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| 001 | 874406 | ||
| 005 | 20210130004652.0 | ||
| 024 | 7 | _ | |a 2128/24505 |2 Handle |
| 037 | _ | _ | |a FZJ-2020-01416 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Drabent, Alexander |0 P:(DE-HGF)0 |b 0 |
| 111 | 2 | _ | |a NIC Symposium 2020 |c Jülich |d 2020-02-27 - 2020-02-28 |w Germany |
| 245 | _ | _ | |a Realising the LOFAR Two-Metre Sky Survey |
| 260 | _ | _ | |a Jülich |c 2020 |b Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag |
| 295 | 1 | 0 | |a NIC Symposium 2020 |
| 300 | _ | _ | |a 113 - 122 |
| 336 | 7 | _ | |a CONFERENCE_PAPER |2 ORCID |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
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| 490 | 0 | _ | |a Publication Series of the John von Neumann Institute for Computing (NIC) NIC Series |v 50 |
| 520 | _ | _ | |a The new generation of high-resolution broad-band radio telescopes, like the Low Frequency Array (LOFAR), produces, depending on the level of compression, between 1 to 10 TB of data per hour after correlation. Such a large amount of scientific data demand powerful computing resources and efficient data handling strategies to be mastered. The LOFAR Two-metre Sky Survey (LoTSS) is a Key Science Project (KSP) of the LOFAR telescope. It aims to map the entire northern hemisphere at unprecedented sensitivity and resolution. The survey consist of 3 168 pointings, requiring about 30 PBytes of storage space. As a member of the German Long Wavelength Consortium (GLOW) the Forschungszentrum Jülich (FZJ) stores in the Long Term Archive (LTA) about 50% of all LoTSS observations conducted to date. In collaboration with SURFsara in Amsterdam we developed service tools that enables the KSP to process LOFAR data stored in the LTA at the Jülich Supercomputing Centre (JSC) in an automated and robust fashion. Through our system more than 500 out of 800 existing LoTSS observationshave already been processed with the prefactor pipeline. This pipeline calibrates the direction-independent instrumental and ionospheric effects and furthermore reduces the data size significantly. For continuum imaging, this processing pipeline is the standard pipeline that is executed before more advanced processing and image reconstruction methods are applied. |
| 536 | _ | _ | |a 513 - Supercomputer Facility (POF3-513) |0 G:(DE-HGF)POF3-513 |c POF3-513 |f POF III |x 0 |
| 700 | 1 | _ | |a Hoeft, Matthias |0 P:(DE-HGF)0 |b 1 |e Corresponding author |
| 700 | 1 | _ | |a Mechev, Alex B. |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Oonk, J. B. Raymond |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Shimwell, Timothy W. |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Sweijen, Frits |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Danezi, Anatoli |0 P:(DE-HGF)0 |b 6 |
| 700 | 1 | _ | |a Schrijvers, Coen |0 P:(DE-HGF)0 |b 7 |
| 700 | 1 | _ | |a Manzano, Cristina |0 P:(DE-Juel1)132183 |b 8 |
| 700 | 1 | _ | |a Tsigenov, Oleg |0 P:(DE-Juel1)167207 |b 9 |
| 700 | 1 | _ | |a Dettmar, Ralf-Jürgen |0 P:(DE-HGF)0 |b 10 |
| 700 | 1 | _ | |a Brüggen, Marcus |0 P:(DE-HGF)0 |b 11 |
| 700 | 1 | _ | |a Schwarz, Dominik J. |0 P:(DE-HGF)0 |b 12 |
| 787 | 0 | _ | |i IsPartOf |0 FZJ-2020-01353 |
| 856 | 4 | _ | |y OpenAccess |u https://juser.fz-juelich.de/record/874406/files/NIC_2020_Schwarz.pdf |
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