Fast Spectral Variability from Cygnus X-1

TL;DR

This paper introduces an algorithm that synthesizes X-ray variability data from observations, enabling detailed study of spectral changes and reducing noise, demonstrated on Cygnus X-1 data.

Contribution

The paper presents a novel algorithm that reconstructs spectral variability and reduces noise in X-ray binary data, enhancing analysis of fast spectral changes.

Findings

Spectral index varies between 1.6 and 1.8 on 62 ms timescales.

Spectral index positively correlates with flux at short timescales.

Algorithm successfully reproduces power density spectra and time lags.

Abstract

We have developed an algorithm that, starting from the observed properties of the X-ray spectrum and fast variability of an X-ray binary allows the production of synthetic data reproducing observables such as power density spectra and time lags, as well as their energy dependence. This allows to reconstruct the variability of parameters of the energy spectrum and to reduce substantially the effects of Poisson noise, allowing to study fast spectral variations. We have applied the algorithm to Rossi X-ray Timing Explorer data of the black-hole binary Cygnus X-1, fitting the energy spectrum with a simplified power law model. We recovered the distribution of the power law spectral indices on time-scales as low as 62 ms as being limited between 1.6 and 1.8. The index is positively correlated with the flux even on such time-scales.