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Objective detection of specific patterns in statistical distributions, likegroupings or gaps or abrupt transitions between different subsets, is a taskwith a rich range of applications in astronomy: Milky Way stellar populationanalysis, investigations of the exoplanets diversity, Solar System minor bodiesstatistics, extragalactic studies, etc. We adapt the powerful technique of thewavelet transforms to this generalized task, making a strong emphasis on theassessment of the patterns detection significance. Among other things, ourmethod also involves optimal minimum-noise wavelets and minimum-noisereconstruction of the distribution density function. Based on this development,we construct a self-closed algorithmic pipeline aimed to process statisticalsamples. It is currently applicable to single-dimensional distributions only,but it is flexible enough to undergo further generalizations and development.

Statistical detection of patterns in unidimensional distributions by continuous wavelet transforms