JuanCastillo29/space-mission-intelligence-data huggingface dataset:
3.2 µm (center) and
the amount of water ice predicted by the Mars PCM (right). The light blue areas represent the
altitudes where water ice clouds have been identified in the ACS-MIR data.

of the atmospheric aerosols (at least near the morning and evening terminators; i.e., at local times ∼06:00 and 18:00).
• The decrease of the ice crystals size as the altitude increases, previously noticed in the GDS study (Stcherbinine et al., 2020) and by other TGO studies (Luginin et al., 2020; Liuzzi et al., 2020), remains the observed behavior in this extended 2-MY dataset.
• The comparison between the perihelion periods of MY 34 and MY 35 allows us to contrast conditions during the MY 34 GDS with those of the same season in the absence of such a dramatic atmospheric event. It can be seen that the global altitude of the water ice clouds has increased by 10 km during the GDS compared to a more typical year, and this altitude increase can been 20 km for the smallest ice crystals (reff ≤0.1 µm).
• No large-grained clouds (reff ≥1.5 µm) have been observed at high altitudes (60 to 65 km range) during the non-GDS year, while they were detected during the MY 34 GDS. However, such clouds have been detected at 55 km (a relatively high altitude for such large ice crystals) close to the equator without being associated with a particular dust storm event.
• We also note that this elevation of the water ice clouds during a dust storm corresponds to a decrease of the typical extinction values associated with a specific ice crystal sizes.
• We observe variations of 20 to 40 km between the average altitude of the water ice clouds during summer and winter for both hemispheres, with water ice clouds often detected up to 80 km during summer seasons. The variations are stronger in the high latitudes compared to those in the equatorial regions.
• In addition to the seasonal variations, clouds are also detected typically 20 to 40 km higher close to the equator than in the polar regions.
• Finally, comparison between the ACS-MIR retrievals and the Mars PCM shows that the water ice clouds are usually predicted at lower altitudes in the model, up to 10 km lower in the polar regions and 20 km in the midlatitudes.
In conclusion, we investigated the impact of season and latitude on the altitude and the size of the water ice crystals. These results have been compared to predictions from climate models, showing a good agreement overall, while revealing some regions of discrepancies associated with the cloud altitudes. The quasi-systematic detection of water ice clouds in our observations highlights the importance of considering these clouds in studies of the current Martian atmosphere and climate.
ExoMars is a space mission of ESA and Roscosmos. The ACS experiment is led by IKI Space Research Institute in Moscow. The project acknowledges funding by Roscosmos and CNES. Science operations of ACS are funded by Roscosmos and ESA. Science support in IKI is funded by Federal agency of science organization (FANO). MJW acknowledges support from Jet Propulsion Laboratory subcontract contract 1551112.
C. Pagani1,⋆, N. C. Hambly2, M. Davidson2, N. Rowell2, C. Crowley4, R. Collins2, F. van Leeuwen3, G. M. Seabroke5, A. Holland6, M. A. Barstow1, and D. W. Evans3 1 School of Physics and Astronomy, University of Leicester, University Road, Leicester LE1 7RH, United Kingdom 2 Institute for Astronomy, School of Physics and Astronomy, University of Edinburgh, Royal Observatory, Blackford Hill, Edinburgh, EH9 3HJ, United Kingdom 3 Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA, United Kingdom 4 HE Space Operations BV for ESA/ESAC, Camino Bajo del Castillo s/n, 28691 Villanueva de la Cañada, Spain 5 Mullard Space Science Laboratory, University College London, Holmbury St Mary, Dorking, Surrey, RH5 6NT, United Kingdom 6 The Open University, School of Physical Sciences, Centre for Electronic Imaging, Milton Keynes, United Kingdom
During the course of its mission, ESA’s Gaia spacecraft has generated a map of the stars of the Galaxy of exquisite detail. While in its L2 orbit, the satellite has been exposed to high energy cosmic rays and solar particles, that caused permanent damage to its CCDs. The main effect of radiation damage on Gaia data is the distortion of its images and spectra, caused by the CCDs charge transfer inefficiency (CTI) during the readout process, that, if not taken into account, can result in inaccurate measurements of a star’s location and flux. In this work, the impact of CTI in the serial readout direction, larger than in the parallel due to the presence of CCDs manufacturing defects, has been analysed and modelled. A pixel-based, physically motivated CTI model, CtiPixel , has been developed to characterise the damage in Gaia CCDs. The model has been calibrated using dedicated serial CTI diagnostic data, taken every 3-4 months over the course of the mission. The model is shown to be a good representation of the observed signatures of CTI in the calibration datasets, and its parameters reveal significant insights into the nature of the CCD defects generated by space irradiation. The evolution of the damage in the serial direction shows a general small linear increase over time, with sudden step changes after strong solar flares and coronal mass ejections directed towards Earth. The serial CTI showed a further step increase as a consequence of the engineering CCD annealing experiment carried out after the completion of Gaia science observations.
Key words. instrumentation: detectors – methods: data analysis – space vehicles: instruments
of damage during the mission resulted in a parallel CTI that remained a factor of ∼8 below predictions. The serial CTI (sCTI) in the Across Scan (AC) direction has slowly increased over the years and in absolute terms is higher than in the parallel direction. The major cause of sCTI is the manufacturing traps. These, already present at launch, affect the serial
⋆Corresponding author: C. Pagani
e-mail: cp232@le.ac.uk
Article number, page 1 transfers to a larger degree than in the parallel direction because of the faster serial readout timing.

