WeChat Mini Program
Old Version Features

In-flight Performance of the XRISM/Resolve Detector System

SPACE TELESCOPES AND INSTRUMENTATION 2024 ULTRAVIOLET TO GAMMA RAY, PT 1(2024)

NASA

Cited 0|Views1
Abstract
The Resolve instrument was launched on-board the XRISM observatory in early September 2023. The Resolve spectrometer is based on a high-sensitivity X-ray calorimeter detector system that has been successfully deployed in many ground and sub-orbital spectrometers. However, the Resolve instrument will be the first long-term implementation in space. The instrument will provide essential diagnostics for nearly every class of X-ray emitting objects, from the atmosphere of Jupiter to the outskirts of galaxy clusters, without degradation for spatially extended objects. The Resolve detector system consists of a 36-pixel microcalorimeter array operated at a heat-sink temperature of 50mK. In pre-flight testing, the detector system demonstrated a resolving power of better than 1300 at 6 keV with a simultaneous bandpass from below 0.3 keV to above 12 keV and a timing precision better than 100 mu s. An anti-coincidence detector placed directly behind the microcalorimeter array effectively suppresses background. The detector energy-resolution budget included terms for interference from the Resolve cooling system and the spacecraft. Additional terms for energy-scale stability, on-orbit effects, and use of mid-grade events were also included, predicting an end-of-life, on-orbit performance for high and mid-grade events that meets the requirement of 7 eV FWHM at 6 keV. Here we discuss the actual on-orbit performance of the Resolve detector system and compare this to performance in pre-flight testing, on-orbit predictions, and the almost identical Hitomi/SXS instrument. We will also discuss the on-orbit gain stability, any additional on-orbit interference, and measurements of the on-orbit background.
More
Translated text
Key words
X-ray,spectroscopy,detectors,cryogenic
求助PDF
上传PDF
Bibtex
收藏
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