures spectra, solid samples spectra and ICU measurement spectra. The key elements of the calibration strategy were to use the monochromatic scans to calibrate the spectral response as well as to give some data points for the absolute calibration. The spectra acquired with nonmonochromatic sources were used to derive several absolute calibration (and smile) data points based on the matching of signature spectral positions between MAJIS and reference values. These high-accuracy data points complimented parts of the spectral range not covered by the monochromatic scans. This strategy summary is illustrated in Table III.
This setup is described in more detail in a companion paper10. The first stage to collect data used here to derive the spectral calibration occurred at LDO in Italy12. The MAJIS OH was tested inside a Thermal Vacuum Chamber (TVC) at three OH operative temperatures (cold 110 K, nominal 130 K, and hot 150 K), illuminated by the Optical Ground Support Equipment (OGSE) input beam. The OH was fixed, and the optical beam was moved within the FOV by means of a hexapod. The input beam was adapted to cover only the entrance pupil (i.e., it did not cover the whole entrance baffle). The tests were driven by a master computer using automatic test procedure commands to control the OGSE devices and the MAJIS acquisitions during the measurements. The OGSE configuration used specifically for the spectral characterization includes an off-axis parabolic mirror to collimate the test beam, a source module including a monochromator with two sources (Quartz Tungsten Halogen lamp and IR emitter from Newport/Oriel) covering the spectral range between 750 and 5300 nm, target translation stages to scan the test slit along and across the OH slit, and an OGSE hexapod to orientate the collimated beam at various FOV values.
Specifically, one or three positions inside the FOV were explored according to the thermal configurations of the OH.
Table IV, Table V, and Table VI indicate that various thermal steady-state configurations were tested during the LDO and IAS calibration. At the LDO premise, the cryogenic temperatures were achieved through a dedicated thermal control system. Three thermal cases were considered: OH structure stabilized at 110, 130, and 150 K, corresponding to the lower, nominal, and upper operative cases, respectively.

A more restricted data set was obtained for the lower and upper operative cases since these cases are very unlikely during the science operation. At IAS, the thermal control of the IR detector in the TVC was managed with a cryocooler, and liquid nitrogen was used to cool the OH and the VISNIR FPA. The thermal exploration has been selected to cover two operative configurations expected to be representative of most measurements during the science operations at Jupiter: 126 K (nominal thermal case), where most of the sequences were completed, and 137 K (hot case), where part of the sequences was performed again.
As shown in Table I, the requirements are specified for nominal pixels (36-µm pitch). Still, the acquisitions (and consequently the analysis) are performed using the specific readout procedure No Process that allows reading the full frames (1024 × 1024) at the level of the physical pixels (18-µm pitch) in both dimensions (see section I.A). This mode has the advantage of keeping the full detector resolution, which was required to evaluate MAJIS performance in the oversampling (i.e., un-binned) optional mode. Binning can be easily implemented after data acquisition with the same algorithm used by the onboard software. Therefore, unless specified otherwise, the analyses and the results of the spectral calibration are given in terms of physical spectels corresponding to the oversampling mode, and the effects of binning are discussed in a dedicated paragraph (Section IV.C).
Several measurements were acquired using the capability of reading out only part of the 1024 rows along the spatial direction thanks to the MAJIS capability of spatial windowing. This mode results in a shorter repetition time, and it is useful when the source covers only a part of the FOV or when a shorter repetition time is required (for instance, to avoid saturation).
The use of this mode has no impact on the derivation of the spectral performances.
The summary of the data sets acquired at LDO and IAS premises is presented in Table III. Table IV, Table V, and Table VI provide additional details on the measurements (wavelengths, position on the FOV, OH temperature…) for each source.
Because of the planning constraints, the parameters were adjusted during the calibration campaigns.
Table III. Overview of data sets used for the data analysis. The monochromatic data acquired at LDO were processed similarly and used for the same purposes as the IAS OP1 data. This data will also be referred to as OP1 in the article.
Internal calibration unit
Monochromatic light (see Table IV and Table V)
Gas sample in
Solid sample in
transmission
reflectance
(see Table VI)
(see Table VI)
(see Table VI)

| Spectral response |  | LDO / IAS datasets |  |  |
| --- | --- | --- | --- | --- |
| Absolute spectral calibration | IAS dataset | LDO / IAS datasets | IAS dataset | IAS dataset |
| Optical source reference number | OP0 | OP1 | OP3 | OP5 |
calibration and LDO characterization. The spectral and spatial coverage is also illustrated in Fig. 4. As seen on this figure, the number of spectral intervals and positions inside the FOV that were explored was restricted because of the planning constraint. Achieving a good signal-to-noise for wavelengths beyond 3.6 µm was also very challenging due to the thermal background and the low level of the source flux. The mitigation of these limitations was to complement monochromatic measurements with polychromatic measurements over solid samples and gas, as described in the following section.

Table IV. Monochromator scan measurements series from IAS OP1

| Channel | Wavelength Range (nm) | OH / VISNIR FPA (and IR FPA) Temperature (K) | MAJIS Slit Position | Scan Step (nm) | Monochromator spectral bandwidth (FWHM) (nm) |
| --- | --- | --- | --- | --- | --- |
| VISNIR | 550-585 | 126 (88) | 200 | 0.7 | 0.7 |
|  | 900-935 | 126 (88), 137 (97) | 20, 200, 380 | 0.7 | 0.7 |
|  | 1400-1435 | 126 (88), 137 (97) | 20, 200, 380 | 0.7 | 0.7 |
|  | 1900-1935 | 126 (88), 137 (97) | 20, 200, 380 | 0.7 | 0.7 |
| IR | 2600-2650 | 126 (88), 137 (97) | 20, 200, 380 | 1 | 1.3 |

Table V. Monochromator scan measurements series from LDO OP1

| Channel | Wavelength Range (nm) | OH / VISNIR FPA (and IR FPA) Temperature (K) | MAJIS Slit Position | Scan Step (nm) | Monochromator spectral bandwidth (FWHM) (nm) |
| --- | --- | --- | --- | --- | --- |
| VISNIR | 687.5-712.5 | 130 (91) | 200 | 0.5 | 0.5 |
|  | 1387.5-1412.5 | 130 (91) | 20, 200, 380 | 0.5 | 0.55 |
|  | 2087.5-2112.5 | 130 (91) | 20, 200, 380 | 0.5 | 0.55 |
| IR | 2275-2325 | 130 (91) | 20, 200, 380 | 1 | 3.15 |

[Figure: Fig. 4. Schematic view of the coverage obtained from the monochromatic scan dataset acquired at IAS and LDO. The spectral positions of the scans are approximate.]
A detailed description of the processing of the MAJIS data will be given in a companion paper11. All data are saved as .fits files. Each file is a data cube representing a series of N images (2D acquisitions with the same number of rows and columns).
The number of images and the size of the images varies from file to file (typically 8 or 16 full-window no process images). In case an acquisition is performed with both channels switched on, each channel is stored in a different .fits file. All files have been processed using the same high-level processing methodology for the calibration analysis.
First, the 3D cube of N images is turned into a 2D median image. This increases the SNR and removes any outliers out of the data set. The proper linearity file, instrument transfer function (ITF) file, and gain are selected based on the acquisition mode and channel11. More specifically, the linearity file is applied to correct the H1RG detector’s non-linearities19,20. Then, the gain and ITF file are applied to transform the measurements reported by each pixel in Analog-to-Digital-Unit (ADU) into a radiance expressed in (W/m2/sr/µm).