论文标题
使用深度学习程序发现微弱且高明显的运动率接近地球小行星
Discovering Faint and High Apparent Motion Rate Near-Earth Asteroids Using A Deep Learning Program
论文作者
论文摘要
尽管地面望远镜已经发现了许多近地的物体，但观测值却错过了一些快速移动的物体，尤其是那些近地检测限制的物体。我们开发了一个卷积神经网络，用于检测微弱的快速移动近地物体。它是通过模拟产生的人造条纹训练的，并且能够在模拟数据上找到这些小行星条纹的精度为98.7％，假正率为0.02％。该程序用于在2019年的四个晚上搜索来自Zwicky瞬态设施（ZTF）的图像数据，并确定了六个先前未被发现的小行星。我们检测的视觉幅度范围为〜19.0-20.3，运动速率范围为〜6.8-24 ver/天，与其他ZTF检测相比，这非常微弱。我们的小行星的大小也〜1-51 m，并且在近距离接近时〜5-60个月距距离，假设其反照率值遵循已知的小行星的反照率分布函数。使用纯模拟的数据集训练我们的模型，使该程序能够在检测微弱和快速移动的对象方面获得灵敏度，同时仍然能够恢复几乎所有使用真实检测来训练神经网络的神经网络产生的发现。我们的方法可以被任何观测员用于检测快速移动的小行星条纹。
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 away at close approach, assuming their albedo values follow the albedo distribution function of known asteroids. The use of a purely simulated dataset to train our model enables the program to gain sensitivity in detecting faint and fast-moving objects while still being able to recover nearly all discoveries made by previously designed neural networks which used real detections to train neural networks. Our approach can be adopted by any observatory for detecting fast-moving asteroid streaks.
