# Non-stationary variability in accreting compact objects

## TL;DR

This paper explores how non-stationary power spectra affect flux distributions and rms-flux relations in accreting compact objects, showing that exponential models can still describe observed variability despite non-stationarity.

## Contribution

It demonstrates that exponential transforms of Gaussian processes can model non-stationary light curves and highlights potential misinterpretations from poorly sampled PSDs.

## Key findings

- Exponential models reproduce observed flux properties in non-stationary light curves.
- Non-lognormal flux distributions can arise from sampling issues.
- Proper sampling of PSDs is crucial for accurate variability analysis.

## Abstract

Accreting compact objects show variations in source flux over a broad range of timescales and in all wavebands. The light curves typically show a lognormal distribution of flux and a linear relation between flux and rms. It has been demonstrated that an exponential transform of an underlying (and unobserved) Gaussian stochastic process provides a very good description of the light curves with these observed properties. Recently, a non-stationary power spectrum was observed on fast timescales (~ days) in the active galaxy, IRAS 13224--3809, as well as a non-lognormal flux distribution and non-linear rms-flux relation. Here, we investigate the affects of piecewise non-stationary power spectra on the resultant flux distribution and rms-flux relation. We demonstrate that the simple "exponentiation" model successfully reproduces the observed quantities, even when the light curves are non-stationary. We also demonstrate how non-lognormal flux distributions and rms-flux relations inconsistent with a linear model can be erroneously produced from poorly sampled PSDs. This is of particular importance for AGN surveys where very long baselines are required to sample the PSD down to low enough frequencies.