Correlating nonlinear properties with spectral states of RXTE data: Possible observational evidences for four different accretion modes around compact objects
Oluwashina Adegoke (1), Prasun Dhang (1), Banibrata Mukhopadhyay (1),, M. C. Ramadevi (2), Debbijoy Bhattacharya (3) ((1) IISc, (2) ISAC, (3), Manipal)

TL;DR
This study investigates the relationship between nonlinear variability and spectral states in X-ray binaries, proposing that different accretion modes can be identified through combined spectral and nonlinear time series analysis, with accretion rate influencing transitions.
Contribution
It links nonlinear time series features with spectral states to diagnose accretion modes and their transitions in X-ray binaries, a novel approach in understanding accretion physics.
Findings
Different nonlinear features correspond to specific spectral states.
Transitions between accretion modes are associated with changes in accretion rate.
Spectral and nonlinear analyses together can diagnose accretion flow geometry.
Abstract
By analyzing the time series of RXTE/PCA data, the nonlinear variabilities of compact sources have been repeatedly established. Depending on the variation in temporal classes, compact sources exhibit different nonlinear features. Sometimes they show low correlation/fractal dimension, but in other classes or intervals of time they exhibit stochastic nature. This could be because the accretion flow around a compact object is a nonlinear general relativistic system involving magnetohydrodynamics. However, the more conventional way of addressing a compact source is the analysis of its spectral state. Therefore, the question arises: What is the connection of nonlinearity to the underlying spectral properties of the flow when the nonlinear properties are related to the associated transport mechanisms describing the geometry of the flow? The present work is aimed at addressing this question.…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
