Siddarayappa Bikkannavar

Demonstration of on Sky Contrast Improvement Using the Modified Gerchberg-Saxton Algorithm at the Palomar Observatory

We have successfully demonstrated significant improvements in the high contrast detection limit of the Well-Corrected Subaperture (WCS) using a number of steps aimed at reducing non-common path (NCP) wavefront errors, including the Autonomous Phase Retrieval Calibration (APRC)1 software package developed at the Jet Propulsion Laboratory (JPL) for the Palomar adaptive optics instrument (PALAO). APRC utilizes the Modified Gerchberg-Saxton (MGS) wavefront sensing algorithm, also developed at JPL2. The WCS delivers such excellent correction of the atmosphere that NCP wavefront errors not sensed by PALAO but present at the coronagraphic image plane begin to factor heavily as a limit to contrast. The APRC program was implemented to reduce these NCP wavefront errors from 110 nm to 35 nm (rms) in the lab, and now these exceptional results have been extended to targets on the sky for the first time, leading to a significant suppression of speckle noise. Consequently we now report a contrast level of very nearly 1×10-4 at separations of 2λ/D before the data is post processed, and 1×10-5 after post processing. We describe here the major components of our instrument, the work done to improve the NCP wavefront errors, and the ensuing excellent on sky results, including the detection of the three exoplanets orbiting the star HR8799.

Autonomous phase retrieval control for calibration of the Palomar Adaptive Optics system

An autonomous wavefront sensing and control software suite (APRC) has been developed as a method to calibrate the internal static errors in the Palomar Adaptive Optics system. An image-based wavefront sensing algorithm, Adaptive Modified Gerchberg-Saxton Phase Retrieval (MGS), provides wavefront error knowledge upon which actuator command voltages are calculated for iterative wavefront control corrections. This automated, precise calibration eliminates non-common path error to significantly reduce AO system internal error to the controllable limit of existing hardware, or can be commanded to prescribed polynomials to facilitate high contrast astronomy. System diagnostics may be performed through analysis of the wavefront result generated by the phase retrieval software.

Performance of the optical communication adaptive optics testbed

We describe the current performance of an adaptive optics testbed for free space optical communication. This adaptive optics system allows for simulation of night and day-time observing on a 1 meter telescope with a 97 actuator deformable mirror. In lab-generated seeing of 2.1 arcseconds (at 0.5μm) the system achieves a Strehl of 21% at 1.064μm (210nm RMS wavefront). Predictions of the systems performance based on real-time wavefront sensor telemetry data and analytical equations are shown to agree with the observed image performance. We present experimentally measured gains in communications performance of 2-4dB in the received signal power when AO correction is applied in the presence of high background and turbulence at an uncoded bit error rate of 0.1. The data source was a 100Mbps on-offkeyed signal detected with an IR-enhanced avalanche photodiode detector as the receiver.

Phase retrieval methods for wavefront sensing

Phase retrieval is an image-based wavefront sensing process, used to recover phase information from defocused stellar images. Phase retrieval has proven to be useful for diagnosis of optical aberrations in space telescopes, calibration of adaptive optics systems, and is intended for use in aligning and phasing the James Webb Space Telescope. This paper describes a robust and accurate phase retrieval algorithm for wavefront sensing, which has been successfully demonstrated on a variety of testbeds and telescopes. Key features, such as image preprocessing, diversity adaptation, and prior phase nulling, are described and compared to other methods. Results demonstrate high accuracy and high dynamic range wavefront sensing.

Advanced DFS: a dispersed fringe sensing algorithm insensitive to small calibration errors

Dispersed Fringe Sensing (DFS) is an elegant method of coarse phasing segmented mirrors. DFS performance accuracy is dependent upon careful calibration of the system as well as other factors such as internal optical alignment, system wavefront errors, and detector quality. Novel improvements to the algorithm have led to substantial enhancements in DFS performance. In this paper, we present Advanced DFS, an advancement of the DFS algorithm, which allows the overall method to be less sensitive to calibration errors. This is achieved by correcting for calibration errors, which appear in the fitting equations as a signal phase term. This paper will outline a brief analytical explanation of the improvements, results of advanced DFS processed simulations and experimental advanced DFS results.

Experimental Validation of Advanced Dispersed Fringe Sensing (ADFS) Algorithm Using Advanced Wavefront Sensing and Correction Testbed (AWCT)

Large aperture telescope commonly features segment mirrors and a coarse phasing step is needed to bring these individual segments into the fine phasing capture range. Dispersed Fringe Sensing (DFS) is a powerful coarse phasing technique and its alteration is currently being used for JWST. An Advanced Dispersed Fringe Sensing (ADFS) algorithm is recently developed to improve the performance and robustness of previous DFS algorithms with better accuracy and unique solution. The first part of the paper introduces the basic ideas and the essential features of the ADFS algorithm and presents the some algorithm sensitivity study results. The second part of the paper describes the full details of algorithm validation process through the advanced wavefront sensing and correction testbed (AWCT): first, the optimization of the DFS hardware of AWCT to ensure the data accuracy and reliability is illustrated. Then, a few carefully designed algorithm validation experiments are implemented, and the corresponding data analysis results are shown. Finally the fiducial calibration using Range-Gate-Metrology technique is carried out and a <10nm or <1% algorithm accuracy is demonstrated.