Classification of X-Ray Binaries in NGC 6946 with the Use of Multiwavelength Data
- 1Sabancı University
- 2Türkiye National Observatories
- 3The George Washington University
Abstract
We identified X-ray binary (XRB) candidates in the galaxy NGC 6946 using archival Chandra data. A total of 98 XRB candidates were detected within the 0.3−8 keV energy range, with luminosities between 10 ³⁶ and 10 ⁴⁰ erg s ⁻¹ . X-ray color–color diagrams were constructed for these sources, classifying 57 as high-mass X-ray binaries (HMXBs) and 28 as low-mass X-ray binaries (LMXBs). The X-ray temporal variability of the sources was analyzed, revealing that 40 sources exhibit variability and 26 are classified as transient sources. Possible optical counterparts for 57 sources were identified using Hubble Space Telescope (HST) images. Based on the color–magnitude diagrams (CMDs), 40 sources were found to have masses greater than 8 M ⊙ and were classified as HMXBs. Seventeen sources with masses between 5 and 8 M ⊙ were classified as intermediate-mass X-ray binaries (IMXBs). The remaining sources, for which optical counterparts could not be identified, were assumed to be LMXBs. The X-ray luminosity functions (XLFs) derived for HMXBs and LMXBs, identified through both X-ray and optical classification, were best described by a single power-law model ( α ≈ 1.3−1.6). We supplemented traditional classification methods with a machine learning (ML) approach based on multiwavelength photometry. While traditional methods identified no active galactic nucleus (AGN) candidates, the ML approach detected a significant AGN population. The spatial resolution of the observing instruments and the limits of the training database hinder the reliability of the ML classifications, thus highlighting a key challenge in combining these methods for extragalactic X-ray source analysis.
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BibTeX
@article{avdan2026classification,
title = {{Classification of X-Ray Binaries in NGC 6946 with the Use of Multiwavelength Data}},
author = {Senay Avdan and Hasan Avdan and Eda Sonbas and Kalvir S. Dhuga},
journal = {The Astrophysical Journal},
year = {2026},
month = nov,
doi = {10.3847/1538-4357/aeaff1},
url = {https://iopscience.iop.org/article/10.3847/1538-4357/aeaff1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Classification of X-Ray Binaries in NGC 6946 with the Use of Multiwavelength Data
AU - Senay Avdan
AU - Hasan Avdan
AU - Eda Sonbas
AU - Kalvir S. Dhuga
PY - 2026
DA - 2026/11/05
JO - The Astrophysical Journal
DO - 10.3847/1538-4357/aeaff1
UR - https://iopscience.iop.org/article/10.3847/1538-4357/aeaff1
AB - We identified X-ray binary (XRB) candidates in the galaxy NGC 6946 using archival Chandra data. A total of 98 XRB candidates were detected within the 0.3−8 keV energy range, with luminosities between 10 ³⁶ and 10 ⁴⁰ erg s ⁻¹ . X-ray color–color diagrams were constructed for these sources, classifying 57 as high-mass X-ray binaries (HMXBs) and 28 as low-mass X-ray binaries (LMXBs). The X-ray temporal variability of the sources was analyzed, revealing that 40 sources exhibit variability and 26 are classified as transient sources. Possible optical counterparts for 57 sources were identified using Hubble Space Telescope (HST) images. Based on the color–magnitude diagrams (CMDs), 40 sources were found to have masses greater than 8 M ⊙ and were classified as HMXBs. Seventeen sources with masses between 5 and 8 M ⊙ were classified as intermediate-mass X-ray binaries (IMXBs). The remaining sources, for which optical counterparts could not be identified, were assumed to be LMXBs. The X-ray luminosity functions (XLFs) derived for HMXBs and LMXBs, identified through both X-ray and optical classification, were best described by a single power-law model ( α ≈ 1.3−1.6). We supplemented traditional classification methods with a machine learning (ML) approach based on multiwavelength photometry. While traditional methods identified no active galactic nucleus (AGN) candidates, the ML approach detected a significant AGN population. The spatial resolution of the observing instruments and the limits of the training database hinder the reliability of the ML classifications, thus highlighting a key challenge in combining these methods for extragalactic X-ray source analysis.
SN - 1538-4357
SN - 0004-637X
ER -