arXiv subjects

Anko Börner

Publications and source records attributed to Anko Börner.

4 recordsLinked to original sources

arXiv2026

The PLATO Science Calibration and Validation Plan: Targets for the First Long-pointing Field

In order to meet the science goals of the PLATO space mission, an extensive science calibration and validation plan has been designed. This paper describes this plan, as well as the methodology adopted to select the science calibration and validation stars that have entered its input catalogue. This is the so-called {\tt scvPIC}, which is part of the general PLATO Input Catalogue (PIC) for the first selected long pointing field in the Southern Hemisphere known as LOPS2. While many of PLATO's science requirements needed dedicated stars as calibrators as discussed here, its most stringent requirement is the delivery of the age of the host stars of exoplanetary systems with an accuracy better than 10\% for a G0V star of {\it V} = 10 mag, i.e. a nearby Sun-like star. This is presently not within reach for large populations of dwarfs and subgiants in the Milky Way as it requires the models of their stellar interiors to be improved. We discuss how this ambitious age requirement led to the selection of tens of thousands of red giants, and of thousands of main-sequence early F-type gravity-mode pulsators in order to deduce their internal rotation profile across stellar evolution. This asteroseismic observable will then be imported as key information into improved models of dwarfs and subgiants in the Milky Way as optimal modelling tools for ever better age-dating of the exoplanet hosts as the PLATO mission moves along. Additional calibrators and validators included in the {\tt scvPIC} are a few thousands of binaries, a few hundreds of legacy and benchmark stars, a few hundred photometrically stable stars, and six transiting brown dwarfs.

astro-ph.SR↗

arXiv2024

PLATO's signal and noise budget

ESA's PLATO mission aims the detection and characterization of terrestrial planets around solar-type stars as well as the study of host star properties. The noise-to-signal ratio (NSR) is the main performance parameter of the PLATO instrument, which consists of 24 Normal Cameras and 2 Fast Cameras. In order to justify, verify and breakdown NSR-relevant requirements the software simulator PINE was developed. PINE models the signal pathway from a target star to the digital output of a camera based on physical models and considers the major noise contributors. In this paper, the simulator's coarse mode is introduced which allows fast performance analyses on instrument level. The added value of PINE is illustrated by exemplary applications.

astro-ph.EP↗

arXiv2023

Monitoring and Adapting the Physical State of a Camera for Autonomous Vehicles

Autonomous vehicles and robots require increasingly more robustness and reliability to meet the demands of modern tasks. These requirements specially apply to cameras onboard such vehicles because they are the predominant sensors to acquire information about the environment and support actions. Cameras must maintain proper functionality and take automatic countermeasures if necessary. Existing solutions are typically tailored to specific problems or detached from the downstream computer vision tasks of the machines, which, however, determine the requirements on the quality of the produced camera images. We propose a generic and task-oriented self-health-maintenance framework for cameras based on data- and physically-grounded models. To this end, we determine two reliable, real-time capable estimators for typical image effects of a camera in poor condition (blur, noise phenomena and most common combinations) by evaluating traditional and customized machine learning-based approaches in extensive experiments. Furthermore, we implement the framework on a real-world ground vehicle and demonstrate how a camera can adjust its parameters to counter an identified poor condition to achieve optimal application capability based on experimental (non-linear and non-monotonic) input-output performance curves. Object detection is chosen as target application, and the image effects motion blur and sensor noise as conditioning examples. Our framework not only provides a practical ready-to-use solution to monitor and maintain the health of cameras, but can also serve as a basis for extensions to tackle more sophisticated problems that combine additional data sources (e.g., sensor or environment parameters) empirically in order to attain fully reliable and robust machines. Code: https://github.com/MaikWischow/Camera-Condition-Monitoring

cs.CV↗

arXiv2020

Sensor Artificial Intelligence and its Application to Space Systems -- A White Paper

Information and communication technologies have accompanied our everyday life for years. A steadily increasing number of computers, cameras, mobile devices, etc. generate more and more data, but at the same time we realize that the data can only partially be analyzed with classical approaches. The research and development of methods based on artificial intelligence (AI) made enormous progress in the area of interpretability of data in recent years. With growing experience, both, the potential and limitations of these new technologies are increasingly better understood. Typically, AI approaches start with the data from which information and directions for action are derived. However, the circumstances under which such data are collected and how they change over time are rarely considered. A closer look at the sensors and their physical properties within AI approaches will lead to more robust and widely applicable algorithms. This holistic approach which considers entire signal chains from the origin to a data product, "Sensor AI", is a highly relevant topic with great potential. It will play a decisive role in autonomous driving as well as in areas of automated production, predictive maintenance or space research. The goal of this white paper is to establish "Sensor AI" as a dedicated research topic. We want to exchange knowledge on the current state-of-the-art on Sensor AI, to identify synergies among research groups and thus boost the collaboration in this key technology for science and industry.

cs.CY↗