Isgri: the Integral Soft Gamma-Ray Imager

F Lebrun,JP Leray,P Lavocat,J Cretolle,M Arques,C Blondel, C Bonnin,A Bouere,C Cara, T Chaleil,F Daly,F Desages,H Dzitko,B Horeau,P Laurent,O Limousin,F Mathy, V Mauguen, F Meignier,F Molinie,E Poindron,M Rouger,A Sauvageon,T Tourrette

Astronomy and Astrophysics（2003）SCI 2区

CEA Saclay | CEA-Saclay DSM/DAPNIA/Service d'Astrophysique | CEA-Grenoble | CEA-Saclay DSM/DAPNIA/SIS | CEA-Saclay DSM/DAPNIA/SEDI

Cited 721|Views59

Abstract

For the first time in the history of high energy astronomy, a large CdTe gamma-ray camera is operating in space. ISGRI is the low-energy camera of the IBIS telescope on board the INTEGRAL satellite. This paper details its design and its in-flight behavior and performances. Having a sensitive area of 2621 cm(2) with a spatial resolution of 4.6 mm, a low threshold around 12 keV and an energy resolution of similar to8% at 60 keV, ISGRI shows absolutely no signs of degradation after 9 months in orbit. All aspects of its in-flight behavior and scientific performance are fully nominal, and in particular the observed background level confirms the expected sensitivity of 1 milliCrab for a 10(6) s observation.

Translated text

Key words

space telescope,cadmium telluride detectors,gamma-ray astronomy,calibration,INTEGRAL,IBIS

Bibtex

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

Try using models to generate summary,it takes about 60s

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper