We present a novel machine-learning approach for detecting faint point sources in high-contrast adaptive optics imaging datasets. The most widely used algorithms for primary subtraction aim to decouple bright stellar speckle noise from planetary signatures by subtracting an approximation of the temporally evolving stellar noise from each frame in an imaging sequence. Our approach aims to improve the stellar noise approximation and increase the planet detection sensitivity by leveraging deep learning in a novel direct imaging post-processing algorithm. We show that a convolutional autoencoder neural network, trained on an extensive reference library of real imaging sequences, accurately reconstructs the stellar speckle noise at the location of a potential planet signal. This tool is used in a post-processing algorithm we call Direct Exoplanet Detection with Convolutional Image Reconstruction, or ConStruct. The reliability and sensitivity of ConStruct are assessed using real Keck/NIRC2 angular differential imaging datasets. Of the 30 unique point sources we examine, ConStruct yields a higher S/N than traditional PCA-based processing for 67$\%$ of the cases and improves the relative contrast by up to a factor of 2.6. This work demonstrates the value and potential of deep learning to take advantage of a diverse reference library of point spread function realizations to improve direct imaging post-processing. ConStruct and its future improvements may be particularly useful as tools for post-processing high-contrast images from the James Webb Space Telescope and extreme adaptive optics instruments, both for the current generation and those being designed for the upcoming 30 meter-class telescopes.

翻译：我们提出一种新颖的机器学习方法，用于在高对比度自适应光学成像数据集中探测微弱点源。现有主流的减法算法通过从成像序列的每一帧中减去随时间演变的恒星噪声近似信号，从而将明亮的恒星散斑噪声与行星信号解耦。我们的方法旨在通过利用深度学习创新性地改进直接成像后处理算法，提升恒星噪声近似精度并增强行星探测灵敏度。研究表明，基于真实成像序列大规模参考库训练的卷积自编码神经网络，能够精确重建潜在行星信号位置的恒星散斑噪声。该工具被集成至名为"基于卷积图像重建的系外行星直接探测（ConStruct）"的后处理算法中。通过真实凯克望远镜/NIRC2角差分成像数据集评估ConStruct的可靠性和灵敏度。在检测的30个独特点源中，ConStruct在67%的案例中获得了比传统PCA处理方法更高的信噪比，相对对比度提升最高达2.6倍。本工作证明了深度学习利用多样化点扩散函数参考库优化直接成像后处理的价值与潜力。ConStruct及其未来改进版本特别适用于詹姆斯·韦伯空间望远镜及极端自适应光学仪器的高对比度图像后处理，既适配当前设备，也服务于正在设计的30米级下一代望远镜。