High-Frequency and Type-C QPOs from Oscillating, Precessing Hot, Thick Flow

TL;DR

This paper presents a unified model explaining high-frequency and type-C QPOs in black hole X-ray binaries through oscillating, precessing hot flows, linking their origins to geometrical structures and flow dynamics.

Contribution

The study introduces a model that unifies the explanation of multiple QPOs using a single hot flow geometry, connecting their frequencies to specific oscillation modes and precession.

Findings

The model explains the observed correlation between high-frequency and type-C QPOs.

It accounts for the near 3:2 frequency ratio of high-frequency QPOs.

The model successfully matches observations from GRO J1655-40 and predicts lower frequency QPOs.

Abstract

Motivated by recent studies showing an apparent correlation between the high-frequency quasi-periodic oscillations (QPOs) and the low-frequency, type-C QPO in low-mass, black hole X-ray binaries (LMXBs), we explore a model that explains all three QPOs in terms of an oscillating, precessing hot flow in the truncated-disk geometry. Our model favors attributing the two high-frequency QPOs, often occurring in a near 3:2 frequency ratio, to the breathing and vertical epicyclic frequency modes of the hot, thick flow, although we can not rule out the Keplerian and m=-1 radial epicyclic modes. In either case, the type-C QPO is attributed to precession. The correlation of the QPOs comes from the fact that all three frequencies are associated with the same geometrical structure. While the exact QPO frequencies are sensitive to the black hole mass and spin, their evolution over the course of an outburst is mainly tied to the truncation radius between the geometrically thin, optically thick disk and the inner, hot flow. We show that, in the case of the LMXB GRO J1655-40, this model can explain the one simultaneous observation of all three QPOs and that an extrapolation of the model appears to match lower frequency observations where only two of the three components are seen. Thus, this model may be able to unify multiple QPO observations using the properties of a single, simple, geometrical model.