{"title":"Thermal modelling with neural network applied to Planck space mission","authors":"C. Leroy, J. Bernard, J. Trouilhet","doi":"10.1109/NNSP.2003.1318014","DOIUrl":null,"url":null,"abstract":"The European Space Agency Planck satellite will be launched in 2007. The goal of this mission is to perform a complete survey of the cosmic microwave background. The high frequency instrument (HFI) on-board Planck would perform all-sky mapping at sub-millimetre and millimetre wavelengths using bolometers cooled at very low temperatures. We have developed a new method able to predict precisely the thermal behaviour of the instrument in order to extract instrumental additive signals due to self-emission by the various cryogenic stages. This article presents a synthesis of the results obtained with neural methods for this modelling problem.","PeriodicalId":315958,"journal":{"name":"2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NNSP.2003.1318014","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The European Space Agency Planck satellite will be launched in 2007. The goal of this mission is to perform a complete survey of the cosmic microwave background. The high frequency instrument (HFI) on-board Planck would perform all-sky mapping at sub-millimetre and millimetre wavelengths using bolometers cooled at very low temperatures. We have developed a new method able to predict precisely the thermal behaviour of the instrument in order to extract instrumental additive signals due to self-emission by the various cryogenic stages. This article presents a synthesis of the results obtained with neural methods for this modelling problem.
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