1. Searching for binary and millisecond pulsars : a high-latitude drift-scan survey
- Author
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Kasian, Laura Elizabeth
- Subjects
Astrophysics::High Energy Astrophysical Phenomena ,Astrophysics::Instrumentation and Methods for Astrophysics - Abstract
We performed a drift-scan pulsar survey using the Green Bank Telescope (GBT) in West Virginia, USA. The survey is among the most sensitive ever undertaken in the Northern sky, and was collected with the Berkeley-Caltech Pulsar Machine filterbank. It was performed at low frequencies (400, 600, and 800 MHz) and mostly high Galactic latitudes with the aim of detecting new binary and millisecond pulsars. Population models of-pulsars have shown that young pulsars tend to be concentrated in the Galactic plane, while binary and millisecond pulsars appear to have a more isotropic distribution on the sky. Searches at high Galactic latitudes are therefore required in order to discover new binary and millisecond pulsars, and to better understand the distinct population of millisecond (recycled) pulsars. The data from the survey were reduced mainly using the sigproc pulsar signal processing software package. As of the writing of this thesis, ~ 73% of the data have been searched for periodicities. Each pulsar candidate is given a classification based on how closely it resembles a pulsar (Class 1 = probably a pulsar; Class 4 = most likely not a pulsar) . We were successful in re-detecting 4 previously-known pulsars, and we obtained lists of Class-1 and Class-2 candidates. We will confirm these candidates through follow-up observations with the GBT in December 2005. We detected fewer convincing Class 1 candidates than expected, which we attribute to a recently-discovered problem in the sigproc software and persistent radiofrequency interference (RFI) in the data, particularly at 600 MHz. We have corrected the software problem, which has resulted in the discovery our first Class-1 pulsar candidates. We expect that a reanalysis of processed scans and a first analysis of the remaining ~ 27% of data will yield a significantly higher number of promising candidates.
- Published
- 2005
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