论文标题
LBT灵魂数据作为Micado PSF-R工具的科学测试工作台
LBT SOUL data as a science test bench for MICADO PSF-R tool
论文作者
论文摘要
当前的最新自适应光学器件(AO)提供了具有高肌张力比(SR)的基于地面的,衍射有限的观测值。但是,需要对点扩散函数(PSF)的详细知识,以充分利用这些数据的科学潜力。这对于将配备30米级望远镜的下一代AO工具至关重要,因为PSF的表征将是强制性的,以满足计划的科学要求。因此,人们对开发工具的兴趣越来越浓厚,该工具可以准确地重建所谓的PSF重建的AO系统的PSF。在这种情况下,PSF-R服务是Micado@Elt工具的计划可交付,我们的小组负责其开发。在Micado的情况下,正在采用盲目的PSF-R方法,以对科学案例具有最广泛的适用性。这意味着仅依靠遥测和校准,对PSF进行了重建,而无需从科学数据中提取信息。虽然目前正在开发我们的PSF-R算法,但其实现已经足够成熟,可以通过实际观察结果测试性能。在本演讲中,我们将讨论我们重建的PSF的可靠性以及在LBT的Soul+Luci仪器中进行的明亮,轴心观察的科学量测量中引入的不确定性。这是我们算法对真实数据的第一个应用。它展示了它的准备水平,并为进一步的测试铺平了道路。我们的PSF-R算法能够以分别小于2%和4.5%的误差的一半最大为一半的SR和全宽度。由于对理想科学案例的一组专门的模拟观察,我们对获得的重建PSF进行了科学评估。
Current state-of-the-art adaptive optics (AO) provides ground-based, diffraction-limited observations with high Strehl ratios (SR). However, a detailed knowledge of the point spread function (PSF) is required to fully exploit the scientific potential of these data. This is even more crucial for the next generation AO instruments that will equip 30-meter class telescopes, as the characterization of the PSF will be mandatory to fulfill the planned scientific requirements. For this reason, there is a growing interest in developing tools that accurately reconstruct the observed PSF of AO systems, the so-called PSF reconstruction. In this context, a PSF-R service is a planned deliverable for the MICADO@ELT instrument and our group is in charge of its development. In the case of MICADO, a blind PSF-R approach is being pursued to have the widest applicability to science cases. This means that the PSF is reconstructed without extracting information from the science data, relying only on telemetry and calibrations. While our PSF-R algorithm is currently being developed, its implementation is mature enough to test performances with actual observations. In this presentation we will discuss the reliability of our reconstructed PSFs and the uncertainties introduced in the measurements of scientific quantities for bright, on-axis observations taken with the SOUL+LUCI instrument of the LBT. This is the first application of our algorithm to real data. It demonstrates its readiness level and paves the way to further testing. Our PSF-R algorithm is able to reconstruct the SR and full-width at half maximum of the observed PSFs with errors smaller than 2% and 4.5%, respectively. We carried out the scientific evaluation of the obtained reconstructed PSFs thanks to a dedicated set of simulated observations of an ideal science case.