Classifying X-Ray Binaries: A Probabilistic Approach

Abstract

In X-ray binary star systems consisting of a compact object that accretes material from an orbiting secondary star, there is no straightforward means to decide if the compact object is a black hole or a neutron star. To assist this classification, we develop a Bayesian statistical model that makes use of the fact that X-ray binary systems appear to cluster based on their compact object type when viewed from a 3-dimensional coordinate system derived from X-ray spectral data. The first coordinate of this data is the ratio of counts in mid to low energy band (color 1), the second coordinate is the ratio of counts in high to low energy band (color 2), and the third coordinate is the sum of counts in all three bands. We use this model to estimate the probabilities that an X-ray binary system contains a black hole, non-pulsing neutron star, or pulsing neutron star. In particular, we utilize a latent variable model in which the latent variables follow a Gaussian process prior distribution, and hence we are able to induce the spatial correlation we believe exists between systems of the same type. The utility of this approach is evidenced by the accurate prediction of system types using Rossi X-ray Timing Explorer All Sky Monitor data, but it is not flawless. In particular, non-pulsing neutron systems containing "bursters" that are close to the boundary demarcating systems containing black holes tend to be classified as black hole systems. As a byproduct of our analyses, we provide the astronomer with public R code that can be used to predict the compact object type of X-ray binaries given training data. arXiv:1507.03538v3 [astro-ph.HE]

Comparing X-ray color selection in separating X-ray binary classes using Color-Color-Intensity diagrams

“…We also found considerable overlap between HMXB and LMXB pulsars which suggests that the effect of the high magnetic fields in these systems outweighs the effect of the masses of their companions on their position in CCI diagrams. However, there is a significant difference between the range of fluxes displayed by HMXBs and LMXBs so they should be distinguishable using the machine learning technique of Gopalan et al (2015).…”

“…In a CCI diagram, the various classes of X-ray binaries -systems containing white dwarfs, neutron stars, or black holes -separate into complex, but geometrically distinct volumes. Using CCI diagrams, Gopalan et al (2015) developed a probabilistic (Bayesian) model which uses a supervised learning approach (unknown classifications are predicted using known classifications as priors), to predict the type of an unknown X-ray binary.…”

“…In future work, we will use CCI diagrams to classify the nature of X-ray point sources detected in Chandra observations of external galaxies (Vrtilek et al, in prep) and expand on Gopalan et al (2015) Bayesian techniques to determine the X-ray states of BH binaries using the more sensitive MAXI/GSC data (de Beurs et al in prep).…”

Identifying new X-ray binary candidates in M31 using random forest classification

“…They found that classifications made by their method tend to agree with classifications made with previously established classification techniques (e.g., by Saeedi et al 2016). Gopalan et al (2015) expanded the colour-colour-intensity diagram classification technique by applying a supervised learning algorithm as a method of demarcating systems containing black holes, pulsating neutron stars, or non-pulsating neutron stars. Lo et al (2014) employed the random forest algorithm to classify time-varying X-ray sources in the Second XMM-Newton Serendipitous Source Catalog using X-ray photometric time series and spectra and multiwavelength information.…”

A Machine Learning Approach For Classifying Low-mass X-ray Binaries Based On Their Compact Object Nature

“…Machine learning has also been applied in the X-ray domain by Huppenkothen et al (2017) to classify light curves of the unusual BH X-ray binary GRS 1915+105. An effort to distinguish between different types of Xray binaries has been reported by Gopalan et al (2015), where they use a three-dimensional coordinate system comprising of colourcolour-Intensity diagrams to find clusters of data which can distinguish between BH and NS.…”