Machine learning, and eventually true artificial intelligence techniques, are extremely important advancements in astrophysics and astronomy. We explore the application of deep learning using neural networks in order to automate the detection of astronomical bodies for future exploration missions, such as missions to search for signatures or suitability of life. The ability to acquire images, analyze them, and send back those that are important, as determined by the deep learning algorithm, is critical in bandwidth-limited applications. Our previous foundational work solidified the concept of using simulator images and deep learning in order to detect planets. Optimization of this process is of vital importance, as even a small loss in accuracy might be the difference between capturing and completely missing a possibly-habitable nearby planet. Through computer vision, deep learning, and simulators, we introduce methods that optimize the detection of exoplanets. We show that maximum achieved accuracy can hit above 98% for multiple model architectures, even with a relatively small training set.

翻译：机器学习,以及最终真实的人工智能技术,是天体物理学和天文学方面的极为重要的进步。我们探索运用神经网络进行深层学习,以便在未来的探索任务中,例如寻找生命的签名或适切性的任务中,将天文体的探测工作自动化。获得图像、分析图像和发送由深层次学习算法确定的重要图像的能力,在带宽限制的应用中至关重要。我们先前的基础工作巩固了使用模拟图像和深层学习以探测行星的概念。优化这一过程至关重要,因为精度方面的微小损失可能是捕捉和完全丢失一个可能居住于附近的行星之间的差别。我们通过计算机的视觉、深层学习和模拟器,引入了优化外行星探测的方法。我们显示,即使使用相对小的培训装置,对于多个模型结构,达到的最高精确度可以超过98%。