Simulating Spectral Kurtosis Mitigation against Realistic Radio Frequency Interference Signals

Smith, Edward K.; Lynch, R.; Pisano, D. J.

doi:10.3847/1538-3881/ac7e47

Cited by 5 publications

(1 citation statement)

References 21 publications

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“…For Gaussian noise, the distribution of ŜK is predictable and any deviations from it can be identified up to a certain threshold and confidence and are removed from the data. Several studies have assessed the effectiveness of spectral kurtosis in searches for radio transients [14,15].…”

Section: Spectral Kurtosismentioning

confidence: 99%

A Needle in a Cosmic Haystack: A Review of FRB Search Techniques

Rajwade,

van Leeuwen

2024

Universe

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Ephemeral Fast Radio Bursts (FRBs) must be powered by some of the most energetic processes in the Universe. That makes them highly interesting in their own right, and as precise probes for estimating cosmological parameters. This field thus poses a unique challenge: FRBs must be detected promptly and immediately localised and studied based only on that single millisecond-duration flash. The problem is that the burst occurrence is highly unpredictable and that their distance strongly suppresses their brightness. Since the discovery of FRBs in single-dish archival data in 2007, detection software has evolved tremendously. Pipelines now detect bursts in real time within a matter of seconds, operate on interferometers, buffer high-time and frequency resolution data, and issue real-time alerts to other observatories for rapid multi-wavelength follow-up. In this paper, we review the components that comprise a FRB search software pipeline, we discuss the proven techniques that were adopted from pulsar searches, we highlight newer, more efficient techniques for detecting FRBs, and we conclude by discussing the proposed novel future methodologies that may power the search for FRBs in the era of big data astronomy.

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Section: Spectral Kurtosismentioning

confidence: 99%

A Needle in a Cosmic Haystack: A Review of FRB Search Techniques

Rajwade,

van Leeuwen

2024

Universe

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Enhanced Bayesian RFI mitigation and transient flagging using likelihood reweighting

Anstey,

Leeney

2024

RAS Techniques and Instruments

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Contamination by Radio Frequency Interference (RFI) is a ubiquitous challenge for radio astronomy. In particular, transient RFI is difficult to detect and avoid, especially in large data sets with many time bins. In this work, we present a Bayesian methodology for time-dependent, transient anomaly mitigation performed jointly with model fitting. The computation time for correcting for transient anomalies in this manner in time-separated data sets grows proportionally with the number of time bins. We demonstrate that utilising likelihood reweighting can allow our Bayesian anomaly mitigation method to be performed with a computation time close to independent of the number of time bins. In particular, we identify a factor of 44 improvement in computation time for a test case with 2000 time bins. We also demonstrate how this method enables the flagging threshold to be fit for as a free parameter, fully automating the mitigation process. We find that this threshold fitting also prevents overcorrecting of the data in the case of wide priors. Finally, we investigate the potential of the methodology as a transient detector. We demonstrate that the method is able to reliably flag an individual anomalous data point out of 302,000 provided the SNR ≥10.

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