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PreprintCC BY 4.0arXiv

Validating an X-ray-based method for identifying Fermi blazars

Abstract

Context. Blazars are the main population of active galactic nuclei (AGNs) detected in gamma rays by Fermi. However, around 34% of Fermi sources remain unidentified, partly due to their large positional uncertainties, motivating multiwavelength methods to identify possible counterparts. Aims. We evaluate a method proposed to identify BL Lacertae (BL Lac) candidates among unidentified Fermi sources based on their X-ray properties, using Swift-XRT observations. Methods. We examined published optical spectra for an initial sample of 148 X-ray-selected candidates. We found 71 sources with spectra spatially consistent with the proposed associations and reassessed their classifications using standard blazar criteria. We then compared these classifications with the method's initial predictions. Results. Of the 71 proposed counterparts, 80.28% are classified as BL Lac objects and 87.32% as possible gamma-ray-emitting AGNs, supporting the effectiveness of the X-ray-based selection. We also identify four Seyfert galaxies, suggesting that the method is sensitive to this class despite its rarity among gamma-ray emitters. Conclusions. Our results show that X-ray observations are effective for selecting BL Lac candidates among unidentified Fermi sources and support future spectroscopic follow-up campaigns. The high fraction of BL Lacs and AGNs suggests that blazars may account for a substantial part of the unidentified gamma-ray population. The Seyfert identifications also raise questions about the origin of high-energy emission in AGNs not typically associated with relativistic jets, highlighting the need for further multiwavelength observations.

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BibTeX
@misc{weiss2026validating,
  title = {{Validating an X-ray-based method for identifying Fermi blazars}},
  author = {J. L. Weiss and E. J. Marchesini and I. Andruchow and N. Masetti},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.37902},
  eprint = {2610.37902},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.37902},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - Validating an X-ray-based method for identifying Fermi blazars
AU  - J. L. Weiss
AU  - E. J. Marchesini
AU  - I. Andruchow
AU  - N. Masetti
PY  - 2026
DA  - 2026/10/27
PB  - arXiv
DO  - 10.48550/arxiv.2610.37902
UR  - https://arxiv.org/abs/2610.37902
AB  - Context. Blazars are the main population of active galactic nuclei (AGNs) detected in gamma rays by Fermi. However, around 34% of Fermi sources remain unidentified, partly due to their large positional uncertainties, motivating multiwavelength methods to identify possible counterparts. Aims. We evaluate a method proposed to identify BL Lacertae (BL Lac) candidates among unidentified Fermi sources based on their X-ray properties, using Swift-XRT observations. Methods. We examined published optical spectra for an initial sample of 148 X-ray-selected candidates. We found 71 sources with spectra spatially consistent with the proposed associations and reassessed their classifications using standard blazar criteria. We then compared these classifications with the method's initial predictions. Results. Of the 71 proposed counterparts, 80.28% are classified as BL Lac objects and 87.32% as possible gamma-ray-emitting AGNs, supporting the effectiveness of the X-ray-based selection. We also identify four Seyfert galaxies, suggesting that the method is sensitive to this class despite its rarity among gamma-ray emitters. Conclusions. Our results show that X-ray observations are effective for selecting BL Lac candidates among unidentified Fermi sources and support future spectroscopic follow-up campaigns. The high fraction of BL Lacs and AGNs suggests that blazars may account for a substantial part of the unidentified gamma-ray population. The Seyfert identifications also raise questions about the origin of high-energy emission in AGNs not typically associated with relativistic jets, highlighting the need for further multiwavelength observations.
ER  -