Abstract
With the growing amount of astronomical data, there is an increasing need for automated data processing pipelines, which can extract scientific information from observation data without human interventions. A critical aspect of these pipelines is the image quality evaluation and masking algorithm, which evaluates image qualities based on various factors such as cloud coverage, sky brightness, scattering light from the optical system, point spread function size and shape, and read-out noise. Occasionally, the algorithm requires masking of areas severely affected by noise. However, the algorithm often necessitates significant human interventions, reducing data processing efficiency. In this study, we present a deep learning based image quality evaluation algorithm that uses an autoencoder to learn features of high quality astronomical images. The trained autoencoder enables automatic evaluation of image quality and masking of noise affected areas. We have evaluated the performance of our algorithm using two test cases: images with point spread functions of varying full width half magnitude, and images with complex backgrounds. In the first scenario, our algorithm could effectively identify variations of the point spread functions, which can provide valuable reference information for photometry. In the second scenario, our method could successfully mask regions affected by complex regions, which could significantly increase the photometry accuracy. Our algorithm can be employed to automatically evaluate image quality obtained by different sky surveying projects, further increasing the speed and robustness of data processing pipelines.
Abstract (translated)
随着天文观测数据的不断增加，自动化数据处理管道的需求也在增加。这些管道可以提取观测数据中的科学信息，而无需人工干预。这些管道的关键方面是图像质量评估和掩码算法，该算法根据各种因素（如云覆盖，天空亮度，光学系统散射光点，点扩散函数尺寸和形状，以及读出噪声）评估图像质量。偶尔，算法需要对受到噪声严重影响的区域进行掩码。然而，该算法通常需要进行重大的人为干预，降低数据处理效率。 在本文中，我们提出了一个基于深度学习的图像质量评估算法，该算法使用自动编码器来学习高质量天文图像的特征。训练好的自动编码器可以自动评估图像质量和掩码噪点影响区域。我们用两个测试 case来评估我们算法的性能：具有不同半幅宽点扩散函数的图像和具有复杂背景的图像。 在第一种情景中，我们的算法可以有效地识别出点扩散函数的差异，这可以为 photometry 提供有价值的参考信息。在第二种情景中，我们的方法可以成功地掩码受复杂区域影响的区域，这可以显著提高 photometry 精度。我们的算法可以应用于自动评估不同天体测量项目获得的图像质量，进一步增加数据处理管道的速度和稳健性。
URL
https://arxiv.org/abs/2405.03408