The focus of this paper is the analysis of the serial CTI effects on a set of Gaia engineering data, that are taken periodically during the course of the mission, and the mapping of the damage evolution over time. In Section 2 we describe the telescope focal plane and its detectors, the L2 radiation environment and the type of damage it causes on the CCDs. We will review the preparatory work carried out before the satellite launch by the Gaia irradiation study team, aimed at predicting and modelling the effects of radiation in Gaia data. That analysis has informed the development of the CtiPixel CTI model and software package described in this paper. The model is based on physical properties of the defects that cause CTI and their effects on charge during the detectors readout process. CtiPixel has been calibrated using a collection of dedicated serial CTI engineering activities, carried out periodically during the course of the mission, that involved the controlled injection of charge at the top of the CCDs. These engineering tasks and calibration datasets are presented in Section 3, while the CtiPixel model is described in Section 4. In Section 5 the method used to process the serial calibration activities and to fit them with the CtiPixel model will be shown. The results of the fits are presented in Section 6. Finally, the physical interpretation of the fitted trap parameters, the limitations of the model and an attempt to use CtiPixel to treat the signatures of serial CTI in Gaia science observations are discussed in Section 7. Appendix A presents the main effects on serial CTI following the CCDs annealing procedure, one of Gaia End-of-Life activities.
in detail in Section 3. The analysis of the charge trails in Gaia CCDs has revealed a number of significant results that were first reported by Crowley et al. (2016b) using data from the initial two years of the mission. The measured evolution of the damage is characterised by a steady, gradual increase since the beginning of the mission, that is attributed to the continuous irradiation from the Galactic Cosmic Rays (CR) flux (mostly protons and alpha particles). A second component of the damage evolution is seen as sudden steps at specific times in correspondence with significant solar activity events. The strongest of these occurred on the]

[Figure: Fig. 1: Evolution of the measured charge trails intensity in the parallel direction averaged over all Astrometric Field devices, covering the entire course of the Gaia mission. The trails are an empirical measure of the amount of radiation accumulated in orbit. The gradual increase in damage is caused by Cosmic Rays, while strong solar flares directed towards Earth are responsible for the step changes in the damage evolution. Time intervals affected by temperature disturbances of the focal plan, due for example to decontamination activities, or instances of incomplete telemetry have been removed from the plot for clarity.]

