PEACE: pulsar evaluation algorithm for candidate extraction - a software package for post-analysis processing of pulsar survey candidates
Modern radio pulsar surveys produce a large volume of prospective candidates, the majority of which are polluted by human-created radio frequency interference or other forms of noise. Typically, large numbers of candidates need to be visually inspected in order to determine if they are real pulsars....
| Main Authors: | , , , , , , , , , |
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| Format: | Journal Article |
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Wiley-Blackwell Publishing Ltd.
2013
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/10500 |
| _version_ | 1848747550228611072 |
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| author | Lee, K Stovall, K Jenet, F Martinez, J Dartez, L Mata, A Lunsford, G Cohen, S Biwer, C Rohr, M |
| author_facet | Lee, K Stovall, K Jenet, F Martinez, J Dartez, L Mata, A Lunsford, G Cohen, S Biwer, C Rohr, M |
| author_sort | Lee, K |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Modern radio pulsar surveys produce a large volume of prospective candidates, the majority of which are polluted by human-created radio frequency interference or other forms of noise. Typically, large numbers of candidates need to be visually inspected in order to determine if they are real pulsars. This process can be labour intensive. In this paper, we introduce an algorithm called Pulsar Evaluation Algorithm for Candidate Extraction (peace) which improves the efficiency of identifying pulsar signals. The algorithm ranks the candidates based on a score function. Unlike popular machine-learning-based algorithms, no prior training data sets are required. This algorithm has been applied to data from several large-scale radio pulsar surveys. Using the human-based ranking results generated by students in the Arecibo Remote Command Center programme, the statistical performance of peace was evaluated. It was found that peace ranked 68?per?cent of the student-identified pulsars within the top 0.17?per?cent of sorted candidates, 95?per?cent within the top 0.34?per?cent and 100?per?cent within the top 3.7?per?cent. This clearly demonstrates that peace significantly increases the pulsar identification rate by a factor of about 50 to 1000. To date, peace has been directly responsible for the discovery of 47 new pulsars, 5 of which are millisecond pulsars that may be useful for pulsar timing based gravitational-wave detection projects |
| first_indexed | 2025-11-14T06:50:56Z |
| format | Journal Article |
| id | curtin-20.500.11937-10500 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:50:56Z |
| publishDate | 2013 |
| publisher | Wiley-Blackwell Publishing Ltd. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-105002017-09-13T15:54:08Z PEACE: pulsar evaluation algorithm for candidate extraction - a software package for post-analysis processing of pulsar survey candidates Lee, K Stovall, K Jenet, F Martinez, J Dartez, L Mata, A Lunsford, G Cohen, S Biwer, C Rohr, M pulsars: general methods: statistical Modern radio pulsar surveys produce a large volume of prospective candidates, the majority of which are polluted by human-created radio frequency interference or other forms of noise. Typically, large numbers of candidates need to be visually inspected in order to determine if they are real pulsars. This process can be labour intensive. In this paper, we introduce an algorithm called Pulsar Evaluation Algorithm for Candidate Extraction (peace) which improves the efficiency of identifying pulsar signals. The algorithm ranks the candidates based on a score function. Unlike popular machine-learning-based algorithms, no prior training data sets are required. This algorithm has been applied to data from several large-scale radio pulsar surveys. Using the human-based ranking results generated by students in the Arecibo Remote Command Center programme, the statistical performance of peace was evaluated. It was found that peace ranked 68?per?cent of the student-identified pulsars within the top 0.17?per?cent of sorted candidates, 95?per?cent within the top 0.34?per?cent and 100?per?cent within the top 3.7?per?cent. This clearly demonstrates that peace significantly increases the pulsar identification rate by a factor of about 50 to 1000. To date, peace has been directly responsible for the discovery of 47 new pulsars, 5 of which are millisecond pulsars that may be useful for pulsar timing based gravitational-wave detection projects 2013 Journal Article http://hdl.handle.net/20.500.11937/10500 10.1093/mnras/stt758 Wiley-Blackwell Publishing Ltd. unknown |
| spellingShingle | pulsars: general methods: statistical Lee, K Stovall, K Jenet, F Martinez, J Dartez, L Mata, A Lunsford, G Cohen, S Biwer, C Rohr, M PEACE: pulsar evaluation algorithm for candidate extraction - a software package for post-analysis processing of pulsar survey candidates |
| title | PEACE: pulsar evaluation algorithm for candidate extraction - a software package for post-analysis processing of pulsar survey candidates |
| title_full | PEACE: pulsar evaluation algorithm for candidate extraction - a software package for post-analysis processing of pulsar survey candidates |
| title_fullStr | PEACE: pulsar evaluation algorithm for candidate extraction - a software package for post-analysis processing of pulsar survey candidates |
| title_full_unstemmed | PEACE: pulsar evaluation algorithm for candidate extraction - a software package for post-analysis processing of pulsar survey candidates |
| title_short | PEACE: pulsar evaluation algorithm for candidate extraction - a software package for post-analysis processing of pulsar survey candidates |
| title_sort | peace: pulsar evaluation algorithm for candidate extraction - a software package for post-analysis processing of pulsar survey candidates |
| topic | pulsars: general methods: statistical |
| url | http://hdl.handle.net/20.500.11937/10500 |