AI 中文总结
该研究对比FNNs与传统PMs修正天文仪器盲指向误差的效果，将FNNs应用于MeerKAT+望远镜原型数据评估其实用性，为AtLAST等项目的指向误差建模改进提供参考。
AI 中文摘要
本研究旨在比较深度学习方法（具体为前馈神经网络FNNs）与传统指向模型PMs在补偿天文仪器盲指向误差方面的有效性。正在开展的阿塔卡马大口径亚毫米波望远镜AtLAST等雄心勃勃的项目，推动了对传统指向误差PE建模可能改进方向的研究。该研究通过将前馈神经网络应用于运行中仪器的数据，评估其实用性：该仪器是位于南非南非射电天文台SARAO站点、南非猫鼬国家公园内的Max Planck射电天文研究所MPIfR的MeerKAT+望远镜原型，旨在扩展当前的MeerKAT射电望远镜阵列。
英文摘要
This study aims to compare the effectiveness of deep learning methods, specifically Feedforward Neural Networks (FNNs), with traditional Pointing Models (PMs) for compensating Blind Pointing Errors in astronomical instruments. Ambitious projects like the ongoing study for the Atacama Large Aperture Submillimeter Telescope (AtLAST) inspired the investigation of possible improvements to traditional Pointing Error (PE) modeling. The study assesses the practicality of FNNs by applying them to data from an instrument in operation: a precursor MeerKAT+ telescope from the Max Planck Institute for Radio Astronomy (MPIfR), intended to extend the current MeerKAT Radio Telescope Array at the South African Radio Astronomy Observatory (SARAO) site in the Meerkat National Park in South Africa.
CommentsSubmitted as an SPIE proceedings manuscript for the 2024 SPIE Astronomical Telescopes + Instrumentation Conference
