Modern space missions, ground telescopes and modeling facilities are producing huge amount of data. A new era of data distribution and access procedures is now starting with interoperable infrastructures and big data technologies. Long term archives exist for telescopic and space-borne observations but high-level functions need to be setup on top of theses repositories to make Solar and Planetary Science data more accessible and to favor interoperability. Results of simulations and reference laboratory data also need to be integrated to support and interpret the observations.
The Virtual Observatory (VO) standards developed in Astronomy may be adapted in the field of Planetary Science to develop interoperability, including automated workflows to process related data from different sources. Other communities have developed their own standards (GIS for surfaces, SPASE for space plasma, PDS4 for planetary mission archives…) and an effort to make them interoperable is starting.
Planetary Science Informatics and Data Analytics (PSIDA) are also offering new ways to exploit the science out of planetary data through modern techniques such as: data exploitation and collaboration platforms, visualisation and analysis applications, artificial intelligence and machine learning, data fusion and integration supported by new big data architecture and management infrastructure, potentially being hosted by cloud and scalable computing.
We call for contributions presenting progresses in the fields of Solar and Planetary science databases, tools and data analytics. We encourage contributors to focus on science use cases and on international standard implementation, such as those proposed by the IVOA (International Virtual Observatory Alliance), the OGC (Open Geospatial Consortium), the IPDA (International Planetary Data Alliance) or the IHDEA (International Heliophysics Data Environment Alliance), as well as applications linked to the EOSC (European Open Science Cloud) infrastructure.
André M. Silva, Sérgio G. Sousa, Nuno Santos, Olivier D. S. Demangeon, and Mafalda X. Matos
High precision time-series photometry from space is being used for a number of scientific cases. In this context, the recently launched CHaracterizing ExOPlanet Satellite (CHEOPS) (ESA) mission promises to bring 20 ppm precision over an exposure time of 6 h, when targeting nearby bright stars, having in mind the detailed characterization of exoplanetary systems through transit measurements. However, the official CHEOPS (ESA) mission pipeline only provides photometry for the main target (the central star in the field). In order to explore the potential of CHEOPS photometry for all stars in the field, we present archi, an additional open-source pipeline module to analyse the background stars present in the image. As archi uses the official data reduction pipeline data as input, it is not meant to be used as an independent tool to process raw CHEOPS data but, instead, to be used as an add-on to the official pipeline. We test archi using CHEOPS simulated images, and show that photometry of background stars in CHEOPS images is only slightly degraded (by a factor of 2–3) with respect to the main target. This opens a potential for the use of CHEOPS to produce photometric time-series of several close-by targets at once, as well as to use different stars in the image to calibrate systematic errors. We also show one clear scientific application where the study of the companion light curve can be important for the understanding of the contamination on the main target.
How to cite:
M. Silva, A., G. Sousa, S., Santos, N., D. S. Demangeon, O., and X. Matos, M.: archi: pipeline for light curve extraction of CHEOPS background stars , Europlanet Science Congress 2020, online, 21 Sep–9 Oct 2020, EPSC2020-214, https://doi.org/10.5194/epsc2020-214, 2020.
Santa Martínez, Mark S. Bentley, Thomas Cornet, M. Angeles Cuevas, Nicolas Fajersztejn, Marco Freschi, Daniel Galán, Julio Gallegos, Alan J. Macfarlane, Francisco Vallejo, Iñaki Ortiz de Landaluce, and Antonio Villacorta
The joint ESA/JAXA BepiColombo mission to Mercury comprises two orbiters and a solar-electric transfer module, currently in a stack configuration. The BepiColombo spacecraft stack flew by the Earth on 10th April 2020 and will perform eight flybys more on its way to Mercury: two at Venus (in October 2020 and August 2021), and 6 at Mercury, starting from October 2021, before orbit insertion in December 2025. The two spacecraft host many instruments designed to study Mercury's interior structure, surface properties, close space environment, and their interplay.
