PLATO Data Processing System (DPS): On-board data processing and its verification for Europe's next planet hunter

Kurzfassung

The PLAnetary Transits and Oscillations of stars (PLATO) mission will detect and characterize exoplanets by means of their transit signature in front of a very large sample of bright stars, and measure the seismic oscillations of the parent stars orbited by these planets in order to understand the properties of the exoplanetary systems. Summarized, the main scientific goals are: - Determine the bulk properties (mass, radius, mean density) of planets in a wide range of systems, including terrestrial planets in the habitable zone of solar-like stars. - Study how planets and planet systems evolve with age. - Study the typical architectures of planetary systems. - Analyse the correlation of planet properties and their frequencies with stellar parameters (e.g., stellar metallicity, stellar type). - Analyse the dependence of the frequency of terrestrial planets on the environment in which they formed. - Study the internal structure of stars and how it evolves with age. - Identify good targets for spectroscopic follow-up measurements to investigate planet atmospheres. PLATO will deliver an extensive legacy catalog featuring hundreds of well-characterized planets, thousands of stars with precise ages and masses, and hundreds of thousands of high-precision stellar light curves. The nominal scientific operations will involve a prolonged observation period of at least 4 years. The payload comprises 26 telescopes for star observation: 24 standard telescopes organized into four subsets of six telescopes each, and two fast telescopes designed for precise spacecraft pointing. Given the payload's capability to generate substantial data volumes exceeding downlink capacities, significant on-board data processing is required to reduce the data volume to a manageable level for transmission to Earth. We will outline the comprehensive data processing architecture of the PLATO payload's data processing system (DPS), including key features such as CCD windowing, on-board science algorithms, compression techniques, and fine guidance data processing. Additionally, we will detail the Assembly, Integration, and Verification (AIV) activities at DLR. This will cover the configuration of the hardware-in-the-loop test bench and the test automation and data archiving framework employed during these activities. In addition to the payload units, the test setup incorporates multiple emulators and simulators developed by various institutions. These enable not only testing of the nominal science data processing by the payload units but also provide real-time data generation for Fine Guidance System (FGS) testing. A significant challenge in verifying on-board satellite applications is ensuring repeatability, reproducibility and consistency due to the vast number of test cases and related documentation. To address this, our test scripts serve as the single source of truth, generating test procedures, test reports, requirements verification control matrices, and database coverage reports. Additionally, all raw data can be recorded and stored in a database, enriched with metadata through further automatic processing to facilitate post-test data analysis. The data processing and archiving pipeline employs a distributed architecture, allowing all workstations to contribute to the data management system and share computationall load.