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Analysis of the frozen flow assumption using GeMS telemetry data

Analysis of the frozen flow assumption using GeMS telemetry data Angela Cortés 1 , Alexander Rudy 2 , Benoit Neichel 3 , Lisa Poyneer 4 , Mark Ammons 4 , Andres Guesalaga 1 (1) Pontificia Universidad Católica de Chile, Santiago, Chile (2) University of California Santa Cruz

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Analysis of the frozen flow assumption using GeMS telemetry data

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  1. Analysis of the frozen flow assumption using GeMS telemetry data Angela Cortés1, Alexander Rudy2, Benoit Neichel3, Lisa Poyneer4, Mark Ammons4, Andres Guesalaga1 (1) Pontificia Universidad Católica de Chile, Santiago, Chile (2) University of California Santa Cruz (3) Gemini Observatory Southern Operations Center, La Serena, Chile (4) Lawrence Livermore National Laboratory

  2. Analysis of the frozen flow assumption using GeMS telemetry data • Goal: • Use telemetry data from the GeMS to study the validity of the frozen flow hypothesis using two types of algorithms: • Spatio-temporal cross-correlations of 5 GeMS laser guidestars WFS • The Fourier Wind Identification (FWI) • Results: • Number of layers present and their associated velocities. • Estimation of their altitude and strength (turbulence profiler) • Rate of de-correlation (how frozen is the flow?)

  3. ← 60 arcseconds → 0 km 4.5 km 2 1 9 km 0 ← 84.9 arcseconds → ← 42.4 arcsec → 5 4 Gemini-South MCAO System (GeMS) • 16x16 grid Shack-Hartmann • 204 active subapertures (total: 1020) • sampling rate= < 800 Hz 5 WFSs • 917 actuators in total • 684 valid actuators (seen by the WFSs) • 233 extrapolated actuators 3 DMs

  4. Turbulence profiling using GeMS telemetry data • (Cortés et al, MNRAS 2012) Problemswith SLODAR dueto dome seeing Variance in subapertures, Y direction Solution: spatio-temporal correlations Poster - N: 16162 :Performance of two turbulence profilers for a MCAO system under strong dome seeing condition

  5. The “windprofiler” orspatio-temporal correlations For T = 0 s, the turbulence profile in altitude is extracted from the baseline w = 17.7 m/s αw = 227.7° For T > 0, the layers present can be detected and their velocity estimated w = 8.8 m/s αw = 187.1°

  6. Next, we analyze 4 cases of Frozen Flow: • Dome Turbulence • Ground Layer Turbulence • Mid Altitude Turbulence • High Altitude Turbulence

  7. 1. Frozen flow for turbulence inside the dome Ground layer Decay in correlation for dome turbulence Dome Wind speed = 0.0 m/s By applying this method, an estimate of the dome seeing can be obtained at any time!

  8. 2. Frozen flow for turbulence at the ground layer wind speed = 8.8 m/s wind direction = 187.1° Decay in correlation for ground layer turbulence

  9. 3. Frozen flow for turbulence at mid-altitude (~ 4 Km) Wind speed = 10.0 m/s Wind direction = 172.9° Decay in correlation for mid-altitude turbulence

  10. 4. Frozen flow for turbulence at high-altitude (~ 12 Km) Wind speed = 21.3 m/s Wind direction = 227.7° Decay in correlation for high-altitude turbulence Wind speed=17.7 m/s Wind direction = 227.7°

  11. Dependence of frozen flow to wind speed Decay in correlation w = 10.0 m/s |m|= 1.66 s-1 w = 8.8 m/s |m|= 1.33 s-1 w = 0.0 m/s |m|= 0.32 s-1 w = 17.7m/s |m|= 3.26 s-1 Absolute rate of fading, |m| vs. wind speed Linear or just a coincidence ? 1 ~ 6 m More data is required to verify this!!

  12. Fourier Wind Identification Fourier Modes “Blow” in Wind by Use Fourier Modes in Space and Time to find Frozen Flow

  13. Fourier Wind Identification Transform Open-Loop Phase into Fourier Modes GeMS Telemetry Fourier Modes Pseudo-Open Loop Phase Spatial and Temporal Fourier Modes (Same as Angela’s data)

  14. Fourier Wind Identification Then Fit Temporal Fourier Peaks to Frozen-Flow Layers Find Peaks in Temporal Space Match Found Peaks to Layer Templates

  15. Fourier Wind Identification FWI Finds Frozen Flow Layers in GeMS Pseudo Open Loop Data Single Layer Identified (plus a weak second layer) 2 Layers Identified

  16. Fourier Wind Identification Wind Vector Remains Constant when Examined over Longer Times Time

  17. Fourier Wind Identification Analysis of Longer Telemetry Intervals Improves Overall Signal

  18. Spatial-temporal correlation and Fourier Wind Identification agree Wind speed=17.7 m/s Wind direction = 227.7°

  19. Conclusions • Both Methods Detect Frozen Flow Turbulence • The short-timescale Spatial Temporal Correlation complements the long timescale Fourier Wind Identification • Both methods makes no assumption of Kolmogorov Turbulence. Fourier Wind Identification Spatial-Temporal Correlation • Frozen flow exists and its melting rate is proportional to the wind speed. • The method provides an estimate of the dome seeing. • Tracking correlation peaks is a major problem. • Non-Frozen Flow Turbulence is automatically rejected. • No suppression of static modes, DC terms are fit like any other turbulence.

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