Tutorial on running median subtraction filter with application to searches for exotic field transients in multi-messenger astronomy

TL;DR

This paper provides a comprehensive tutorial on the Running Median Subtraction Filter (RMSF), demonstrating its effectiveness in removing baseline variations while preserving transient signals in multi-messenger astronomy data, especially for detecting exotic fields.

Contribution

It offers a detailed analysis and validation of RMSF's properties and performance, including its ability to preserve signals and statistical characteristics, with practical guidance for its application in astrophysical data analysis.

Findings

RMSF effectively filters out baseline variations without distorting transients.

RMSF preserves the statistical properties of white noise in data.

The paper includes practical illustrations and proofs of RMSF properties.

Abstract

Running Median Subtraction Filter (RMSF) is a robust statistical tool for removing slowly varying baselines in data streams containing transients (short-duration signals) of interest. In this work, we explore the RMSF performance and properties using simulated time series and analytical methods. We study the RMSF fidelity in preserving the signal of interest in the data using (i) a Gaussian pulse and (ii) a transient oscillatory signal. Such signals may be generated by hypothetical exotic low-mass fields (ELFs) associated with intense astrophysical events like binary black hole or neutron star mergers. We consider and assess RMSF as a candidate method to extract transient ELF signals. RMSF operates by sliding a window across the data and subtracting the median value within each window from the data points. With a suitable choice of running window size, RMSF effectively filters out baseline variations without compromising the integrity of transients. The RMSF window width is a critical parameter: it must be wide enough to encompass a short transient but narrow enough to remove the slowly varying baseline. We show that the RMSF removes the mean of a normally distributed white noise while preserving its variance and higher order moments in the limit of large windows. In addition, RMSF does not color the white noise stream, i.e., it does not induce any significant correlation in the filtered data. Ideally, a filter would preserve both the signal of interest and the statistical characteristics of the stochastic component of the data, while removing the background clutter and outliers. We find the RMSF to satisfy these practical criteria for data pre-processing. While we rigorously prove several RMSF properties, the paper is organized as a tutorial with multiple illustrations of RMSF applications.