Discovering Faint and High Apparent Motion Rate Near-Earth Asteroids Using A Deep Learning Program
Franklin Wang, Jian Ge, Kevin Willis

TL;DR
This paper presents a deep learning method using a convolutional neural network trained on simulated data to detect faint, fast-moving near-Earth asteroids, successfully discovering six new objects in ZTF data.
Contribution
The study introduces a neural network trained solely on simulated asteroid streaks, enhancing detection sensitivity for faint, fast-moving near-Earth objects.
Findings
Achieved 98.7% accuracy and 0.02% false positive rate on simulated data.
Discovered six previously unknown asteroids in ZTF data.
Detected asteroids with magnitudes 19.0-20.3 and motion rates 6.8-24 deg/day.
Abstract
Although many near-Earth objects have been found by ground-based telescopes, some fast-moving ones, especially those near detection limits, have been missed by observatories. We developed a convolutional neural network for detecting faint fast-moving near-Earth objects. It was trained with artificial streaks generated from simulations and was able to find these asteroid streaks with an accuracy of 98.7% and a false positive rate of 0.02% on simulated data. This program was used to search image data from the Zwicky Transient Facility (ZTF) in four nights in 2019, and it identified six previously undiscovered asteroids. The visual magnitudes of our detections range from ~19.0 - 20.3 and motion rates range from ~6.8 - 24 deg/day, which is very faint compared to other ZTF detections moving at similar motion rates. Our asteroids are also ~1 - 51 m diameter in size and ~5 - 60 lunar distances…
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Taxonomy
TopicsAstro and Planetary Science · Gamma-ray bursts and supernovae · Planetary Science and Exploration
