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تسجيل مستخدم جديدTowards a reliability assessment of the Cn2 and wind speed vertical profiles retrieved from GeMS
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The advent of a new generation of Adaptive Optics systems called Wide Field AO (WFAO) mark the beginning of a new era. By using multiple Guide Stars (GSs), either Laser Guide Stars (LGSs) or Natural Guide Stars (NGSs), WFAO significantly increases the field of view of the AO-corrected images, and the fraction of the sky that can benefit from such correction. Different typologies of WFAO have been studied over the past years. They all require multiple GSs to perform a tomographic analysis of the atmospheric turbulence. One of the fundamental aspects of the new WFAO systems is the knowledge of the spatio-temporal distribution of the turbulence above the telescope. One way to get to this information is to use the telemetry data provided by the WFAO system itself. Indeed, it has been demonstrated that WFAO systems allows one to derive the Cn2 and wind profile in the main turbulence layers (see e.g. Cortes et al. 2012). This method has the evident advantage to provide information on the turbulence stratification that effectively affects the AO system, property more difficultly respected by independently vertical profilers. In this paper, we compare the wind speeds profiles of GeMS with those predicted by a non-hydrostatical mesoscale atmospherical model (Meso-NH). It has been proved (Masciadri et al., 2013), indeed, that this model is able to provide reliable wind speed profiles on the whole troposphere and stratosphere (up to 20-25 km) above top-level astronomical sites. Correlation with measurements revealed to be very satisfactory when the model performances are analyzed from a statistical point of view as well on individual nights. Such a system appears therefore as an interesting reference to be used to quantify the GeMS wind speed profiles reliability.
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Wide Field Adaptive Optics (WFAO) systems represent the more sophisticated AO systems available today at large telescopes. A critical aspect for these WFAO systems in order to deliver an optimised performance is the knowledge of the vertical spatiote
mporal distribution of the CN2 and the wind speed. Previous studies (Cortes et al., 2012) already proved the ability of GeMS (the Gemini Multi-Conjugated AO system) in retrieving CN2 and wind vertical stratification using the telemetry data. To assess the reliability of the GeMS wind speed estimates a preliminary study (Neichel et al., 2014) compared wind speed retrieved from GeMS with that obtained with the atmospherical model Meso-Nh on a small sample of nights providing promising results. The latter technique is very reliable for the wind speed vertical stratification. The model outputs gave, indeed, an excellent agreement with a large sample of radiosoundings (~ 50) both in statistical terms and on individual flights (Masciadri et al., 2013). Such a tool can therefore be used as a valuable reference in this exercise of cross calibrating GeMS on-sky wind estimates with model predictions. In this contribution we achieved a two-fold results: (1) we extended analysis on a much richer statistical sample (~ 43 nights), we confirmed the preliminary results and we found an even better correlation between GeMS observations and the atmospherical model with basically no cases of not-negligible uncertainties; (2) we evaluate the possibility to use, as an input for GeMS, the Meso-Nh estimates of the wind speed stratification in an operational configuration. Under this configuration these estimates can be provided many hours in advanced with respect to the observations and with a very high temporal frequency (order of 2 minutes or less).
Wide Field Adaptive Optics (WFAO) systems are among the most sophisticated AO systems available today on large telescopes. The knowledge of the vertical spatio-temporal distribution of the wind speed (WS) and direction (WD) are fundamental to optimiz
e the performance of such systems. Previous studies already proved that the Gemini Multi-Conjugated AO system (GeMS) is able to retrieve measurements of the WS and WD stratification using the SLODAR technique and to store measurements in the telemetry data. In order to assess the reliability of these estimates and of the SLODAR technique applied to such a kind of complex AO systems, in this study we compared WS and WD retrieved from GeMS with those obtained with the atmospherical model Meso-Nh on a rich statistical sample of nights. It has been previously proved that, the latter technique, provided an excellent agreement with a large sample of radiosoundings both, in statistical terms and on individual flights. It can be considered, therefore, as an independent reference. The excellent agreement between GeMS measurements and the model that we find in this study, proves the robustness of the SLODAR approach. To by-pass the complex procedures necessary to achieve automatic measurements of the wind with GeMS, we propose a simple automatic method to monitor nightly WS and WD using the Meso-Nh model estimates. Such a method can be applied to whatever present or new generation facilities supported by WFAO systems. The interest of this study is, therefore, well beyond the optimization of GeMS performance.
We present a new approach to automate the spectroscopic redshift reliability assessment based on machine learning (ML) and characteristics of the redshift probability density function (PDF). We propose to rephrase the spectroscopic redshift estimat
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We use spatio-temporal cross-correlations of slopes from five Shack-Hartmann wavefront sensors to analyse the temporal evolution of the atmospheric turbulence layers at different altitudes. The focus is on the verification of the frozen flow assumpti
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