Making the unmodulated pyramid wavefront sensor smart II. First on-sky demonstration of extreme adaptive optics with deep learning
- URL: http://arxiv.org/abs/2503.16690v1
- Date: Thu, 20 Mar 2025 20:17:30 GMT
- Title: Making the unmodulated pyramid wavefront sensor smart II. First on-sky demonstration of extreme adaptive optics with deep learning
- Authors: R. Landman, S. Y. Haffert, J. D. Long, J. R. Males, L. M. Close, W. B. Foster, K. Van Gorkom, O. Guyon, A. D. Hedglen, P. T. Johnson, M. Y. Kautz, J. K. Kueny, J. Li, J. Liberman, J. Lumbres, E. A. McEwen, A. McLeod, L. Schatz, E. Tonucci, K. Twitchell,
- Abstract summary: Pyramid wavefront sensors (PWFSs) are the preferred choice for current and future extreme adaptive optics (XAO) systems.<n>Almost all instruments use the PWFS in its modulated form to mitigate its limited linearity range.<n>We present the first on-sky demonstration of XAO with an unmodulated PWFS.
- Score: 0.08121379264351831
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Pyramid wavefront sensors (PWFSs) are the preferred choice for current and future extreme adaptive optics (XAO) systems. Almost all instruments use the PWFS in its modulated form to mitigate its limited linearity range. However, this modulation comes at the cost of a reduction in sensitivity, a blindness to petal-piston modes, and a limit to the sensor's ability to operate at high speeds. Therefore, there is strong interest to use the PWFS without modulation, which can be enabled with nonlinear reconstructors. Here, we present the first on-sky demonstration of XAO with an unmodulated PWFS using a nonlinear reconstructor based on convolutional neural networks. We discuss the real-time implementation on the Magellan Adaptive Optics eXtreme (MagAO-X) instrument using the optimized TensorRT framework and show that inference is fast enough to run the control loop at >2 kHz frequencies. Our on-sky results demonstrate a successful closed-loop operation using a model calibrated with internal source data that delivers stable and robust correction under varying conditions. Performance analysis reveals that our smart PWFS achieves nearly the same Strehl ratio as the highly optimized modulated PWFS under favorable conditions on bright stars. Notably, we observe an improvement in performance on a fainter star under the influence of strong winds. These findings confirm the feasibility of using the PWFS in its unmodulated form and highlight its potential for next-generation instruments. Future efforts will focus on achieving even higher control loop frequencies (>3 kHz), optimizing the calibration procedures, and testing its performance on fainter stars, where more gain is expected for the unmodulated PWFS compared to its modulated counterpart.
Related papers
- FADPNet: Frequency-Aware Dual-Path Network for Face Super-Resolution [70.61549422952193]
Face super-resolution (FSR) under limited computational costs remains an open problem.<n>Existing approaches typically treat all facial pixels equally, resulting in suboptimal allocation of computational resources.<n>We propose FADPNet, a Frequency-Aware Dual-Path Network that decomposes facial features into low- and high-frequency components.
arXiv Detail & Related papers (2025-06-17T02:33:42Z) - Rasterizing Wireless Radiance Field via Deformable 2D Gaussian Splatting [10.200300617390013]
Modeling wireless radiance field (WRF) is fundamental to modern communication systems.<n>We propose SwiftWRF, a deformable 2D splatting framework that synthesizes WRF spectra at arbitrary positions.<n>Experiments on both real-world and synthetic indoor scenes demonstrate that SwiftWRF can reconstruct WRF up to 500x faster than existing state-of-the-art methods.
arXiv Detail & Related papers (2025-06-15T09:36:45Z) - FLEX: A Backbone for Diffusion-Based Modeling of Spatio-temporal Physical Systems [51.15230303652732]
FLEX (F Low EXpert) is a backbone architecture for generative modeling of-temporal physical systems.<n>It reduces the variance of the velocity field in the diffusion model, which helps stabilize training.<n>It achieves accurate predictions for super-resolution and forecasting tasks using as few features as two reverse diffusion steps.
arXiv Detail & Related papers (2025-05-23T00:07:59Z) - Fried Parameter Estimation from Single Wavefront Sensor Image with Artificial Neural Networks [0.9883562565157392]
Atmospheric turbulence degrades the quality of astronomical observations in ground-based telescopes, leading to distorted and blurry images.
Adaptive Optics (AO) systems are designed to counteract these effects, using atmospheric measurements captured by a wavefront sensor to make real-time corrections to the incoming wavefront.
The Fried parameter, r0, characterises the strength of atmospheric turbulence and is an essential control parameter for optimising the performance of AO systems.
We develop a novel data-driven approach, adapting machine learning methods from computer vision for Fried parameter estimation from a single Shack-Hartmann or pyramid wavefront sensor image.
arXiv Detail & Related papers (2025-04-23T18:16:07Z) - KFD-NeRF: Rethinking Dynamic NeRF with Kalman Filter [49.85369344101118]
We introduce KFD-NeRF, a novel dynamic neural radiance field integrated with an efficient and high-quality motion reconstruction framework based on Kalman filtering.