Processing the telemetry received from the spacecraft on ground into science products for the archive and providing quick access and visibility of the science results to the science team are the responsibility of the ESA Science Ground Segment (SGS). Raw and calibrated science products are generated by the data processing pipelines within hours after reception of the telemetry and can be visualised through a Quick-Look Analysis (QLA) web-application. Science products are also made available in the Planetary Science Archive (PSA) to the instrument teams for detailed analysis and further processing.
This contribution will describe how the SGS data processing and quick-look analysis infrastructure, in operation since launch, has been enhanced to support the monitoring, sharing and analysis of all the scientific measurements that will be acquired during the flybys. This infrastructure includes instrument-specific calibration and reduction processing pipelines developed by the PI teams. Reduction pipelines are hosted either by the SGS or by the corresponding PI team based in a prime-redundant configuration and with a replica always available at the SGS.
The Quick-Look Analysis web-application is intended to provide rapid feedback on the content and quality of the science archive products, to support the diagnosis of pipeline or instrument issues, to facilitate the monitoring of deviations of the executed observations from the planned observations and, when possible, to feedback the result of the analysis into the different cycles of the science planning. This strategy is science-driven and offers the possibility of exploring and sharing information and plots of the science data collected by both the MPO and MMO instruments among the BepiColombo science working team members.
In addition to the Quick-Look Analysis web-application, all the spacecraft and payload housekeeping parameters are made available to the science team via a web-based user interface (WebMUST). This interface allows monitoring of the spacecraft and instrument operations in near-real time, as the telemetry arrives on ground. Pre-configured dashboards can be designed for specific needs such as monitoring the data volumes in the packet stores of the on-board mass memory (SSMM) along with the switch on of the various instruments of the BepiColombo payload.
Despite the limited science capabilities of the spacecraft in cruise configuration (as the boresight of many instruments is partially or fully blocked by the Mercury Transfer Module), several instruments will perform calibration and scientific measurements during flybys. Some examples of how the science observations can be monitored with the tools developed by the SGS will be presented. The main goal of these tools is to make available to the science team all the information relevant for the post-analysis of the executed observations and to capture and preserve this knowledge for the future users of the BepiColombo data in the scientific community.
How to cite:
Martínez, S., Bentley, M. S., Cornet, T., Cuevas, M. A., Fajersztejn, N., Freschi, M., Galán, D., Gallegos, J., Macfarlane, A. J., Vallejo, F., Ortiz de Landaluce, I., and Villacorta, A.: BepiColombo Science Data Processing & Quick-Look Analysis, Europlanet Science Congress 2020, online, 21 Sep–9 Oct 2020, EPSC2020-661, https://doi.org/10.5194/epsc2020-661, 2020.
Edoardo Rognini, Angelo Zinzi, Davide Grassi, Alberto Adriani, Alessandro Mura, and Maria Teresa Capria and the JIRAM team
MATISSE (Multi-purpose Advanced Tool for the Solar System Exploration)  is a tool that allows the visualization of observations from space missions and datasets derived from these observations on a three-dimensional model of the selected target body. The second version of the tool (named MATISSE 2.0 –https://tools.ssdc.asi.it/Matisse) will, among other things, include algorithms developed by partner research teams; in this work we focalize our attention on the MATISSE inclusion of two codes developed for atmospheric retrieval and thermophysical modeling. The retrieval code is used for the analysis of the spectra provided by the JIRAM instrument (Jovian Infrared Auroral Mapper ) onboard the NASA’s Juno mission, whose main purpose is the study of the upper regions of Jupiter’s atmosphere in the 2-5 μm wavelength range and pressure up to 5-7 bar. The spectra provided by the instrument are processed with the retrieval code that calculates, for each pixel of a hyperspectral image, the chemical and physical parameters in the corresponding points of the atmosphere . The code processes all pixels of a hyperspectral image, so parallelization is convenient in order to reduce the computation time; this is possible by using the Python language tools, which allow the execution of a code written in its own language (FORTRAN in this case) by providing the required parallelization. As a further optimization step, the code has been converted into a Docker image to make it portable and easy to run on heterogeneous architectures. The second code included in MATISSE is a thermophysical model that calculates the surface temperature of airless bodies as function of thermal conductivity [4,5] and other physical properties; the calculated temperature can be compared with the measured ones, if any, in order to retrieve the thermal properties of the soil, or can be used to compute other temperature-dependent quantities. At the present time this code is going to be used for Mercury and Ceres and is almost ready to be included in MATISSE 2.0.
