Investigating the Energy-Dependent Temporal Nature of Black Hole Binary System H 1743-322

TL;DR

This study analyzes the energy-dependent timing properties of Type-C QPOs in black hole binary H 1743-322, revealing how spectral components influence variability and lag features during outbursts.

Contribution

It introduces a modeling approach linking spectral parameters to energy-dependent QPO timing features, enhancing understanding of accretion flow dynamics.

Findings

QPOs show soft/negative lags, upper harmonic shows opposite behavior

A simple model with disk temperature and scattering variations explains RMS and lag spectra

Technique successfully identifies spectral parameters responsible for variability

Abstract

Black hole X-ray binaries routinely exhibit Quasi Periodic Oscillations (QPOs) in their Power density spectrum. Studies of QPOs have demonstrated immense ability to understand these dynamical systems although their unambiguous origin still remains a challenge. We investigate the energy-dependent properties of the Type-C QPOs detected for H 1743-322 as observed with AstroSat in its two X-ray outbursts of 2016 and 2017. The combined broadband LAXPC and SXT spectrum is well modelled with a soft thermal and a hard Comptonization component. The QPO exhibits soft/negative lags i.e. variation in soft band lags the variation in hard band, although the upper harmonic shows opposite behaviour i.e. hard/positive lags. Here, we model energy-dependent properties (fractional root mean square and time-lag variation with energy) of the QPO and its upper harmonic individually with a general scheme that fits these properties by utilizing the spectral information and consequently allows to identify the radiative component responsible for producing the variability. Considering the truncated disk picture of accretion flow, a simple model with variation in inner disk temperature, heating rate and fractional scattering with time delays is able to describe the fractional RMS and time-lag spectra. In this work, we show that this technique can successfully describe the energy-dependent features and identify the spectral parameters generating the variability.