Estimating the spectral resolution of a radio data cube from its noise properties

Journal article, 2027

We present a new method to estimate the spectral resolution of a radio data cube from its noise properties. We assume that the data cube is the result of the convolution of a spectrally-uncorrelated cube with a constant convolution kernel K. We demonstrate that K and the resolution, defined as K's integral, can be estimated thanks to the analytical relation between the autocorrelation of the noise spectra extracted from the cube and the power spectrum of the kernel. We discuss a number of algorithms that make this method robust in the realistic case of a finite number of spectra and channels, real signal and artefacts, and we present the results obtained for a number of datasets from the literature. We further highlight that this method is applicable to any data cube that meets our assumptions independently of the observing frequency or the exact nature of the cube's “third” axis, e.g., it could be used to measure the time resolution of transient-search data cubes. We make this method available as part of the open-source software tool spectre.