Hintze Centre for Astrophysical Surveys

Abstract:

High-resolution absorption spectroscopy toward bright background sources has had a paramount role in understanding early galaxy formation, the evolution of the intergalactic medium and the reionisation of the Universe. However, these studies are now approaching the boundaries of what can be achieved at ground-based 8-10m class telescopes. The identification of primeval systems at the highest redshifts, within the reionisation epoch and even into the dark ages, and of the products of the first generation of stars and the chemical enrichment of the early Universe, requires observing very faint targets with a signal-to-noise ratio high enough to detect very weak spectral signatures. In this paper, we describe the giant leap forward that will be enabled by ANDES, the high-resolution spectrograph for the ELT, in these key science fields, together with a brief, non-exhaustive overview of other extragalactic research topics that will be pursued by this instrument, and its synergistic use with other facilities that will become available in the early 2030s.

Abstract:

Aims. The expansion of satellite constellations poses a significant challenge to optical ground-based astronomical observations, as satellite trails degrade observational data and compromise research quality. Addressing these challenges requires developing robust detection methods to enhance data processing pipelines, creating a reliable approach for detecting and analyzing satellite trails that can be easily reproduced and applied by other observatories and data processing groups. Methods. Our method, called ASTA (Automated Satellite Tracking for Astronomy), combined deep learning and computer vision techniques for effective satellite trail detection. It employed a U-Net based deep learning network to initially detect trails, followed by a probabilistic Hough transform to refine the output. ASTA’s U-Net model was trained on a dataset of manually labeled full-field MeerLICHT telescope images prepared using the user-friendly LABKIT annotation tool. This approach ensured high-quality and precise annotations while facilitating quick and efficient data refinements, which streamlined the overall model development process. The thorough annotation process was crucial for the model to effectively learn the characteristics of satellite trails and generalize its detection capabilities to new, unseen data. Results. The U-Net performance was evaluated on a test set of 20 000 image patches, both with and without satellite trails, achieving approximately 0.94 precision and 0.94 recall at the selected threshold. For each detected satellite, ASTA demonstrated a high detection efficiency, recovering approximately 97% of the pixels in the trails, resulting in a False Negative Rate (FNR) of only 0.03. When applied to around 200 000 full-field MeerLICHT images focusing on Geostationary (GEO) and Geosynchronous (GES) satellites, ASTA identified 1742 trails −19.1% of the detected trails – that could not be matched to any objects in public satellite catalogs. This indicates the potential discovery of previously uncatalogued satellites or debris, confirming ASTA’s effectiveness in both identifying known satellites and uncovering new objects.