We present an analysis of six independent on-sky datasets taken with the Keck-II/NIRC2 instrument. Using the off-axis point spread function (PSF) reconstruction software AIROPA, we extract stellar astrometry, photometry, and other fitting metrics to characterize the performance of this package. We test the effectiveness of AIROPA to reconstruct the PSF across the field of view in varying atmospheric conditions, number and location of PSF reference stars, stellar crowding, and telescope position angle (PA). We compare the astrometric precision and fitting residuals between a static PSF model and a spatially varying PSF model that incorporates instrumental aberrations and atmospheric turbulence during exposures. Most of the fitting residuals we measure show little to no improvement in the variable-PSF mode over the single-PSF mode. For one of the data sets, we find photometric performance is significantly improved (by ∼10 × ) by measuring the trend seen in photometry as a function of off-axis location. For nearly all other metrics we find comparable astrometric and photometric precision across both PSF modes, with a ∼13 % smaller astrometric uncertainty in variable-PSF mode in the best case. We largely confirm that the spatially variable PSF does not significantly improve the astrometric and other PSF fitting residuals over the static PSF for on-sky observations. We attribute this to unaccounted instrumental aberrations that are not characterized through afternoon adaptive optics (AO) bench calibrations.
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%.
We present evaluations of the Keck Telescope’s adaptive optics (AO) performance on Milky Way Galactic center imaging and spectroscopic observations using three different AO setups: laser guide star with infrared (IR) tip-tilt correction, laser guide star with visible tip-tilt correction, and infrared natural guide star with a pyramid wavefront sensor. Observations of the Galactic Center can utilize a bright IR tip-tilt star (K′ = 7.4 mag) for corrections, which is over 10 arcseconds closer than the optical tip-tilt star. The proximity of this IR star enables the comparison of the aforementioned AO configurations. We present performance metrics such as full-width-at-half-maximum (FWHM), Strehl ratio, and spectral signal to noise ratio and their relations to atmospheric seeing conditions. The IR tip-tilt star decreases the median spatial FWHM by 31% in imaging data and 30% in spectroscopy. Median Strehl for imaging data improves by 24%. Additionally, the IR star removes the seeing dependence from differential tip-tilt error in both imaging and spectroscopic data. This evaluation provides important work for ongoing upgrades to AO systems, such as the Keck All sky Precision Adaptive Optics (KAPA) upgrade on the Keck I Telescope, and the development of new AO systems for extremely large telescopes.
Adaptive optics (AO) images from the W. M. Keck Observatory have delivered numerous influential scientific results, including detection of multi-system asteroids, the supermassive black hole at the center of the Milky Way, and directly imaged exoplanets. Specifically, the precise and accurate astrometry these images yield was used to measure the mass of the supermassive black hole using orbits of the surrounding star cluster. Despite these successes, one of the major obstacles to improved astrometric measurements is the spatial and temporal variability of the point-spread function delivered by the instruments. Anisoplanatic and Instrumental Reconstruction of Off-axis PSFs for AO (AIROPA) is a software package for the astrometric and photometric analysis of AO images using point-spread function fitting together with the technique of point-spread function reconstruction. In AO point-spread function reconstruction, the knowledge of the instrument performance and of the atmospheric turbulence is used to predict the long-exposure point-spread function of an observation. We present the results of our tests using AIROPA on both simulated and on-sky images of the Galactic Center. We find that our method is very reliable in accounting for the static aberrations internal to the instrument, but it does not improve significantly the accuracy on sky, possibly due to uncalibrated telescope aberrations.
PSF knowledge is central to extract science from observations with adaptive optics.
However, it is often challenging to have a good PSF estimate. For instance, this is a problem for the integral field unit (IFU) OSIRIS at Keck Observatory. OSIRIS has a field of only few arcseconds, and it is often impossible to obtain a good empirical PSF. OSIRIS is equipped with an imager designed to track changes in the PSF on a reference star. However, the imager is 20 arcseconds away, which prevents to apply the observed PSF directly to spectroscopic data.
We developed a new software package to predict PSF variability for Keck AO images (AIROPA, see Paolo Turri’s contribution, this conference). To properly use the parallel imager to predict a PSF on the IFU, we adapted the code to the OSIRIS case (AIROPA-IFU).
Here, we present results of the application of this post-processing tools to Galactic Center observation. We also discuss the challenges encountered and the lessons learned when doing PSF
KEYWORDS: Point spread functions, Imaging systems, Stars, Spectrographs, Signal to noise ratio, Adaptive optics, Atmospheric modeling, Data modeling, Astronomy
Knowledge of the point spread function (PSF) is critical to many astronomical science cases. However, the PSF can be very difficult to estimate for cases where there are many crowded point sources or for observations of extended objects. Additionally, for adaptive optics observations, the PSF can be very complex with both spatial and temporal variability in the PSF. Integral-field spectroscopy behind adaptive optics is especially challenging because the fields of view are typically too small to sample the halo for even a single PSF. Here, we present a method for semi-empirical PSF reconstruction for integral field spectrographs using a combination of point source observations on a parallel imager, instrumental aberration measurements, and atmospheric turbulence profiles. This work builds upon the PSF reconstruction project AIROPA designed for imaging and extending it to IFU work (AIROPA-IFU). By using empirical calibrators from the parallel imager, which has a much larger field of view, and accounting for anisoplantic effects and instrumental aberrations, we can predict the PSF on the spectrograph. An important aspect is being able to predict the PSF at many different wavelengths based on observations from broad-band imaging. Here, we discuss how science cases such as observations of stars at the Galactic center can benefit from this method. We also establish metrics to quantitatively assess the performance of PSF reconstruction. We show that for bright stars, AIROPA-IFU can produce spectra with signal to noise ratio 50% higher than with simple aperture extraction of a data cube.
The integral field spectrograph OSIRIS at Keck I has been used to measure the motion of the stars around the supermassive black hole at the Center of the Galaxy. The small field of view provided and the crowding of the region prevent any good PSF estimate. A parallel imager can be used simultaneously to the IFU. However, its distance of 19 arcseconds prevents the observed PSF to be directly applied to the IFU because of anisoplanatism and instrumental aberrations. The Galactic Center Group at UCLA has developed an algorithms to predict PSF variability for Keck AO images (Off-axis PSF reconstruction, AIROPA software package). AIROPA allows us to use the parallel imager to correctly predict the IFU’s PSF. We modified this package to adapt it to the case of OSIRIS imager and IFU (AIROPA-IFU) and characterized the instrumental aberrations of both detectors. Here, we present preliminary results of the application of this post-processing tool to OSIRIS datasets of the Galactic Center.
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