maxsmooth: Rapid Maximally Smooth Function Fitting With Applications in Global 21-cm Cosmology

TL;DR

maxsmooth is a fast, open-source tool for fitting maximally smooth functions, crucial for foreground modeling in 21-cm cosmology, enabling efficient detection of systematics in experimental data.

Contribution

This paper introduces maxsmooth, a quadratic programming-based package that significantly accelerates MSF fitting and extends the method to partially smooth functions for complex foreground modeling.

Findings

maxsmooth reduces fitting time by about 100 times

demonstrates effective detection of sinusoidal systematics in EDGES data

applies MSFs to LEDA data, revealing sinusoidal systematics

Abstract

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. The constrained nature of MSFs makes fitting these functions a non-trivial task. 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 and show that we can reduce the fitting time by approximately two orders of magnitude. We 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. 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. We demonstrate the presence of a sinusoidal systematic in the EDGES data with a log-evidence difference of $86.19\pm0.12$ when compared to a pure foreground fit. MSFs are applied to data from LEDA for the first time in this paper and we identify the presence of sinusoidal systematics. maxsmooth is pip installable and available for download at: https://github.com/htjb/maxsmooth