Spectral-timing analysis of the kilohertz quasi-periodic oscillations and constraints on the mass of the neutron star in 4U 1636-536 using AstroSat observations

TL;DR

This paper analyzes kHz QPOs in 4U 1636-536 using AstroSat data, linking spectral states to variability features and estimating the neutron star's mass through relativistic precession modeling.

Contribution

It provides a comprehensive spectral and timing analysis of kHz QPOs in 4U 1636-536 and constrains the neutron star mass using the relativistic precession model.

Findings

Identification of three variability types, including twin kHz QPOs.

Neutron star mass estimated at 2.37 solar masses.

Insights into corona size and radiative origins of QPOs.

Abstract

Kilohertz quasi-periodic oscillations (kHz QPOs) are believed to originate from the orbital timescales of the inner accretion flow, reflecting the dynamics of the innermost disk regions under strong gravitational forces. Despite numerous radiative and geometric models proposed so far, a comprehensive explanation of the observed properties of these variability components remains elusive. This study systematically examines kHz QPOs, their variability, and their connection to spectral properties in $4U 1636-536$ using AstroSat data. Our analysis tracks the source transition from hard to soft states in the hardness-intensity diagram. Broad spectral analysis (0.7-25 keV) using SXT and LAXPC data indicates a spectrum shaped by reflection from a thermal corona, with contributions from boundary layer emission and a soft disk component. We find significant changes in optical depth, blackbody temperature, and inner disk temperature that likely drive state transitions. Power density spectra reveal three variability types: a low frequency QPO (LFQPOs) (~30 Hz), and two simultaneous kHz QPOs. The LFQPOs and the upper kHz QPOs appear more prominently in soft spectral states. The presence of LFQPOs and twin kHz QPOs in soft spectral states enable us to estimate the neutron star mass at (2.37 $\pm$ 0.02) $M_\odot$ using the relativistic precession model (RPM). Additionally, time-lag and root mean square (rms) analysis provide insights into the size of the corona and the radiative origin of these variability components.