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Measurement problems in the statistical identification of the scale of cosmological distances.

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Název: Measurement problems in the statistical identification of the scale of cosmological distances.
Autoři: Levin, S.
Zdroj: Measurement Techniques; Mar2012, Vol. 54 Issue 12, p1334-1341, 8p
Témata: METROLOGY, ELECTRONIC data processing, COMPUTER software, COMPUTERS in education, RADIO galaxies, GALAXIES, RADIO sources (Astronomy)
Abstrakt: Problems of statistical data processing that are general problems from the standpoint of metrology are considered for the Hubble diagram as the basis of the scale of cosmological distances. Results of an identification of the diagram for radio galaxies and quasars on the basis of a rigorous solution of Mattig's equation are presented. [ABSTRACT FROM AUTHOR]
Copyright of Measurement Techniques is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Measurement problems in the statistical identification of the scale of cosmological distances.
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  Data: Problems of statistical data processing that are general problems from the standpoint of metrology are considered for the Hubble diagram as the basis of the scale of cosmological distances. Results of an identification of the diagram for radio galaxies and quasars on the basis of a rigorous solution of Mattig's equation are presented. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Measurement Techniques is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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