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Astronomical Image Time Series Segmentation for Faint, Fast Object Detection

We present a method for detecting faint, fast-moving objects in astronomical image time series using image segmentation via a recurrent convolutional neural network called SMDET. We use SMDET to segment Wide-field Infrared Survey Explorer (WISE) image time series that cover the full sky and rank sky...

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Principais autores: Dan Caselden, J. Davy Kirkpatrick, Lindsey Lack, Andrew White, Federico Marocco, Aaron M. Meisner, Guillaume Colin, Bruce Baller, Kareem Ammar, Thomas P. Bickle, Hunter Brooks, S. L. Casewell, Peter R. M. Eisenhardt, Charles A. Elachi, Jacqueline K. Faherty, John W. Fowler, Daniella C. Bardalez Gagliuffi, Jonathan Gagné, Christopher R. Gelino, Jake Grigorian, Les Hamlet, Hiro Higashimura, Justin Hong, Issam Ibnouhsein, Tarun Kota, Marc J. Kuchner, Ken A. Marsh, Matteo Paz, Yadukrishna Raghu, Adam C. Schneider, Sajesh Singh, Asa Trek, Kieran Wall, Andrew Washburn, Edward L. Wright, David Zurek, The Backyard Worlds: Planet 9 Collaboration
Formato: Artigo
Idioma:Inglês
Publicado em: IOP Publishing 2026-01-01
coleção:The Astronomical Journal
Assuntos:
Astronomy image processing
Detection
Convolutional neural networks
Time series analysis
Brown dwarfs
Acesso em linha:https://doi.org/10.3847/1538-3881/ae593c
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