Direct imaging of Earth-like exoplanets is one of the most prominent
scientific drivers of the next generation of ground-based telescopes.
Typically, Earth-like exoplanets are located at small angular separations from
their host stars, making their detection difficult. Consequently, the adaptive
optics (AO) system's control algorithm must be carefully designed to
distinguish the exoplanet from the residual light produced by the host star.
A new promising avenue of research to improve AO control builds on
data-driven control methods such as Reinforcement Learning (RL). RL is an
active branch of the machine learning research field, where control of a system
is learned through interaction with the environment. Thus, RL can be seen as an
automated approach to AO control, where its usage is entirely a turnkey
operation. In particular, model-based reinforcement learning (MBRL) has been
shown to cope with both temporal and misregistration errors. Similarly, it has
been demonstrated to adapt to non-linear wavefront sensing while being
efficient in training and execution.
In this work, we implement and adapt an RL method called Policy Optimization
for AO (PO4AO) to the GHOST test bench at ESO headquarters, where we
demonstrate a strong performance of the method in a laboratory environment. Our
implementation allows the training to be performed parallel to inference, which
is crucial for on-sky operation. In particular, we study the predictive and
self-calibrating aspects of the method. The new implementation on GHOST running
PyTorch introduces only around 700 microseconds in addition to hardware,
pipeline, and Python interface latency. We open-source well-documented code for
the implementation and specify the requirements for the RTC pipeline. We also
discuss the important hyperparameters of the method, the source of the latency,
and the possible paths for a lower latency implementation.

通过实施和适应一种名为 AO 的 RL 方法（PO4AO），我们在 ESO 总部的 GHOST 测试台上展示了这种方法在实验室环境中的强大性能，该实验允许与推理并行进行训练，对于天空操作至关重要。

基于模型的强化学习在自适应光学控制中的实验研究

Laboratory Experiments of Model-based Reinforcement Learning for  Adaptive Optics Control

Exoplanet detection by direct imaging is a difficult task: the faint signals
from the objects of interest are buried under a spatially structured nuisance
component induced by the host star. The exoplanet signals can only be
identified when combining several observations with dedicated detection
algorithms. In contrast to most of existing methods, we propose to learn a
model of the spatial, temporal and spectral characteristics of the nuisance,
directly from the observations. In a pre-processing step, a statistical model
of their correlations is built locally, and the data are centered and whitened
to improve both their stationarity and signal-to-noise ratio (SNR). A
convolutional neural network (CNN) is then trained in a supervised fashion to
detect the residual signature of synthetic sources in the pre-processed images.
Our method leads to a better trade-off between precision and recall than
standard approaches in the field. It also outperforms a state-of-the-art
algorithm based solely on a statistical framework. Besides, the exploitation of
the spectral diversity improves the performance compared to a similar model
built solely from spatio-temporal data.

该研究基于卷积神经网络，结合空间，时间和光谱特征，利用多次观测的图像数据中残留信号检测方法，有效提高了外行星探测的精度和召回率。

利用统计和深度学习模型结合多光谱数据，提高高对比度直接成像中外行星的检测

Combining multi-spectral data with statistical and deep-learning models  for improved exoplanet detection in direct imaging at high contrast

Direct imaging of exoplanetary systems is a powerful technique that can
reveal Jupiter-like planets in wide orbits, can enable detailed
characterization of planetary atmospheres, and is a key step towards imaging
Earth-like planets. Imaging detections are challenging due to the combined
effect of small angular separation and large luminosity contrast between a
planet and its host star. High-contrast observations with the Keck and Gemini
telescopes have revealed three planets orbiting the star HR 8799, with
projected separations of 24, 38, and 68 astronomical units. Multi-epoch data
show counter-clockwise orbital motion for all three imaged planets. The low
luminosity of the companions and the estimated age of the system imply
planetary masses between 5 and 13 times that of Jupiter. This system resembles
a scaled-up version of the outer portion of our Solar System.

研究通过 Keck 和 Gemini 望远镜的高对比观测发现了三颗绕 HR 8799 行星的成像行星，其中一颗距离恒星 68 天文单位，这些伴星的低光度和估计的系统年龄表明它们的质量介于 5 至 13 个木星质量之间，并且该行星系统与我们太阳系的外部区域发生了尺寸上的相似。