# Recurrence network measures for hypothesis testing using surrogate data: application to black hole light curves

## TL;DR

This paper evaluates the effectiveness of recurrence network measures in analyzing real-world time series data, specifically black hole light curves, to distinguish noise types and infer system dimensionality.

## Contribution

It demonstrates the utility of recurrence network measures, especially characteristic path length and clustering coefficient distribution, in hypothesis testing and system dimensionality analysis of astrophysical data.

## Key findings

- Characteristic path length effectively discriminates noise types.
- Network measures can infer the underlying system's dimensionality.
- Combined network analysis reveals variability characteristics of black hole light curves.

## Abstract

Recurrence networks and the associated statistical measures have become important tools in the analysis of time series data. In this work, we test how effective the recurrence network measures are in analyzing real world data involving two main types of noise, white noise and colored noise. We use two prominent network measures as discriminating statistic for hypothesis testing using surrogate data for a specific null hypothesis that the data is derived from a linear stochastic process. We show that the characteristic path length is especially efficient as a discriminating measure with the conclusions reasonably accurate even with limited number of data points in the time series. We also highlight an additional advantage of the network approach in identifying the dimensionality of the system underlying the time series through a convergence measure derived from the probability distribution of the local clustering coefficients. As examples of real world data, we use the light curves from a prominent black hole system and show that a combined analysis using three primary network measures can provide vital information regarding the nature of temporal variability of light curves from different spectroscopic classes.