Classification of X-ray Binaries in NGC 6946 with the use of Multiwavelength Data
Abstract
We identified X-ray binary (XRB) candidates in the galaxy NGC 6946 using archival {\it Chandra} data. A total of 98 XRB candidates were detected within the 0.3−8 keV energy range, with luminosities between 1036 and 1040 erg~s−1. X-ray color-color diagrams were constructed for these sources, classifying 57 as high-mass X-ray binaries (HMXBs) and 8 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 {\it Hubble Space Telescope} ({\it HST}) images. Based on the color-magnitude diagrams (CMDs), 40 sources were found to have masses greater than 8M⊙ and were classified as HMXBs. Seventeen sources with masses between 5 and 8M⊙ 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 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
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BibTeX
@misc{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},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.06916},
eprint = {2610.06916},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.06916},
note = {CC BY 4.0},
}
RIS
TY - UNPB
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 - 2026/10/02
PB - arXiv
DO - 10.48550/arxiv.2610.06916
UR - https://arxiv.org/abs/2610.06916
AB - We identified X-ray binary (XRB) candidates in the galaxy NGC 6946 using archival {\it 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 8 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 {\it Hubble Space Telescope} ({\it HST}) images. Based on the color-magnitude diagrams (CMDs), 40 sources were found to have masses greater than 8M_⊙ and were classified as HMXBs. Seventeen sources with masses between 5 and 8M_⊙ 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 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
ER -