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arXiv 2407.08046astro-ph.IM
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望远镜指向与导星的深度学习解决方案

Deep learning solutions to telescope pointing and guiding

Jackson Zariski, Kaitlin Kratter, Sarah Logsdon, Chad Bender, Dan Li, Heidi Schweiker, Jayadev Rajagopal, Bill McBride, Emily Hunting

AI总结:

针对WIYN望远镜指向精度不足问题,提出基于循环神经网络的指向系统,不依赖传统模型,初步结果良好,并计划推广至其他站点。

摘要:

位于基特峰国家天文台的WIYN 3.5米望远镜配备了一系列光学和近红外仪器,包括为系外行星径向速度研究而构建的极端精度光学光谱仪NEID。为了实现亚毫秒级精度,NEID对巡天效率、恒星图像定位和导星性能有严格要求,这些要求超出了望远镜原有指向和跟踪系统的原生能力。为了提高望远镜的运行效率,我们开发了一种基于循环神经网络的新型望远镜指向系统,该系统不依赖通常的指向模型(如TPoint或其他准物理基)。我们讨论了该系统的开发过程,指向问题的内在特性如何指导我们的网络设计,并展示了我们最佳模型的初步结果。我们还讨论了将该框架推广到其他站点的计划。

英文摘要:

The WIYN 3.5m Telescope at Kitt Peak National Observatory hosts a suite of optical and near infrared instruments, including an extreme precision, optical spectrograph, NEID, built for exoplanet radial velocity studies. In order to achieve sub ms precision, NEID has strict requirements on survey efficiency, stellar image positioning, and guiding performance, which have exceeded the native capabilities of the telescope's original pointing and tracking system. In order to improve the operational efficiency of the telescope we have developed a novel telescope pointing system, built on a recurrent neural network, that does not rely on the usual pointing models (TPoint or other quasi physical bases). We discuss the development of this system, how the intrinsic properties of the pointing problem inform our network design, and show preliminary results from our best models. We also discuss plans for the generalization of this framework, so that it can be applied at other sites.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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