星周盘的天文高对比度成像：MUSTARD 逆问题方法与基于 PCA 的方法对比
Astronomical high-contrast imaging of circumstellar disks: MUSTARD inverse-problem versus PCA-based methods
AI总结：
本文介绍用于高对比度星周盘成像去散斑的 MUSTARD 逆问题算法，并通过系统性测试比较其与基于 PCA 等方法在恢复延展盘形态上的优势。
摘要：
近期观测表明，年轻恒星周围的原行星盘可能包含多种多样的特征。由高对比度成像仪器产生的原始盘图像，会受到以准静态散斑形式缓慢变化的恒星光残余信号污染。因此，需要通过图像处理去除图像中的散斑并恢复星周信号。然而，依赖主流角差分成像（ADI）观测技术的现有算法受到几何偏差限制，因此在可靠推断延展盘特征形态方面面临重大挑战。过去两年中，已有四种算法为此任务开发，其中三种基于逆问题（IP）方法：REXPACO、MAYONNAISE 和 MUSTARD。在本报告中，我们将（i）介绍新的 MUSTARD 算法，并（ii）基于系统性测试讨论 IP 相比其他方法的优势。
英文摘要：
Recent observations have shown that protoplanetary disks around young stars can embed a wide variety of features. Raw disk images produced by high-contrast imaging instruments are corrupted by slowly varying residual stellar light in the form of quasi-static speckles. Hence, image processing is required to remove speckles from images and to recover circumstellar signals. Current algorithms that rely on the mainstream angular differential imaging (ADI) observing technique are however limited by geometrical biases, and therefore face a major challenge to reliably infer the morphology of extended disk features. In the last two years, four algorithms have been developed for this task, with three of them based on inverse problem (IP) approaches: REXPACO, MAYONNAISE, and \MUSTARD. In this presentation, we will (i) present the new MUSTARD algorithm and (ii) discuss the advantages of IP compared to others methods based on systematic tests.
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