اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدDirection dependent Point spread function reconstruction for Multi-Conjugate Adaptive Optics on Giant Segmented Mirror Telescopes
60
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
Modern Giant Segmented Mirror Telescopes (GSMT) like the Extremely Large Telescope (ELT), currently under construction depend heavily on Adaptive Optics (AO) systems to correct for atmospheric turbulence. To be able to correct wider fields of view (FoV), Multi-Conjugate Adaptive Optics (MCAO) systems were introduced, which use multiple guide stars to obtain an almost uniform correction over the FoV. However, a residual blur remains in the astronmical images due to the time delay stemming from the wavefront sensor (WFS) integration time and temporal response of the deformable mirror(s) (DM). This results in a blur which can be mathematically described by a convolution of the true image with the point spread function (PSF). Due to the nature of the atmosphere and its correction, the PSF is spatially varying. In this paper, we present an algorithm for MCAO PSF reconstruction adapted to the needs of GSMTs in a storage efficient way. In particular, the PSF reconstruction algorithm for Single Conjugate Adaptive Optics (SCAO) from [33] is combined with an algorithm for atmospheric tomography from [27] to obtain a direction dependent reconstruction of the post-AO PSF. Results obtained in an end-to-end simulation tool show qualitatively good reconstruction of the PSF compared to the PSF calculated directly from the simulated incoming wavefront. Furthermore, the used algorithm has a reasonable runtime and memory consumption.
قيم البحث
اقرأ أيضاً
Context. The knowledge of the point-spread function compensated by adaptive optics is of prime importance in several image restoration techniques such as deconvolution and astrometric/photometric algorithms. Wavefront-related data from the adaptive o
ptics real-time computer can be used to accurately estimate the point-spread function in adaptive optics observations. The only point-spread function reconstruction algorithm implemented on astronomical adaptive optics system makes use of particular functions, named $U_{ij}$. These $U_{ij}$ functions are derived from the mirror modes, and their number is proportional to the square number of these mirror modes. Aims. We present here two new algorithms for point-spread function reconstruction that aim at suppressing the use of these $U_{ij}$ functions to avoid the storage of a large amount of data and to shorten the computation time of this PSF reconstruction. Methods. Both algorithms take advantage of the eigen decomposition of the residual parallel phase covariance matrix. In the first algorithm, the use of a basis in which the latter matrix is diagonal reduces the number of $U_{ij}$ functions to the number of mirror modes. In the second algorithm, this eigen decomposition is used to compute phase screens that follow the same statistics as the residual parallel phase covariance matrix, and thus suppress the need for these $U_{ij}$ functions. Results. Our algorithms dramatically reduce the number of $U_{ij}$ functions to be computed for the point-spread function reconstruction. Adaptive optics simulations show the good accuracy of both algorithms to reconstruct the point-spread function.
Here we describe a simple, efficient, and most importantly fully operational point-spread-function(PSF)-reconstruction approach for laser-assisted ground layer adaptive optics (GLAO) in the frame of the Multi Unit Spectroscopic Explorer (MUSE) Wide F
ield Mode. Based on clear astrophysical requirements derived by the MUSE team and using the functionality of the current ESO Adaptive Optics Facility we aim to develop an operational PSF-reconstruction (PSFR) algorithm and test it both in simulations and using on-sky data. The PSFR approach is based on a Fourier description of the GLAO correction to which the specific instrumental effects of MUSE Wide Field Mode (pixel size, internal aberrations, etc.) have been added. It was first thoroughly validated with full end-to-end simulations. Sensitivity to the main atmospheric and AO system parameters was analysed and the code was re-optimised to account for the sensitivity found. Finally, the optimised algorithm was tested and commissioned using more than one year of on-sky MUSE data. We demonstrate with an on-sky data analysis that our algorithm meets all the requirements imposed by the MUSE scientists, namely an accuracy better than a few percent on the critical PSF parameters including full width at half maximum and global PSF shape through the kurtosis parameter of a Moffat function. The PSFR algorithm is publicly available and is used routinely to assess the MUSE image quality for each observation. It can be included in any post-processing activity which requires knowledge of the PSF.
Astrometric precision and knowledge of the point spread function are key ingredients for a wide range of astrophysical studies including time-delay cosmography in which strongly lensed quasar systems are used to determine the Hubble constant and othe
r cosmological parameters. Astrometric uncertainty on the positions of the multiply-imaged point sources contributes to the overall uncertainty in inferred distances and therefore the Hubble constant. Similarly, knowledge of the wings of the points spread function (PSF) is necessary to disentangle light from the background sources and the foreground deflector. We analyze adaptive optics (AO) images of the strong lens system J0659+1629 obtained with the W. M. Keck Observatory using the laser guide star AO system. We show that by using a reconstructed point spread function we can i) obtain astrometric precision of $< 1$ milliarcsecond (mas), which is more than sufficient for time-delay cosmography; and ii) subtract all point-like images resulting in residuals consistent with the noise level. The method we have developed is not limited to strong lensing, and is generally applicable to a wide range of scientific cases that have multiple point sources nearby.
A suite of science instruments is critical to any high contrast imaging facility, as it defines the science capabilities and observing modes available. SCExAO uses a modular approach which allows for state-of-the-art visitor modules to be tested with
in an observatory environment on an 8-m class telescope. This allows for rapid prototyping of new and innovative imaging techniques that otherwise take much longer in traditional instrument design. With the aim of maturing science modules for an advanced high contrast imager on an giant segmented mirror telescopes (GSMTs) that will be capable of imaging terrestrial planets, we offer an overview and status update on the various science modules currently under test within the SCExAO instrument.
We overview the current status of photometric analyses of images collected with Multi Conjugate Adaptive Optics (MCAO) at 8-10m class telescopes that operated, or are operating, on sky. Particular attention will be payed to resolved stellar populatio
n studies. Stars in crowded stellar systems, such as globular clusters or in nearby galaxies, are ideal test particles to test AO performance. We will focus the discussion on photometric precision and accuracy reached nowadays. We briefly describe our project on stellar photometry and astrometry of Galactic globular clusters using images taken with GeMS at the Gemini South telescope. We also present the photometry performed with DAOPHOT suite of programs into the crowded regions of these globulars reaching very faint limiting magnitudes Ks ~21.5 mag on moderately large fields of view (~1.5 arcmin squared). We highlight the need for new algorithms to improve the modeling of the complex variation of the Point Spread Function across the field of view. Finally, we outline the role that large samples of stellar standards plays in providing a detailed description of the MCAO performance and in precise and accurate colour{magnitude diagrams.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
