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
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.
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.