10th of September 2017 (this correspond to an On Board Mission Time of 5651 revolutions), that saw a strong class X8.2 solar flare emitted in the Earth’s direction (Seaton & Darnel 2018). The high energy particles from that flare caused the generation of new defects in the detectors that resulted in a sudden increase of approximately 20% in the level of the charge trails. A wealth of additional information can be derived from the analysis of the trails. For example, it can be seen that the gradual increase in the damage follows the 11-year modulation of the solar cycle, with a steeper accumulation rate during solar minimum and a shallower increase at solar maximum, in correspondence with the higher and lower levels of the measured Galactic CR flux. Another feature of the observed damage is its non-uniformity over the focal plane, due to the different amount of shielding from the irradiation and the mitigation effect of the local diffuse background signal. The analysis of the charge trails can reveal detailed information on the physical nature of the traps, such as the characteristic emission timescale of a specific defect type. This will be described and discussed in the next Sections 3 and 4.
sample of the trails.

Investigating this issue, evidence of charge blooming from the charge injection line into the neighbouring pixel was found in the case of red devices (RP and RVS CCDs), especially at the highest commanded injection voltage levels.

To mitigate this effect, the flux values of the first sample in the case of the red devices has been corrected using the signal measured in the second pre-scan sample, i.e. that nearest the image section at the opposite end of the serial register.

This of course assumes that any charge bleeds at the same rate forwards and backwards and affects only the pixels adjacent to the image section.

In the case of the blue variant detectors (AF and BP CCDs) no evidence of any significant level of charge bleeding was found, and no correction of the signal in the first trails sample has been attempted.
6.1. Initial calibrations Visual inspection of the post-scan charge release trails clearly indicates the need for multi-component decay curve modelling, cf. deferred charge models as sum–of–exponentials in Hopkinson et al. (2005) and references therein, owing to the presence of trap species of different characteristic charge release timescales. Initially we chose a model with two trap species as a baseline to test the CtiPixel implementation and associated calibration procedure for all AF and XP devices. Each individual device and epoch was independently calibrated with a model having 7 free parameters: a single charge volume confinement parameter β common between the trap species, plus a trap density ρ, trap emission timescale τe (at an assumed constant nominal operating temperature in the focal plane) and trap capture crosssection parameter α for each of the two trap species. The results of these initial calibrations were rather unsatisfactory, with poor goodness–of–fits and clear evidence of systematic errors increasing with mission time. This is summarised in Figure 3 which shows the robustly estimated unit–weight residuals of the entire calibrating dataset as a function of mission time, combined for all blue (AF and BP) and red (RP, but not including RVS) devices. The typical residual is ≈1.5 in both cases with a clear evolution as a function of time.
6.2. Final calibrations The poor quality of the fits of the initial calibrations was addressed with a series of refinements to the CtiPixel model and the fit configuration. Specifically, two major changes were introduced: we increased the number of trap species to three, and implemented a more detailed treatment of the temperature of the CCDs, initially assumed constant over time and uniform over the field of view. The introduction of a third trap species is justified by the gradual degradation with time in the quality of the initial two trap species calibrations, that hints at the emergence of a third defect Article number, page 7 due to the exponential in Equation 5.

[Figure: Fig. 3: Time evolution in the Gaussian–scaled median absolute deviation in unit–weight residual, with time measured as On– Board Mission Time for initial 2–trap species calibrations fitting every device and epoch independently at an assumed constant focal plane operating temperature. The top panel is for all blue (AF and BP) devices, the bottom panel for RP devices.]

[Figure: Fig. 4: Gaia Focal Plane Array temperature variation over the course of the ten year mission at three different positions: (blue) row 1 SM; (red) row 4 AF5; (green) row 7 RVS. The large variations seen later in the timeline are caused by a series of end-oflife engineering tests.]
–board analogue–to–digital converters reading the thermistor signals.
– large temperature variations induced by the mission end-oflife engineering tests.
The available telemetry was used to create an FPA temperature interpolation map by device position and mission time with the simplifying assumption of reflection symmetry to create a 2d temperature model from the limited positional coverage afforded by the FPA thermistors. The incorrect assumption of a fixed temperature introduces significant scattering in the values derived for the fits of the parameter τe, the characteristic emission time of a trap species, Article number, page 8 very first pixels of the release curve following the injected signal block.

