论文标题
Airopa II:自适应光学仪的离轴点传播功能的仪器畸变
AIROPA II: Modeling Instrumental Aberrations for Off-Axis Point Spread Functions in Adaptive Optics
论文作者
论文摘要
通过大气偏旋恒定和仪器畸变,用单相轭自适应光学器件(AO)获得的图像显示了点扩散函数(PSF)的空间变化。在整个视野中对PSF的知识不足会对充分利用AO功能的能力产生强烈影响。 Airopa项目旨在为Keck天文台的NIRC2成像仪对这些PSF变化进行建模。在这里,我们介绍了整个NIRC2视场中仪器相位畸变的表征,并提出了一个新的指标,用于量化校准的质量,方差的部分无法解释(FVU)。我们使用在人造光源上获得的相位多样性测量值来表征整个视野及其演变随时间变化的变化。我们发现,整个检测器常见的波前误差(残差的RMS为94〜nm)的每日变化,但是整个视野上的差异像差非常稳定(不同时期之间残差的RMS为59〜nm)。这意味着需要仅在检测器的中心监视器乐校准,并且整个视野的耗时较高的变化可以较少频率(当发生硬件升级时很可能)。此外,我们通过检测器上的光纤图像的真实数据测试了Airopa的仪器模型。我们发现,对整个视野的PSF变化进行建模会将FVU度量提高60 \%,并将伪造来源的检测降低70 \%。
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 new 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\%.