Scaling of the photon index vs mass accretion rate correlation and estimate of black hole mass in M101 ULX-1
Lev Titarchuk (University of Ferrara, Italy, National Research Nuclear, University MEPhI (Moscow Engineering Physics Institute), Moscow, Russia) and, Elena Seifina (Moscow State University/Sternberg Astronomical Institute,, Russia)

TL;DR
This study analyzes X-ray observations of M101 ULX-1, revealing spectral state transitions, establishing a photon index correlation with accretion rate, and estimating the black hole mass to be around 10,000 solar masses, indicating an intermediate-mass black hole.
Contribution
The paper introduces a method to estimate black hole mass in ULXs using the photon index versus accretion rate correlation, supported by observational evidence and scaling from Galactic black holes.
Findings
Spectral transitions from low/hard to high/soft states observed.
Photon index saturation level identified at ~2.8.
Black hole mass estimated to be approximately 3.2-4.3 x 10^4 solar masses.
Abstract
We report the results of Swift and Chandra observations of an ultra-luminous X-ray source, ULX-1 in M101. We show strong observational evidence that M101 ULX-1 undergoes spectral transitions from the low/hard state to the high/soft state during these observations. The spectra of M101 ULX-1 are well fitted by the so-called bulk motion Comptonization (BMC) model for all spectral states. We have established the photon index (\Gamma) saturation level, \Gamma_{sat}=2.8 +/- 0.1, in the \Gamma vs. mass accretion rate (\dot M) correlation. This \Gamma-\dot M correlation allows us to evaluate black hole (BH) mass in M101 ULX-1 to be M_{BH}~(3.2 - 4.3)x10^4 solar masses assuming the spread in distance to M101 (from 6.4+/- 0.5 Mpc to 7.4+/-0.6 Mpc). For this BH mass estimate we use the scaling method taking Galactic BHs XTE~J1550-564, H~1743-322 and 4U~1630-472 as reference sources. The Gamma vs.…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
