AIROPA II: modeling instrumental aberrations for off-axis point spread functions in adaptive optics
AIROPA II: modeling instrumental aberrations for off-axis point spread functions in adaptive optics
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AIROPA II：自适应光学中离轴点扩散函数的仪器像差建模
DOI：
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发表时间：
2022
影响因子：
2.3
通讯作者：
K. Matthews
中科院分区：
文献类型：
--
作者：
A. Ciurlo;P. Turri;G. Witzel;Jessica R. Lu;T. Do;B. Sitarski;M. Fitzgerald;A. Ghez;C. Alvarez;S. Terry;G. Doppmann;J. Lyke;S. Ragland;R. Campbell;K. Matthews
抽象的。用单共轭自适应光学(AO)获得的图像显示了点扩展函数(PSF)的空间变化，这是由于大气各向异性和仪器像差的共同作用。在整个视野范围内，对PSF的了解不足，严重影响了充分利用AO能力的能力。AIROPA项目旨在为凯克天文台的NIRC2成像仪模拟这些PSF变化。在这里，我们介绍了整个NIRC2视场内仪器相位像差的特征，并提出了一个用于量化校准质量的度量，即未解释的方差分数(FVU)。我们使用在人造光源上获得的相位多样性测量来表征视场中像差的变化及其随时间的演变。我们发现，整个探测器的波前误差有一个共同的日变化(残差的均方根为94 nm)，但整个视场的像差是非常稳定的(不同历元之间的残差的均方根为59 nm)。这意味着，仪器校准通常只需要在探测器的中心进行监控，而整个视场中更耗时的变化可以不那么频繁地表征(最有可能是在硬件升级时)。此外，我们通过探测器上的光纤图像的真实数据对AIROPA的仪器模型进行了检验。我们发现，通过对视场中的PSF变化进行建模，FVU度量提高了60%，伪源检测减少了70%。
Abstract. Images obtained with single-conjugate adaptive optics (AO) show spatial variation of the point spread function (PSF) due to both atmospheric anisoplanatism and instrumental aberrations. The poor knowledge of the PSF across the field of view strongly impacts the ability to take full advantage of AO capabilities. The AIROPA project aims to model these PSF variations for the NIRC2 imager at the Keck Observatory. Here, we present the characterization of the instrumental phase aberrations over the entire NIRC2 field of view and we present a metric for quantifying the quality of the calibration, the fraction of variance unexplained (FVU). We used phase diversity measurements obtained on an artificial light source to characterize the variation of the aberrations across the field of view and their evolution with time. We find that there is a daily variation of the wavefront error (RMS of the residuals is 94 nm) common to the whole detector, but the differential aberrations across the field of view are very stable (RMS of the residuals between different epochs is 59 nm). This means that instrumental calibrations need to be monitored often only at the center of the detector, and the much more time-consuming variations across the field of view can be characterized less frequently (most likely when hardware upgrades happen). Furthermore, we tested AIROPA’s instrumental model through real data of the fiber images on the detector. We find that modeling the PSF variations across the field of view improves the FVU metric by 60% and reduces the detection of fake sources by 70%.
AIROPA III：测试模拟数据和实测数据
DOI：
10.1117/1.jatis.8.3.039002
发表时间：
2022
期刊：
and Systems
影响因子：
--
作者：
Turri, Paolo;Lu, Jessica R.;Witzel, Gunther;Ciurlo, Anna;Do, Tuan;Ghez, Andrea M.;Fitzgerald, Michael P.;Britton, Matthew C.;Ragland, Sam;Terry, Sean K.
通讯作者：
Terry, Sean K.