标题： 千赫兹准周期振荡的能谱-时间分析及利用AstroSat观测对$4U 1636-536$中中子星质量的限制 显示英文标题

标题： 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

摘要： 千赫兹类周期性振荡（kHz QPOs）被认为起源于内吸积流的轨道时间尺度，反映了强引力作用下最内盘区域的动力学。尽管迄今为止提出了许多辐射和几何模型，但这些变化成分的观测特性仍缺乏全面解释。 本研究系统地考察了kHz QPOs及其变化，并使用AstroSat数据探讨了它们与$4U 1636-536$光谱性质的联系。 我们的分析追踪了源从硬态到软态的变化，在硬度-强度图中表现出来。 广泛的光谱分析（0.7-25 keV）使用SXT和LAXPC数据表明，光谱由热冕的反射形成，边界层发射和软盘成分都有贡献。 我们发现光学深度、黑体温度和内盘温度有显著变化，这些变化可能驱动状态转换。 功率密度谱揭示了三种变化类型：低频QPO（LFQPOs）（约30 Hz），以及两个同时出现的kHz QPOs。 LFQPOs和上部kHz QPOs在软谱态中更为明显。 软谱态中LFQPOs和双峰kHz QPOs的存在使我们能够使用相对论进动模型（RPM）估算中子星质量为（2.37$\pm$0.02）$M_\odot$。 此外，时间延迟和均方根（rms）分析提供了关于冕大小及这些变化成分辐射起源的见解。 显示英文摘要

摘要： 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.