Performance Analysis Techniques for Real-Time Broadband RFI Filtering System of uGMRT

Author:

Buch K. D.1ORCID,Kale R.1,Naik K. D.1,Aragade R.1,Muley M.1,Kudale S.1,Ajith Kumar B.1

Affiliation:

1. Digital Backend Group Giant Metrewave Radio Telescope (GMRT), NCRA-TIFR, Pune 410504, India

Abstract

Electromagnetic radiation from human activities, known as man-made Radio Frequency Interference (RFI), adversely affects radio astronomy observations. In the vicinity of the Upgraded Giant Metrewave Radio Telescope (uGMRT) array, the sparking on power lines is the major cause of interference at observing frequencies less than 800[Formula: see text]MHz. A real-time broadband RFI detection and filtering system is implemented as part of the uGMRT wideband signal processing backend to mitigate the effect of broadband RFI. Performance analysis techniques used for testing and commissioning the system for observations in the beamformer and correlator modes of the uGMRT are presented. The concept and implementation of recording simultaneous unfiltered and filtered data along with data analysis and interpretation is illustrated using an example. For the beamformer mode, spectrogram, single spectral channel, and its Fourier transform is used for performance analysis whereas, in the correlator mode, the cross-correlation function, closure phase, and visibilities from the simultaneously recorded unfiltered and filtered is carried out. These techniques are used for testing the performance of the broadband RFI filter and releasing it for uGMRT users.

Publisher

World Scientific Pub Co Pte Ltd

Subject

Astronomy and Astrophysics,Instrumentation

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1. A Deep uGMRT View of the Ultra-steep Spectrum Radio Halo in A521;The Astrophysical Journal;2024-02-01

2. A New Enigmatic Radio Relic in the Low-mass Cluster Abell 2108;Monthly Notices of the Royal Astronomical Society;2023-12-23

3. Real-time RFI filtering for uGMRT: Overview of the released system and relevance to the SKA;Journal of Astrophysics and Astronomy;2023-04-25

4. Investigation of a Machine learning methodology for the SKA pulsar search pipeline;Journal of Astrophysics and Astronomy;2023-04-20

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