# Statistical discrimination of RFI and astronomical transients in 2-bit digitized time domain signals

## TL;DR

This paper evaluates the effectiveness of the Spectral Kurtosis estimator in detecting and distinguishing natural and artificial short-duration transients in 2-bit VLBI data, highlighting the importance of spectral analysis for accurate discrimination.

## Contribution

It demonstrates that spectral domain analysis enables discrimination between natural and artificial transients in 2-bit VLBI data, which is not possible with time domain analysis alone.

## Key findings

- Spectral Kurtosis detects transients efficiently in 2-bit data.
- Discrimination between natural and artificial transients requires spectral analysis.
- Spectral domain analysis improves identification of astronomical signals in contaminated data.

## Abstract

We investigate the performance of the generalized Spectral Kurtosis (SK) estimator in detecting and discriminating natural and artificial very short duration transients in the 2-bit sampling time domain Very-Long-Baseline Interferometry (VLBI) data. We demonstrate that, while both types of transients may be efficiently detected, their natural or artificial nature cannot be distinguished if only a time domain SK analysis is performed. However, these two types of transients become distinguishable from each other in the spectral domain, after a 32-bit FFT operation is performed on the 2-bit time domain voltages. We discuss the implication of these findings on the ability of the Spectral Kurtosis estimator to automatically detect bright astronomical transient signals of interests -- such as pulsar or fast radio bursts (FRB) -- in VLBI data streams that have been severely contaminated by unwanted radio frequency interference.