Optical turbulence profiling with Stereo-SCIDAR for VLT and ELT

Osborn, James; Wilson, Richard W.; Sarazin, M.; Butterley, T.; Chacón, A.; Dérie, F.; Farley, O. J. D.; Haubois, X.; Laidlaw, Douglas J.; Lelouarn, M.; Masciadri, E.; Milli, J.; Navarrete, Julio; Townson, Matthew J.

doi:10.1093/mnras/sty1070

Cited by 74 publications

(49 citation statements)

References 32 publications

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“…4. Both the profiles are consistent with long-term statistical analysis (Laidlaw et al 2018): turbulence at the ground is dominant and there are several peaks between 5 and 20 km. Comparing these two profiles, we find that there is a stronger ground layer for the r 0 = 0.0976 profile, while the turbulence layer around 20 km is stronger for the r 0 = 0.171 m profile.…”

Section: Validation Of the Nn Reconstructorsupporting

confidence: 88%

Projected Pupil Plane Pattern (PPPP) with artificial Neural Networks

Yang

González-Gutiérrez

Bharmal

et al. 2019

Monthly Notices of the Royal Astronomical Society

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Focus anisoplanatism is a significant measurement error when using one single laser guide star (LGS) in an Adaptive Optics (AO) system, especially for the next generation of extremely large telescopes. An alternative LGS configuration, called Projected Pupil Plane Pattern (PPPP) solves this problem by launching a collimated laser beam across the full pupil of the telescope. If using a linear, modal reconstructor, the high laser power requirement (∼ 1000 W) renders PPPP uncompetitive with Laser Tomography AO. This work discusses easing the laser power requirements by using an artificial Neural Network (NN) as a non-linear reconstructor. We find that the non-linear NN reduces the required measurement signal-to-noise ratio (SNR) significantly to reduce PPPP laser power requirements to ∼ 200 W for useful residual wavefront error (WFE). At this power level, the WFE becomes 160 nm root mean square (RMS) and 125 nm RMS when r 0 = 0.098 m and 0.171 m respectively for turbulence profiles which are representative of conditions at the ESO Paranal observatory. In addition, it is shown that as a non-linear reconstructor, a NN can perform useful wavefront sensing using a beam-profile from one height as the input instead of the two profiles required as a minimum by the linear reconstructor.

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Section: Validation Of the Nn Reconstructorsupporting

confidence: 88%

Projected Pupil Plane Pattern (PPPP) with artificial Neural Networks

Yang

González-Gutiérrez

Bharmal

et al. 2019

Monthly Notices of the Royal Astronomical Society

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“…We highlight the fact that, for the seeing, the model requires a calibration which make use of turbulence stratification measured in situ [ 20 ]. At present we are calibrating the model using the most recent measurements provided by the new instruments run by ESO at VLT: a new DIMM a and a Stereo-SCIDAR [ 22 ]. It is however not necessary to have a calibrated and optimized version of the model to test the filter effects on the model forecasts.…”

Section: Vlt Testmentioning

confidence: 99%

Evaluation of filtering techniques to increase the reliability of weather forecasts for ground-based telescopes

Turchi

Martelloni

Masciadri

2018

Adaptive Optics Systems VI

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In this contribution we evaluate the impact of filtering techniques in enhancing the accuracy of forecasts of optical turbulence and atmospheric parameters critical for ground-based telescopes. These techniques make use of the data continuously provided by the telescope sensors and instruments to improve the performances of real-time forecasts which have an impact on the telescope operation. In previous works we have already shown how a mesoscale high-frequency forecast (Meso-NH [ 1, 2 ] and Astro-Meso-Nh models [ 3, 4 ]) can produce reliable predictions of different atmospheric parameters [ 5 ] and the optical turbulence [ 4 ]. The mesoscale forecast has an advantage on the global model in having a better implementation of the physical atmospheric processes, including turbulence, and produces an output with greater spatial resolution (up to 100m or beyond). Filtering techniques that make use of the real-time sensor data at the telescope may help in removing potential biases and trends which have an impact on short term mesoscale forecast and, as a consequence, may increase the accuracy of the final output. Given the complexity and cost of present and future top-class telescope installations, each improvement of forecasts of future observing conditions will definitely help in better allocating observing time, especially in queue-mode operation, and will definitely benefit the scientific community in medium-long term.

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“…To extrapolate the PSF across the Field of View (FOV), one must model the spatial variations due to anisoplanatism ( [3,10]), whose model relies on atmospheric parameters such as the C 2 n (h). For single-conjugated AO (SCAO) systems that rely on a single Natural Guide Star (NGS) or a Laser Guide Star (LGS) plus a NGS to measure tip-tilt modes, the anisoplanatism model can not be calibrated from AO control loop data, which necessitates to obtain the C 2 n (h) from external instruments ( [22,5,32]) pointing at different sky directions than the telescope. Discrepancies at a level of 10% up to 20% on the C 2 n (h) estimation compared to AO control loop data-based technique on multiple NGS/LGS systems has been already observed ( [21]), which has an impact of the off-axis PSF model accuracy.…”

Section: Introductionmentioning

confidence: 99%

PRIME: PSF Reconstruction and Identification for Multiple-source characterization Enhancement – application to Keck NIRC2 imager

Beltramo-Martin

Correia

Ragland

et al. 2019

Monthly Notices of the Royal Astronomical Society

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In order to enhance accuracy of astrophysical estimates obtained on Adaptive-optics (AO) images, such as photometry and astrometry, we investigate a new concept to constrain the Point Spread Function (PSF) model called PSF Reconstruction and Identification for Multi-sources characterization Enhancement (PRIME), that handles jointly the science image and the AO control loop data. We present in this paper the concept of PRIME and validate it on Keck II telescope NIRC2 images. We show that by calibrating the PSF model over the scientific image, PSF reconstruction achieves 1% and 3 mas of accuracy on respectively the Strehl-ratio and the PSF full width at half maximum. We show on NIRC2 binary images that PRIME is sufficiently robust to noise to retain photometry and astrometry precision below 0.005 mag and 100µas on a mH = 14 mag object. Finally, we also validate that PRIME performs a PSF calibration on the triple system Gl569BAB which provides a separation of 66.73±1.02 and a differential photometry of 0.538±0.048, compared to the reference values obtained with the extracted PSF which are 66.76 mas ± 0.94 and 0.532 mag ± 0.041. *

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Optical turbulence profiling with Stereo-SCIDAR for VLT and ELT

Cited by 74 publications

References 32 publications

Projected Pupil Plane Pattern (PPPP) with artificial Neural Networks

Projected Pupil Plane Pattern (PPPP) with artificial Neural Networks

Evaluation of filtering techniques to increase the reliability of weather forecasts for ground-based telescopes

PRIME: PSF Reconstruction and Identification for Multiple-source characterization Enhancement – application to Keck NIRC2 imager

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