Our key idea is to model the dynamic radiance field as a dynamic system whose temporally varying states are estimated based on two sources of knowledge: observations and predictions.
Our KFD-NeRF demonstrates similar or even superior performance within comparable computational time and state-of-the-art view synthesis performance with thorough training.
arXiv Detail & Related papers (2024-07-18T05:48:24Z) - Frequency-Adaptive Dilated Convolution for Semantic Segmentation [14.066404173580864]
We propose three strategies to improve individual phases of dilated convolution from the view of spectrum analysis.
We introduce Frequency-Adaptive Dilated Convolution (FADC), which adjusts dilation rates spatially based on local frequency components.
We design two plug-in modules to directly enhance effective bandwidth and receptive field size.
arXiv Detail & Related papers (2024-03-08T15:00:44Z) - Digital Over-the-Air Federated Learning in Multi-Antenna Systems [30.137208705209627]
We study the performance optimization of federated learning (FL) over a realistic wireless communication system with digital modulation and over-the-air computation (AirComp)
We propose a modified federated averaging (FedAvg) algorithm that combines digital modulation with AirComp to mitigate wireless fading while ensuring the communication efficiency.
An artificial neural network (ANN) is used to estimate the local FL models of all devices and adjust the beamforming matrices at the PS for future model transmission.
arXiv Detail & Related papers (2023-02-04T07:26:06Z) - On Controller Tuning with Time-Varying Bayesian Optimization [74.57758188038375]
We will use time-varying optimization (TVBO) to tune controllers online in changing environments using appropriate prior knowledge on the control objective and its changes.
We propose a novel TVBO strategy using Uncertainty-Injection (UI), which incorporates the assumption of incremental and lasting changes.
Our model outperforms the state-of-the-art method in TVBO, exhibiting reduced regret and fewer unstable parameter configurations.
arXiv Detail & Related papers (2022-07-22T14:54:13Z) - FAMLP: A Frequency-Aware MLP-Like Architecture For Domain Generalization [73.41395947275473]
We propose a novel frequency-aware architecture, in which the domain-specific features are filtered out in the transformed frequency domain.
Experiments on three benchmarks demonstrate significant performance, outperforming the state-of-the-art methods by a margin of 3%, 4% and 9%, respectively.
arXiv Detail & Related papers (2022-03-24T07:26:29Z) - Functional Regularization for Reinforcement Learning via Learned Fourier
Features [98.90474131452588]
We propose a simple architecture for deep reinforcement learning by embedding inputs into a learned Fourier basis.
We show that it improves the sample efficiency of both state-based and image-based RL.
arXiv Detail & Related papers (2021-12-06T18:59:52Z) - Learning OFDM Waveforms with PAPR and ACLR Constraints [15.423422040627331]
We propose a learning-based method to design OFDM-based waveforms that satisfy selected constraints while maximizing an achievable information rate.
We show that the end-to-end system is able to satisfy target PAPR and ACLR constraints and allows significant throughput gains.
arXiv Detail & Related papers (2021-10-21T08:58:59Z) - Neural Calibration for Scalable Beamforming in FDD Massive MIMO with
Implicit Channel Estimation [10.775558382613077]
Channel estimation and beamforming play critical roles in frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems.
We propose a deep learning-based approach that directly optimize the beamformers at the base station according to the received uplink pilots.
A neural calibration method is proposed to improve the scalability of the end-to-end design.
arXiv Detail & Related papers (2021-08-03T14:26:14Z) - DUT-LFSaliency: Versatile Dataset and Light Field-to-RGB Saliency
Detection [104.50425501764806]
We introduce a large-scale dataset to enable versatile applications for light field saliency detection.
We present an asymmetrical two-stream model consisting of the Focal stream and RGB stream.
Experiments demonstrate that our Focal stream achieves state-of-the-arts performance.
arXiv Detail & Related papers (2020-12-30T11:53:27Z) - Pushing the Envelope of Rotation Averaging for Visual SLAM [69.7375052440794]
We propose a novel optimization backbone for visual SLAM systems.
We leverage averaging to improve the accuracy, efficiency and robustness of conventional monocular SLAM systems.
Our approach can exhibit up to 10x faster with comparable accuracy against the state-art on public benchmarks.
arXiv Detail & Related papers (2020-11-02T18:02:26Z) - Optimization strategies for modulation transfer spectroscopy applied to
laser stabilization [0.0]
We present a general analysis for determining the optimal modulation parameters for the modulation transfer spectroscopy scheme.
A signal with optimized slope and amplitude is predicted for a large modulation index $M$ and a modulation frequency comparable to the natural linewidth of the spectroscopic transition.
An optimized signal for spectroscopy of the rubidium D2 line is presented.
arXiv Detail & Related papers (2020-03-26T17:04:14Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.