Scholarly Work - Physics

Permanent URI for this collectionhttps://scholarworks.montana.edu/handle/1/3458

Browse

Search Results

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    A dependable distance estimator to black hole low-mass X-ray binaries
    (Oxford University Press, 2024-03) Abdulghani, Y.; Lohfink, A. M.; Chauhan, J.
    Black Hole Low Mass X-ray Binaries (BH-LMXBs) are excellent observational laboratories for studying many open questions in accretion physics. However, determining the physical properties of BH-LMXBs necessitates knowing their distances. With the increased discovery rate of BH-LMXBs, many canonical methods cannot produce accurate distance estimates at the desired pace. In this study, we develop a versatile statistical framework to obtain robust distance estimates soon after discovery. Our framework builds on previous methods where the soft spectral state and the soft-to-hard spectral state transitions, typically present in an outbursting BH-LMXB, are used to place constraints on mass and distance. We further develop the traditional framework by incorporating general relativistic corrections, accounting for spectral/physical parameter uncertainties, and employing assumptions grounded in current theoretical and observational knowledge. We tested our framework by analyzing a sample of 50 BH-LMXB sources using X-ray spectral data from the Swift/XRT, MAXI/GSC, and RXTE/PCA missions. By modeling their spectra, we applied our framework to 26 sources from the 50. Comparison of our estimated distances to previous distance estimates indicates that our findings are dependable and in agreement with the accurate estimates obtained through parallax and H i absorption methods. Investigating the accuracy of our constraints, we have found that estimates obtained using both the soft and transition spectral information have a median uncertainty (1σ) of 20%, while estimates obtained using only the soft spectral state spectrum have a median uncertainty (1σ) of around 50%. Furthermore, we have found no instrument-specific biases.
Copyright (c) 2002-2022, LYRASIS. All rights reserved.