[Figure: Fig. 5: Calibration data (orange points with measured uncertainties), final models (blue lines) and residuals for 3 trap species CtiPixel models for two device/epochs: a typical calibration for row 5, strip 8 (=AF5) near the start of the mission at OBMT 2536 revolutions (upper panels); and a poor example for row 3, strip 12 (=AF9) towards the end of the mission. Calibration data consists of seven individual postscan trails, measured over a large range in injected signal level, and having a length of 20 pixels each; they are shown concatenated in these plots for convenience.]

This tendency of the CtiPixel model to follow less effectively the initial shape of the decay curves appears across all levels of the injected charge block so cannot be plausibly explained as poorly mitigated charge diffusion from large charge packets in the last pixel before the postscan samples. Otherwise, the only systematic effect clearly present is the residual bias non–uniformity discontinuity between samples 11 and 12 (blue), and 8 and 9 (red), which results from the presence of the readout ‘freeze’ during the serial scan (Section 5).
dence that the trap parameters derived in the final calibrations have some useful level of physical reality despite the likely limitations of the fitting procedure. These include the arbitrary initialisation of the trap parameters in the non–linear least–squares optimisation, the possibility of hitting local minima in that process, and the high level of correlation between some parameters as expected from inspection of the model as formulated in Section 4. The goodness–of–fit distribution for the 3 trap species fits (Figure 6) indicates that the final model is a close representation of the data, although the median value is ≈1.5 rather than unity as might be expected under the assumption of normally– distributed uncertainties. In order to examine potential systematic effects in more detail, in Figure 10 we show scatter plots of the studentised residuals of the calibrations as a function of sample index in the postscan trails for the two CCD variants. These show evidence that the model is a little poorer in the
would be problematic in the serial CTI context, where the wings in the Across Scan direction are most important. A proper application of the serial CTI model would also need to account for other CCD response effects, such as the column response non uniformity, when realising the predicted scene. The readout sequencing of science data would be critical in the application of the CTI model, but this aspect might not have been well-constrained by the calibrations using only engineering sequences.
Article number, page 13 was concluded that the integration of the CtiPixel task into the Gaia’s Image Parameter Determination software (IPD)

[Figure: Fig. 14: Trap parameters for the final 3–trap species calibrations plotted as histograms (charge volume confinement exponent β; top row) and scattergrams (trap species energy level Et, middle row; trap capture cross-section parameter α, bottom row) for the full calibration datasets.]

