Spatial Mapping of Kilopixel Kinetic Inductance Detector Arrays for PRIMA
MILLIMETER, SUBMILLIMETER, AND FAR-INFRARED DETECTORS AND INSTRUMENTATION FOR ASTRONOMY XII, PT 1（2024）
CALTECH
Abstract
We present a characterization of the mapping from resonant frequency to spatial position for a kilopixel kinetic inductance detector (KID) array developed for the Probe far-Infrared Mission for Astrophysics (PRIMA). This work targets the longest wavelength band of PRIMA's FIRESS spectrometer, which in total spans 24 to 235 mu m. Light emitting diodes arrayed to match repeating unit cells of 16 KIDs first discriminate among unit cells. Within each unit cell, frequencies are widely spaced, so positions are discriminated by theoretical predictions of the relative frequency spacing between detectors based on KID geometries. With this mapping, we analyze board features to improve the accuracy of modeling PRIMA KIDs and inform future fabrication runs.
MoreTranslated text
Key words
Kinetic Inductance Detectors,Far-Infrared,PRIMA,Multiplexed readout,Radio frequency system on a chip,RFSoC
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