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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Bibliographic Details

Published in:	The Astronomical Journal
Main Authors:	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
Format:	Article
Language:	English
Published:	IOP Publishing 2026-01-01
Subjects:	Astronomy image processing Detection Convolutional neural networks Time series analysis Brown dwarfs
Online Access:	https://doi.org/10.3847/1538-3881/ae593c