arXiv subjects

Anko Boerner

Publications and source records attributed to Anko Boerner.

3 recordsLinked to original sources

arXiv2026

Assessment of PLATO Science Performance

The PLATO mission is scheduled for launch early 2027. In this paper we present an overview of the performance drivers for the mission at the time where all flight models of the cameras have been tested and integrated on the optical bench. The PLATO consortium needs an estimate of the planet detection yield to dimension the ground-based radial velocity follow-up resources. We provide updated estimates on the yield of planet detections that can be expected from the mission under certain assumptions. As of today, large uncertainties remain on the planet occurrence rates, especially for small planets in long-period orbits, and on our ability to detect these planets in the presence of stellar variability and instrumental noise. To partially overcome these limitations, we compare results using different planet occurrence rates, detectability rates, and we include an estimate on the expected contribution of stellar variability to the noise budget. The final detection yield of PLATO will provide constraints to planet occurrence rates which in turn will help constraining planet formation models.

astro-ph.EP↗

arXiv2024

Real-time Noise Source Estimation of a Camera System from an Image and Metadata

Autonomous machines must self-maintain proper functionality to ensure the safety of humans and themselves. This pertains particularly to its cameras as predominant sensors to perceive the environment and support actions. A fundamental camera problem addressed in this study is noise. Solutions often focus on denoising images a posteriori, that is, fighting symptoms rather than root causes. However, tackling root causes requires identifying the noise sources, considering the limitations of mobile platforms. This work investigates a real-time, memory-efficient and reliable noise source estimator that combines data- and physically-based models. To this end, a DNN that examines an image with camera metadata for major camera noise sources is built and trained. In addition, it quantifies unexpected factors that impact image noise or metadata. This study investigates seven different estimators on six datasets that include synthetic noise, real-world noise from two camera systems, and real field campaigns. For these, only the model with most metadata is capable to accurately and robustly quantify all individual noise contributions. This method outperforms total image noise estimators and can be plug-and-play deployed. It also serves as a basis to include more advanced noise sources, or as part of an automatic countermeasure feedback-loop to approach fully reliable machines.

cs.CV↗

arXiv2022

Region-of-Interest Prioritised Sampling for Constrained Autonomous Exploration Systems

Goal oriented autonomous operation of space rovers has been known to increase scientific output of a mission. In this work we present an algorithm, called the RoI Prioritised Sampling (RPS), that prioritises Region-of-Interests (RoIs) in an exploration scenario in order to utilise the limited resources of the imaging instrument on the rover effectively. This prioritisation is based on an estimator that evaluates the change in information content at consecutive spatial scales of the RoIs without calculating the finer scale reconstruction. The estimator, called the Refinement Indicator (RI), is motivated and derived. Multi-scale acquisition approaches, based on classical and multilevel compressed sensing, with respect to the single pixel camera architecture are discussed. The performance of the algorithm is verified on remote sensing images and compared with the state-of-the-art multi-resolution reconstruction algorithms. At the considered sub-sampling rates the RPS is shown to better utilise the system resources for reconstructing the RoIs.

eess.IV↗