NAOS Visible Wavefront Sensor
Astronomical Telescopes and Instrumentation(2000)
Abstract
This paper describes the Visible Wave Front Sensor (visible WFS) for the VLT Nasmith Adaptive Optics system (NAOS). This Shack-Hartman-based wave front sensor instrument includes within a continuous flow liquid nitrogen cryostat: (1) a low noise fast readout CCD camera controlled by the ESO new generation CCD controller FIERA. The readout noise of this system is 3 e- at 50 kilopixel/sec/port, and is only limited by the CCD intrinsic noise. FIERA proposes remotely controlled readout modes with optional binning, windowing and flexible integration time. (2) two remotely exchangeable micro-lens arrays focusing the analyzed wave front directly on the CCD sensitive surface. The wave front sensor includes also its own atmospheric dispersion compensator. Due to the continuous rotation of the NAOS adapter, the mechanical stiffness of the visible wave front sensor must be very high not to disturb the loop operation (no more than 0.1 micrometer of lenslet array displacement compared to the CCD location over a 30 degree rotation angle of the instrument). The following simulations and tests are described: (1) simulation results providing an estimation of the NAOS maximum operating magnitude, (2) camera optimization, (3) mechanical stiffness measurements.
MoreTranslated text
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined