Unsupervised spectral decomposition of X-ray binaries with application to GX 339-4

TL;DR

This paper demonstrates that unsupervised spectral decomposition methods, especially NMF, effectively distinguish spectral components in X-ray binary data, providing insights into accretion disc evolution without extensive spectral fitting.

Contribution

The study introduces the application of NMF for spectral decomposition in X-ray binaries, outperforming other methods and enabling component analysis without traditional spectral fitting.

Findings

NMF outperforms PCA and ICA in spectral component separation.

NMF components can be fitted separately to track parameter evolution.

Results on GX 339-4 match previous studies and reveal disc inner radius at ISCO.

Abstract

In this paper we explore unsupervised spectral decomposition methods for distinguishing the effect of different spectral components for a set of consecutive spectra from an X-ray binary. We use well-established linear methods for the decomposition, namely principal component analysis, independent component analysis and non-negative matrix factorisation (NMF). Applying these methods to a simulated dataset consisting of a variable multicolour disc black body and a cutoff power law, we find that NMF outperforms the other two methods in distinguishing the spectral components. In addition, due the non-negative nature of NMF, the resulting components may be fitted separately, revealing the evolution of individual parameters. To test the NMF method on a real source, we analyse data from the low-mass X-ray binary GX 339-4 and found the results to match those of previous studies. In addition, we found the inner radius of the accretion disc to be located at the innermost stable circular orbit in the intermediate state right after the outburst peak. This study shows that using unsupervised spectral decomposition methods results in detecting the separate component fluxes down to low flux levels. Also, these methods provide an alternative way of detecting the spectral components without performing actual spectral fitting, which may prove to be practical when dealing with large datasets.