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Increasing Pulsar SNR by Using Spectral Kurtosis as a Radio Frequency Mitigation Technique.

  • Published In: Journal of Astronomical Instrumentation, 2024, v. 13, n. 4. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: van Tonder, Vereesé; Schwardt, Ludwig; Faustmann, Alex; Gilmore, Jacki; Büchner, Sarah; Geyer, Marisa 3 of 3

Abstract

We apply the Spectral Kurtosis (SK) estimator as a radio frequency interference (RFI) mitigation technique. The technique is applied to MeerKAT pulsar data whilst operated in beamformer mode. According to the central limit theorem, the unique probability distributions of all radio sources in the sky would add to a Gaussian distribution. The SK estimate of a Gaussian signal is 1. Therefore, any signal that deviates from 1, above or below a specific threshold, can be accounted for as RFI and eliminated. This forms the basis of the SK RFI mitigation technique. However, care should be taken when the astronomical signal contains non-Gaussian components, which is the case for pulsar astronomy. Initially, the statistics and methodology of the estimator are investigated. Afterward, we look at the effects of data clipping, RFI duty cycle, and SNR on the SK estimator. We provide details on the implementation and how to obtain the thresholds used for RFI mitigation. Signal-to-noise ratio (SNR) optimization studies are conducted for two pulsars, J0835-4510 and J0437-4715, where a 116% and 125% improvement in SNR is obtained, respectively. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Astronomical Instrumentation. 2024/12, Vol. 13, Issue 4, p1
  • Document Type:Article
  • Subject Area:Science
  • Publication Date:2024
  • ISSN:2251-1717
  • DOI:10.1142/S2251171724500119
  • Accession Number:184678544
  • Copyright Statement:Copyright of Journal of Astronomical Instrumentation is the property of World Scientific Publishing Company 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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