 Zinzi, A., et al. (2016), Astronomy & Computing, 15, 16-28  Adriani, A., et al. (2017), Space Science Reviews, 213, 393-446  Grassi et al. (2010), Planetary and Space Science, 58, 1265-1278  Capria, M. T. et al (2014), Geophysical Research Letters, 41, 1438-1443  Rognini et al. (2019), Journal of Geophysical Research, https://doi.org/10.1029/2018JE005733
How to cite:
Rognini, E., Zinzi, A., Grassi, D., Adriani, A., Mura, A., and Capria, M. T. and the JIRAM team: Inclusion of scientific algorithms in MATISSE, Europlanet Science Congress 2020, online, 21 Sep–9 Oct 2020, EPSC2020-920, https://doi.org/10.5194/epsc2020-920, 2020.
Ruben Docasal, Isa Barbarisi, Javier Arenas, Silvia De Castro, Angel Montero, Jose Osinde, Francisco Raga, Jorge Ruano, Jaime Saiz, Bruno Merin, Sebastien Besse, Mark Bentley, Daniela Coia, Diego Fraga, Emmanuele Grotheer, David Heather, Tanya Lim, and Santa Martinez
Geographical information systems (GIS) are becoming increasingly used for planetary science. GIS are computerised systems for the storage, retrieval, manipulation, analysis, and display of geographically referenced data.
Some data stored in the Planetary Science Archive (PSA) have spatial metadata associated to them. To facilitate users in handling and visualising spatial data in GIS applications, the PSA should support interoperability with interfaces implementing the standards approved by the Open Geospatial Consortium (OGC).
These standards are followed in order to develop open interfaces and encoding that allow data to be exchanged with GIS Client Applications (e.g. OpenLayers, Cesium...). Access to this data for use in applications can be provided through OGC Web Service (OWS) implementations.
An existing open source server is GeoServer, an instance of which has been deployed for the PSA, that uses the OGC standards to allow the sharing, processing and editing of data and spatial data through the Web Map Service (WMS) and Web Feature Service (WFS) standards. On the back-end side, a PostgreSQL/PostGIS instance allows the spatial queries.
The final goal is to enhance the PSA (accessible through ) further as a portal which enables science exploitation of ESA's planetary missions datasets. This can be facilitated through the GIS framework, offering interfaces (both web GUI and scriptable APIs) that can be used more easily and scientifically by the community, and that will also enable the community to build added value services on top of the PSA.
Some of the current operational ESA planetary missions, such as Mars Express, ExoMars 2016, and BepiColombo, as well as other future missions such as ExoMars 2020, Juice, etc. will benefit of a GIS tool to visualize their targets (Mars, Mercury, Jupiter…) allowing spatial queries to retrieve geometrical information like features, footprints, rover path tracking, rover drill sites, etc.
Other external GIS tools like QGIS might be used to get the PSA spatial data from either the GeoServer or the database.
Figure 1: GIS architecture diagram for the PSA
The PSA provides different views to show the same planetary data. These views are integrated and synchronized to each other to visualize the information as the data type requires. All of them use the filter menu to search by a given criteria and offer similar features such as sorting, pagination, downloading and product detailed info. Once a query is executed on a view, the information is automatically loaded when changing views. The map view is integrated in the current PSA (see Figure 2) as the other views (Table, Image) giving other perspective of displaying results when it comes to search for spatial data.
Figure 2: Views Consistency in the PSA
GIS technology on the PSA will offer a common way to filter (by mission, instrument, target, dates, geometry…) and search for spatial data, even for legacy missions, thanks to the homogenization of the geometrical information with per-product spatial metadata computed in a consistent way via SPICE.
PSA will provide spatial data retrieval of both versions of the NASA Planetary Data Systems archival formats, PDS3 and PDS4, based on a criteria search, and, the possibility of selecting PDS3/PDS4 products from a particular region of interest (ROI) (see Figure 3).