Neuro-Parametric Spectral Classification of Black Hole and Neutron Star X-ray Binary Systems

TL;DR

This paper demonstrates that deep neural networks can accurately classify black hole and neutron star X-ray binaries using spectral data, and interprets the classification through physically meaningful spectral parameters.

Contribution

It introduces a neural network approach that combines spectral data and physical spectral parameters for high-accuracy classification of X-ray binary systems.

Findings

Neural networks achieved 90-94% classification accuracy.

Physically interpretable parameters contribute to classification.

Parameter-based neural network matches spectral data model accuracy.

Abstract

We perform the classification of black hole and neutron star X-ray binary systems using deep neural networks applied to archival RXTE X-ray spectral data. We first construct two neural network models: one trained using only spectral flux values and another trained using both fluxes and their associated errors. Both models achieve high classification accuracies of ~90-94 %. To gain physical interpretability of these networks, we fit all spectra with a simple phenomenological model consisting of a thermal disk component and a power-law. From this analysis, we identify the blackbody temperature, power-law index, the ratio of blackbody to power-law flux, the reduced $\chi^2$, and the variance of the data as key parameters that likely contribute to the classification. We validate this inference by designing an additional neural network trained exclusively on this reduced parameter set, without using the spectral data directly. This parameter-based model achieves a classification accuracy comparable to that of the spectral models. Our results show that deep neural networks can not only classify compact objects in X-ray binaries with high accuracy but can also be interpreted in terms of physically meaningful spectral parameters derived from conventional X-ray spectral analysis. This framework offers a promising, mission-agnostic approach for compact object classification in current and future X-ray surveys.