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Register a new userOn the comparison between MASS and G-SCIDAR techniques
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Elena Masciadri Dr.
Publication date
2013
fields
Physics
and research's language is
English
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The Multi Aperture Scintillation Sensor (MASS) and the Generalized-Scintillation Detection and Ranging (Generalized SCIDAR) are two instruments conceived to measure the optical turbulence (OT) vertical distribution on the whole troposphere and low stratosphere (~ 20 km) widely used in the astronomical context. In this paper we perform a detailed analysis/comparison of measurements provided by the two instruments and taken during the extended site testing campaign carried out on 2007 at Cerro Paranal and promoted by the European Southern Observatory (ESO). The main and final goal of the study is to provide a detailed estimation of the measurements reliability i.e dispersion of turbulence measurements done by the two instruments at different heights above the ground. This information is directly related to our ability in estimating the absolute value of the turbulence stratification. To better analyse the uncertainties between the MASS and the GS we took advantage of the availability of measurements taken during the same campaign by a third independent instrument (DIMM - Differential Imaging Motion Monitor) measuring the integrated turbulence extended on the whole 20 km. Such a cross-check comparison permitted us to define the reliability of the instruments and their measurements, their limits and the contexts in which their use can present some risk.
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We present the largest database so far of atmospheric optical-turbulence profiles (197035 individual CN2(h)) for an astronomical site, the Roque de los Muchachos Observatory (La Palma, Spain). This C2 (h) database was obtained through generalized-SCIDAR observations at the 1 meter Jacobus Kapteyn telescope from Febrary 2004 to August 2009, obtaining useful data for 211 nights. The overestimation of the turbulence strength induced during the generalized SCIDAR data processing has been analyzed for the different observational configurations. All the individual C2 (h) have been recalibrated to compensate the introduced errors during data treatment following (Avila & Cuevas 2009). Comparing results from profiles before and after the recalibration, we analyze its impact on the calculation of relevant parameters for adaptive optics.
The efficiency of the management of top-class ground-based astronomical facilities supported by Adaptive Optics (AO) relies on our ability to forecast the optical turbulence (OT) and a set of relevant atmospheric parameters. Indeed, in spite of the fact that the AO is able to achieve, at present, excellent levels of wavefront corrections (a Strehl Ratio up to 90% in H band), its performances strongly depend on the atmospheric conditions. Knowing in advance the turbulence conditions allows an optimization of the AO use. It has already been proven that it is possible to provide reliable forecasts of the optical turbulence (CN2 profiles and integrated astroclimatic parameters such as seeing, isoplanantic angle, wavefront coherence time, ...) for the next night. In this paper we prove that it is possible to improve the forecast performances on shorter time scales (order of one or two hours) with consistent gains (order of 2 to 8) using filtering techniques. This has permitted us to achieve forecasts accuracies never obtained before and reach a fundamental milestone for the astronomical applications. The time scale of one or two hours is the most critical one for an efficient management of the ground-based telescopes supported by AO. Results shown here open, therefore, to an important revolution in the field. We implemented this method in the operational forecast system of the Large Binocular Telescope, named ALTA Center that is, at our knowledge, the first operational system providing forecasts of turbulence and atmospheric parameters at short time scales to support science operations.
A Single Star Scidar system(SSS) has been developed for remotely sensing atmospheric turbulence profiles. The SSS consists of computing the spatial auto/cross-correlation functions of short exposure images of the scintillation patterns produced by a single star, and provides the vertical profiles of optical turbulence intensity C2n(h) and wind speed V(h). The SSS needs only a 40 cm aperture telescope, so that can be portable and equipped easily to field candidate sites. Some experiments for the SSS have been made in Beijing last year, successfully retrieving atmospheric turbulence and wind profiles from the ground to 30 km. The SSS observations has recently been made at the Xinglong station of NAOC, characterizing atmospheric parameters at this station. We plan to automatize SSS instrument and run remote observation via internet; a more friendly auto-SSS system will be set up and make use at the candidate sites in Tibet and Dome A.
Knowledge of the Earths atmospheric optical turbulence is critical for astronomical instrumentation. Not only does it enable performance verification and optimisation of existing systems but it is required for the design of future instruments. As a minimum this includes integrated astro-atmospheric parameters such as seeing, coherence time and isoplanatic angle, but for more sophisticated systems such as wide field adaptive optics enabled instrumentation the vertical structure of the turbulence is also required. Stereo-SCIDAR is a technique specifically designed to characterise the Earths atmospheric turbulence with high altitude resolution and high sensitivity. Together with ESO, Durham University has commissioned a Stereo-SCIDAR instrument at Cerro Paranal, Chile, the site of the Very Large Telescope (VLT), and only 20~km from the site of the future Extremely Large Telescope (ELT). Here we provide results from the first 18 months of operation at ESO Paranal including instrument comparisons and atmospheric statistics. Based on a sample of 83 nights spread over 22 months covering all seasons, we find the median seeing to be 0.64 with 50% of the turbulence confined to an altitude below 2 km and 40% below 600 m. The median coherence time and isoplanatic angle are found as 4.18 ms and 1.75 respectively. A substantial campaign of inter-instrument comparison was also undertaken to assure the validity of the data. The Stereo-SCIDAR profiles (optical turbulence strength and velocity as a function of altitude) have been compared with the Surface-Layer SLODAR, MASS-DIMM and the ECMWF weather forecast model. The correlation coefficients are between 0.61 (isoplanatic angle) and 0.84 (seeing).
Future radio surveys will generate catalogues of tens of millions of radio sources, for which redshift estimates will be essential to achieve many of the science goals. However, spectroscopic data will be available for only a small fraction of these sources, and in most cases even the optical and infrared photometry will be of limited quality. Furthermore, radio sources tend to be at higher redshift than most optical sources and so a significant fraction of radio sources hosts differ from those for which most photometric redshift templates are designed. We therefore need to develop new techniques for estimating the redshifts of radio sources. As a starting point in this process, we evaluate a number of machine-learning techniques for estimating redshift, together with a conventional template-fitting technique. We pay special attention to how the performance is affected by the incompleteness of the training sample and by sparseness of the parameter space or by limited availability of ancillary multi-wavelength data. As expected, we find that the quality of the photometric-redshift degrades as the quality of the photometry decreases, but that even with the limited quality of photometry available for all sky-surveys, useful redshift information is available for the majority of sources, particularly at low redshift. We find that a template-fitting technique performs best with high-quality and almost complete multi-band photometry, especially if radio sources that are also X-ray emitting are treated separately. When we reduced the quality of photometry to match that available for the EMU all-sky radio survey, the quality of the template-fitting degraded and became comparable to some of the machine learning methods. Machine learning techniques currently perform better at low redshift than at high redshift, because of incompleteness of the currently available training data at high redshifts.
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