Discovery of 108 Faint Asteroid Candidates in Archival NEOWISE Infrared Space Telescope Images Using a Custom Deep Learning-Based Pipeline

Gauri Vani Todur ()

International Journal of Innovative Science and Research Technology (IJISRT), 2026, vol. 11, issue 06, 3212-3225

Abstract: Near Earth asteroids (NEAs) are defined as minor planets with orbits close to Earth that could pose potential collision risks. Over 98% of the estimated 3 million small near-Earth asteroids (19-140 meters) remain undiscovered due to their faintness, leaving Earth vulnerable to impacts like the undetected 18-meter-sized 2013 Chelyabinsk meteor that caused injuries to ∼1600 people. The purpose of this research is to develop an automated deep learning-based pipeline to accurately discover small, faint, near-Earth asteroids. Because of its ability to pick up faint thermal signals, archival image data in the W2 band from the Near-Earth Object Wide-Field Infrared Survey Explorer (NEOWISE) was used. First, I filtered out stationary objects using a star masking process, and bright artifact pixel patterns by referencing WISE archival bitmask frames.

Keywords: Near-Earth Asteroids; Convolutional Neural Network; NEOWISE; Deep Learning; Infrared Astronomy; Moving Object Detection; Planetary Defense; Synthetic Image Generation; Asteroid Tracklet Linking. (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:cvr:ijisrt:2026:06:ijisrt26jun1572

DOI: 10.38124/ijisrt/26jun1572

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