http://arxiv.org/abs/2007.14970
Maximally Smooth Functions (MSFs) are a form of constrained functions in which there are no inflection points or zero crossings in high order derivatives. Consequently, they have applications to signal recovery in experiments where signals of interest are expected to be non-smooth features masked by larger smooth signals or foregrounds. They can also act as a powerful tool for diagnosing the presence of systematics. However, the constrained nature of MSFs makes fitting these functions a non-trivial task. Here, we introduce maxsmooth, an open source package that uses quadratic programming to rapidly fit MSFs. We demonstrate the efficiency and reliability of maxsmooth by comparison to commonly used fitting routines. We show that by using quadratic programming we can reduce the fitting time by approximately two orders of magnitude. maxsmooth features a built-in library of MSF models and allows the user to define their own. We also introduce and implement with maxsmooth Partially Smooth Functions, which are useful for describing elements of non-smooth structure in foregrounds. This work has been motivated by the problem of foreground modelling in 21-cm cosmology for which MSFs have been shown to be a viable alternative to polynomial models. We discuss applications of maxsmooth to 21-cm cosmology and highlight this with examples using data from the Experiment to Detect the Global Epoch of Reionization Signature (EDGES) and the Large-aperture Experiment to Detect the Dark Ages (LEDA) experiments. MSFs are applied to data from LEDA for the first time in this paper. maxsmooth is pip installable and available for download at: https://github.com/htjb/maxsmooth
H. Bevins, W. Handley, A. Fialkov, et. al.
Thu, 30 Jul 20
-588/71
Comments: 19 pages, 15 figures
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