Multi time-step wavefront reconstruction for tomographic adaptive-optics systems

Abstract

In tomographic adaptive-optics (AO) systems, errors due to tomographic wavefront reconstruction limit the performance and angular size of the scientific field of view (FoV), where AO correction is effective. We propose a multi time-step tomographic wavefront reconstruction method to reduce the tomographic error by using measurements from both the current and previous time steps simultaneously. We further outline the method to feed the reconstructor with both wind speed and direction of each turbulence layer. An end-to-end numerical simulation, assuming a multi-object AO (MOAO) system on a 30 m aperture telescope, shows that the multi timestep reconstruction increases the Strehl ratio (SR) over a scientific FoV of 10 arc min in diameter by a factor of 1.5-1.8 when compared to the classical tomographic reconstructor, depending on the guide star asterism and with perfect knowledge of wind speeds and directions. We also evaluate the multi time-step reconstruction method and the wind estimation method on the RAVEN demonstrator under laboratory setting conditions. The wind speeds and directions at multiple atmospheric layers are measured successfully in the laboratory experiment by our wind estimation method with errors below 2 ms −1 . With these wind estimates, the multi time-step reconstructor increases the SR value by a factor of 1.2-1.5, which is consistent with a prediction from the end-to-end numerical simulation.

The statistics of atmospheric turbulence at Maunakea measured by RAVEN

“…Here, we consider the case with two natural guide stars (NGSs) with the angular separation θ, two corresponding SH-WFSs and n layer turbulence layers at different altitudes, as shown as Fig.1. The x-slope of the subaperture indexed as (i, j) of WFS 1 is noted as s x [1] i,j . First, we subtract the temporal average and tip/tilt from a time series of measured slopes.…”

Statistics of turbulence parameters at Maunakea using the multiple wavefront sensor data of RAVEN

“…We also tried to estimate wind speed and direction at each altitude during on-sky observations using a method presented in (Ono et al 2016), and tested new reconstruction algorithms (Correia et al 2015;Ono et al 2016), but in this paper we concentrate on retrieving the vertical profiles of C 2 N (h) and L0(h).…”

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

“…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. On top of that, the PSF is affected by field-dependent static aberrations that must be characterized by using long-exposure phase diversity ( [20]) or focal-plane sharpening ( [16]) for instance, which potentially requires a large amount of telescope time ( [29]).…”