Supervised deep learning was recently introduced in high-contrast imaging (HCI) through the SODINN algorithm, a convolutional neural network designed for exoplanet detection in angular differential imaging (ADI) datasets. The benchmarking of HCI algorithms within the Exoplanet Imaging Data Challenge (EIDC) showed that (i) SODINN can produce a high number of false positives in the final detection maps, and (ii) algorithms processing images in a more local manner perform better. This work aims to improve the SODINN detection performance by introducing new local processing approaches and adapting its learning process accordingly. We propose NA-SODINN, a new deep learning binary classifier based on a convolutional neural network (CNN) that better captures image noise correlations in ADI-processed frames by identifying noise regimes. Our new approach was tested against its predecessor, as well as two SODINN-based hybrid models and a more standard annular-PCA approach, through local receiving operating characteristics (ROC) analysis of ADI sequences from the VLT/SPHERE and Keck/NIRC-2 instruments. Results show that NA-SODINN enhances SODINN in both sensitivity and specificity, especially in the speckle-dominated noise regime. NA-SODINN is also benchmarked against the complete set of submitted detection algorithms in EIDC, in which we show that its final detection score matches or outperforms the most powerful detection algorithms.Throughout the supervised machine learning case, this study illustrates and reinforces the importance of adapting the task of detection to the local content of processed images.

翻译：监督深度学习近日通过SODINN算法被引入高对比度成像领域，该算法是一种专为角差分成像数据集中的系外行星探测设计的卷积神经网络。系外行星成像数据挑战赛对HCI算法的基准测试表明：（i）SODINN在最终检测图中可能产生大量假阳性；（ii）以更局部方式处理图像的算法性能更优。本研究旨在通过引入新的局部处理方法并相应调整学习过程来提升SODINN的检测性能。我们提出NA-SODINN——一种基于卷积神经网络的新型深度学习二分类器，通过识别噪声区间更准确地捕捉经ADI处理图像帧中的噪声相关性。我们的新方法与其前身、两种基于SODINN的混合模型以及更标准的环形PCA方法进行了对比，利用VLT/SPHERE和Keck/NIRC-2仪器获取的ADI序列进行局部受试者工作特征分析。结果表明，NA-SODINN在敏感性和特异性方面均优于SODINN，尤其在散斑主导的噪声区间表现突出。NA-SODINN还与EIDC中所有提交的检测算法进行了完整基准测试，其最终检测得分与最强检测算法持平甚至超越。通过这一监督机器学习案例，本研究阐明并强化了根据处理图像的局部内容调整检测任务的重要性。