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H I stacking has proven to be a highly effective tool to statistically analyse average H I properties for samples of galaxies which may or may not be directly detected. With the plethora of H I data expected from the various upcoming H I surveys with the SKA Precursor and Pathfinder telescopes, it will be helpful to standardize the way in which stacking analyses are conducted. In this work we present a new PYTHON-based package, HISS, designed to stack H I (emission and absorption) spectra in a consistent and reliable manner. As an example, we use HISS to study the H I content in various galaxy sub-samples from the NIBLES survey of SDSS galaxies which were selected to represent their entire range in total stellar mass without a prior colour selection. This allowed us to compare the galaxy colour to average H I content in both detected and non-detected galaxies. Our sample, with a stellar mass range of 10^8 lt {{ M}}_star (M_odot) lt 10^{12}, has enabled us to probe the H I-to-stellar mass gas fraction relationship more than half an order of magnitude lower than in previous stacking studies.
In this paper we introduce a method for stacking data cubelets extracted from interferometric surveys of galaxies in the redshifted 21-cm H,textsc{i} line. Unlike the traditional spectral stacking technique, which stacks one-dimensional spectra extra
Hydrogen and helium emission lines in nebulae form by radiative recombination. This is a simple process which, in principle, can be described to very high precision. Ratios of He I and H I emission lines can be used to measure the He$^+$/H$^+$ abunda
Aims. To present the new novel statistical clustering tool INDICATE which assesses and quantifies the degree of spatial clustering of each object in a dataset, discuss its applications as a tracer of morphological stellar features in star forming reg
Context: The interaction of the light from astronomical objects with the constituents of the Earths atmosphere leads to the formation of telluric absorption lines in ground-based collected spectra. Correcting for these lines, mostly affecting the red
Spectra derived from fast Fourier transform (FFT) analysis of time-domain data intrinsically contain statistical fluctuations whose distribution depends on the number of accumulated spectra contributing to a measurement. The tail of this distribution