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Radio Galaxies in SIMBA: A MIGHTEE Comparison

ArXiv 2412.09824 (2024)

Authors:

Nicole L Thomas, Imogen H Whittam, Catherine L Hale, Leah K Morabito, Romeel Davé, Matt J Jarvis, Robin HW Cook
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Evaluating cosmological biases using photometric redshifts for Type Ia Supernova cosmology with the Dark Energy Survey Supernova Program

Monthly Notices of the Royal Astronomical Society Oxford University Press (OUP) (2024) stae2703

Authors:

RC Chen, D Scolnic, M Vincenzi, ES Rykoff, J Myles, R Kessler, B Popovic, M Sako, M Smith, P Armstrong, D Brout, TM Davis, L Galbany, J Lee, C Lidman, A Möller, BO Sánchez, M Sullivan, H Qu, P Wiseman, TMC Abbott, M Aguena, S Allam, O Alves, F Andrade-Oliveira, J Annis, D Bacon, D Brooks, A Carnero Rosell, J Carretero, A Choi, C Conselice, LN da Costa, MES Pereira, HT Diehl, P Doel, S Everett, I Ferrero, B Flaugher, J Frieman, J García-Bellido, M Gatti, E Gaztanaga, G Giannini, D Gruen, RA Gruendl, G Gutierrez, K Herner, SR Hinton, DL Hollowood, K Honscheid, D Huterer, DJ James, K Kuehn, GF Lewis, M Lima, JL Marshall, J Mena-Fernández, F Menanteau, R Miquel, RLC Ogando, A Palmese, A Pieres, AA Plazas Malagón, A Roodman, S Samuroff, E Sanchez, D Sanchez Cid, I Sevilla-Noarbe, E Suchyta, MEC Swanson, G Tarle, C To, DL Tucker, V Vikram, N Weaverdyck, J Weller
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TiDES -- Young Supernova Selection Pipeline

(2024)

Authors:

Harry Addison, Chris Frohmaier, Kate Maguire, Robert C Nichol, Isobel Hook, Stephen J Smartt
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Commensal Transient Searches with MeerKAT in Gamma-Ray Burst and Supernova Fields

(2024)

Authors:

SI Chastain, AJ van der Horst, A Horesh, A Rowlinson, A Andersson, R Diretse, M Vaccari, RP Fender, PA Woudt
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Automated detection of satellite trails in ground-based observations using U-Net and Hough transform

Astronomy & Astrophysics EDP Sciences 692 (2024) A199-A199

Authors:

F Stoppa, PJ Groot, R Stuik, P Vreeswijk, S Bloemen, DLA Pieterse, PA Woudt

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.
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