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X-ray spectral correlations in a sample of Low-mass black hole X-ray binaries in the hard state

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Liu,  Zhu
High Energy Astrophysics, MPI for Extraterrestrial Physics, Max Planck Society;

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Citation

You, B., Dong, Y., Yan, Z., Liu, Z., Tuo, Y., Yao, Y., et al. (2023). X-ray spectral correlations in a sample of Low-mass black hole X-ray binaries in the hard state. The Astrophysical Journal, 945(1): 65. doi:10.3847/1538-4357/acba11.


Cite as: https://hdl.handle.net/21.11116/0000-000D-CB2F-B
Abstract
Massive, young stars are the main source of energy that maintains multiphase structure and turbulence in the interstellar medium (ISM), and without this "feedback" the star formation rate (SFR) would be much higher than is observed. Rapid energy loss in the ISM and efficient energy recovery by stellar feedback lead to coregulation of SFRs and the ISM state. Realistic approaches to this problem should solve for the dynamical evolution of the ISM, including star formation and the input of feedback energy self-consistently and accurately. Here, we present the TIGRESS-NCR numerical framework, in which UV radiation, supernovae, cooling and heating processes, and gravitational collapse are modeled explicitly. We use an adaptive ray-tracing method for UV radiation transfer from star clusters represented by sink particles, accounting for attenuation by dust and gas. We solve photon-driven chemical equations to determine the abundances of hydrogen (time dependent) and carbon/oxygen-bearing species (steady state), which then set cooling and heating rates self-consistently. Applying these methods, we present high-resolution magnetohydrodynamics simulations of differentially rotating local galactic disks representing typical conditions of nearby star-forming galaxies. We analyze ISM properties and phase distributions and show good agreement with existing multiwavelength galactic observations. We measure midplane pressure components (turbulent, thermal, and magnetic) and the weight, demonstrating that vertical dynamical equilibrium holds. We quantify the ratios of pressure components to the SFR surface density, which we call the feedback yields. The TIGRESS-NCR framework will allow for a wide range of parameter exploration, including in low-metallicity systems.