Data

Parkes observations for project P1072 semester 2020OCTS_01

Commonwealth Scientific and Industrial Research Organisation
Hobbs, George ; Dawson, Joanne ; Norris, Ray ; Wong, Ivy ; Huynh, Minh ; Li, Di ; Galvin, Tim ; Dai, Shi ; Wang, Chen ; Zhang, Songbo ; Zhu, Weiwei ; Farajollahi, Hossein ; Luo, Rui ; Wang, Rosalind ; Petersson, Lars ; Rolland, Vivien
Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]
ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25919/7jaa-ah53&rft.title=Parkes observations for project P1072 semester 2020OCTS_01&rft.identifier=https://doi.org/10.25919/7jaa-ah53&rft.publisher=Commonwealth Scientific and Industrial Research Organisation&rft.description=The Parkes telescope has been used to discover more than half of the known pulsars. The techniques used continue to be optimised, but the basic search strategy has not changed in decades. In particular the algorithms produce more candidates than can be searched by eye and so machine learning algorithms are used to sift through the candidates. However, the properties of the candidates were designed for human viewing. Here we propose to record data streams from the highly-versatile Parkes UWL system in order to develop pulsar search algorithms that have been developed, from the ground-up, explicitly for machine learning algorithms.&rft.creator=Hobbs, George &rft.creator=Dawson, Joanne &rft.creator=Norris, Ray &rft.creator=Wong, Ivy &rft.creator=Huynh, Minh &rft.creator=Li, Di &rft.creator=Galvin, Tim &rft.creator=Dai, Shi &rft.creator=Wang, Chen &rft.creator=Zhang, Songbo &rft.creator=Zhu, Weiwei &rft.creator=Farajollahi, Hossein &rft.creator=Luo, Rui &rft.creator=Wang, Rosalind &rft.creator=Petersson, Lars &rft.creator=Rolland, Vivien &rft.date=2021&rft.edition=v1&rft_rights=Creative Commons Attribution 4.0 International Licence https://creativecommons.org/licenses/by/4.0/&rft_rights=Data is accessible online and may be reused in accordance with licence conditions&rft_rights=All Rights (including copyright) CSIRO 2021.&rft_subject=pulsars&rft_subject=neutron stars&rft_subject=P1072_2020OCTS&rft_subject=Astronomical sciences not elsewhere classified&rft_subject=Astronomical sciences&rft_subject=PHYSICAL SCIENCES&rft.type=dataset&rft.language=English Access the data
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Creative Commons Attribution 4.0 International Licence
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Data is accessible online and may be reused in accordance with licence conditions

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Brief description

The Parkes telescope has been used to discover more than half of the known pulsars. The techniques used continue to be optimised, but the basic search strategy has not changed in decades. In particular the algorithms produce more candidates than can be searched by eye and so machine learning algorithms are used to sift through the candidates. However, the properties of the candidates were designed for human viewing. Here we propose to record data streams from the highly-versatile Parkes UWL system in order to develop pulsar search algorithms that have been developed, from the ground-up, explicitly for machine learning algorithms.

Available: 2021-02-03

Data time period: 2020-10-01 to 2021-03-31

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Subjects
Astronomical Sciences | Astronomical Sciences Not Elsewhere Classified | P1072_2020OCTS | Physical Sciences | neutron stars | pulsars |

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