A technique of removing large-scale variations in astronomical observations

TL;DR

This paper introduces a simple, structure function-based method to effectively remove large-scale variations from astrophysical data, enabling clearer analysis of small-scale fluctuations in observations like molecular clouds and 21cm signals.

Contribution

The paper presents a novel technique using multi-point structure functions to separate large-scale variations from small-scale fluctuations in astrophysical observations.

Findings

Successfully isolates small-scale fluctuations in simulated data

Enhances detection of small-scale features in 21cm observations

Applicable to various astrophysical systems with large-scale gradients

Abstract

In many astrophysical systems, smoothly-varying large-scale variations coexist with small-scale fluctuations. For example, a large-scale velocity or density gradient can exist in molecular clouds that exhibit small-scale turbulence. In redshifted 21cm observations, we also have two types of signals - the Galactic foreground emissions that change smoothly and the redshifted 21cm signals that change fast in frequency space. Sometimes the large-scale variations make it difficult to extract information on small-scale fluctuations. We propose a simple technique to remove smoothly varying large-scale variations. Our technique relies on multi-point structure functions and can obtain the magnitudes of small-scale fluctuations. It can also help us to filter out large-scale variations and retrieve small-scale maps. We discuss applications of our technique to astrophysical observations.