论文标题
朝向低地球轨道的数据驱动的天空模型，如哈勃太空望远镜所观察到的
Towards a data-driven model of the sky from low Earth orbit as observed by the Hubble Space Telescope
论文作者
论文摘要
在低地轨道（LEO）中，空间望远镜观察到的天空可以由来自大地，太阳和月亮在内的多个来源的流浪光主导。这种杂散的光对旨在对超级后背景光（EBL）进行安全测量的任务提出了重大挑战。在这项工作中，我们量化了杂散的光线观测到哈勃太空望远镜（HST）高级相机进行调查的影响。通过选择轨道参数，我们成功地将图像隔离为含有最小和高水平的地球的天空。此外，我们发现CERES卫星的天气观测与观察到的HST天空表面亮度相关，表明将此类数据纳入天空的值。最后，我们提出了一个机器学习模型的天空学习模型，该模型是根据这项工作中使用的数据训练的，以预测观察到的天空表面亮度。我们证明，我们的初始模型能够预测一系列条件下的总天空亮度，占真实测量的天空的3.9％以内。此外，我们发现该模型比当前的黄道十二辈光模型更好地匹配杂散的无光观测。
The sky observed by space telescopes in Low Earth Orbit (LEO) can be dominated by stray light from multiple sources including the Earth, Sun and Moon. This stray light presents a significant challenge to missions that aim to make a secure measurement of the Extragalactic Background Light (EBL). In this work we quantify the impact of stray light on sky observations made by the Hubble Space Telescope (HST) Advanced Camera for Surveys. By selecting on orbital parameters we successfully isolate images with sky that contain minimal and high levels of Earthshine. In addition, we find weather observations from CERES satellites correlates with the observed HST sky surface brightness indicating the value of incorporating such data to characterise the sky. Finally we present a machine learning model of the sky trained on the data used in this work to predict the total observed sky surface brightness. We demonstrate that our initial model is able to predict the total sky brightness under a range of conditions to within 3.9% of the true measured sky. Moreover, we find that the model matches the stray light-free observations better than current physical Zodiacal light models.