for the treatment of the CTI distortion effects would not be feasible, as it would exceed the accessible machine processing time, considering the sheer volume of Gaia observations and the cyclic
A critical aspect already mentioned earlier that was experienced when attempting the processing of the test science data sets with CtiPixel is the high demand of computation resources needed when modelling the image readout at the pixel level. After these initial, and not fully successful test cases, it
Article number, page 14
Gaia context, the AC location bias caused by serial CTI are not
as critical as the CTI bias in the parallel direction, since the AC
source locations are not used in the AGIS astrometric solution
(Lindegren et al. 2016), and are only indirectly employed in the
attitude and calibration solutions. The efforts with the character-
isation and the analysis of CTI were therefore ultimately redi-
rected to a project to report passively on the observed biases,
without attempting any processing of science data.
A. Moraab, M. Biermannc, A. Bombrunad, J. Boyadiane, F. Chassate, P. Corberande, M. Davidsonf, D. Doyleg, D. Escolarg, W.L.M. Gielesenh, T. Guilpainei, J. Hernandeza, V. Kirschnerg, S.A. Klionerj, C. Koecke, B. Laineg, L. Lindegrenk, E. Serpelllm, P. Tatrye, and P. Thorale aESA-ESAC Gaia Science Operations Centre, Camino Bajo del Castillo s/n, Urb. Villafranca del Castillo, 28692 Villanueva de la Ca˜nada, Madrid, Spain; bAurora Technology, Crown Business Centre, Heereweg 345, 2161 CA Lisse, The Netherlands; cAstronomisches Rechen-Institut, Moenchhofstr. 12-14, 69120 Heidelberg, Germany; dHE Space Operations GmbH, Flughafenallee 24, 28199 Bremen, Germany; eAirbus Defence and Space, 31 rue des Cosmonautes, Z.I. du Palays, 31402 Toulouse Cedex 4, France; fRoyal Observatory, Blackford Hill View, Edinburgh EH9 3HJ, United Kingdom; gESA-ESTEC Directorate of Technical and Quality Management, Keplerlaan 1, 2201 AZ Noordwijk, The Netherlands; hTNO Science and Industry, Stieltjesweg 1, 2600 AD Delft, The Netherlands; iAltran Technologies, 17 Avenue Didier Daurat, 31700 Blagnac, France; jLohrmann Observatory, Dresden Technical University, Mommsenstr. 13, 01062 Dresden, Germany; kLund Observatory, Department of Astronomy and Theoretical Physics, Lund University, Box 43, 22100, Lund, Sweden; lESA-ESOC Gaia Flight Control Team, Robert-Bosch-Strasse 5, 64293 Darmstadt, Germany; mTelespazio VEGA Deutschland GmbH, Europaplatz 5, 64293 Darmstadt, Germany;
The Gaia all-sky astrometric survey is challenged by several issues affecting the spacecraft stability. Amongst them, we find the focus evolution, straylight and basic angle variations Contrary to pre-launch expectations, the image quality is continuously evolving, during commissioning and the nominal mission. Payload decontaminations and wavefront sensor assisted refocuses have been carried out to recover optimum performance. Straylight and basic angle variations several orders of magnitude greater than foreseen were found and studied during commissioning by the Gaia scientists (payload experts). Building on their investigations, an ESA-Airbus DS working group was established during the early nominal mission and worked on a detailed root cause analysis. In parallel, Gaia scientists have also continued analysing the data, most notably comparing the BAM signal to global astrometric solutions, with remarkable agreement.
In this contribution, a status review of these issues will be provided, with emphasis on the mitigation schemes and the lessons learned for future space missions where extreme stability is a key requirement.
Keywords: Gaia, astrometry, wavefront sensor, focus, stability, straylight, interferometry, basic angle Further author information: (Send correspondence to A.M.) A.M.: E-mail: alcione.mora@esa.int, Telephone: +34 91 813 1480

[Figure: Figure 1. Gaia architecture layout. Left: The thermal tent, service module and deployable sun-shield are apparent. The Sun would be downwards, keeping 45◦aspect angle with respect to the spin axis. Right, the payload is located within the thermal tent and atop the service module, isolated from it via three double bipods. Images: Airbus DS.]
The ESA Gaia mission is creating the most comprehensive 3D census of the Galaxy ever envisaged, comprising more than a billion parallaxes and proper motions, complemented with exquisite visible spectrophotometry and millions of radial velocities, providing astrophysical parameters, non-single star solutions, solar system objects, variability, etc.1 See also Prusti et al. (2016 A&A, in press).
The most important quality behind the Gaia overall design is its extreme stability. In this work, some issues related to that stability are reviewed, both to explain the Gaia performance and as a source of information for future missions.
The general architecture and design choices needed to obtain very high stability from purely passive means are described in Sect. 2. The evolution of the telescopes focus is presented in Sect. 3. The analysis of the additional straylight is given in Sect. 4. The basic angle variations are discussed in detail in Sect. 5. Finally Sect. 6 provides the conclusions.