Estimating the spectral resolution of a radio data cube from its noise properties
Abstract
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.
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BibTeX
@misc{serra2026estimating,
title = {{Estimating the spectral resolution of a radio data cube from its noise properties}},
author = {P. Serra and G. Mulas and K. M. Hess and P. Kamphuis and T. Westmeier and Y. Ascasibar and J. M. van der Hulst},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.17223},
eprint = {2610.17223},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.17223},
}
RIS
TY - UNPB
TI - Estimating the spectral resolution of a radio data cube from its noise properties
AU - P. Serra
AU - G. Mulas
AU - K. M. Hess
AU - P. Kamphuis
AU - T. Westmeier
AU - Y. Ascasibar
AU - J. M. van der Hulst
PY - 2026
DA - 2026/10/12
PB - arXiv
DO - 10.48550/arxiv.2610.17223
UR - https://arxiv.org/abs/2610.17223
AB - 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.
ER -