Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.10023 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915679389614080 |
|---|---|
| author | Neumann, Kyle D. Falcone, Abraham D. DiKerby, Stephen Deppe, Sierra Ferrara, Elizabeth C. Kennea, Jamie A. Cenko, Brad Grove, Eric |
| author_facet | Neumann, Kyle D. Falcone, Abraham D. DiKerby, Stephen Deppe, Sierra Ferrara, Elizabeth C. Kennea, Jamie A. Cenko, Brad Grove, Eric |
| contents | Our survey of the fourth $\mathit{Fermi}$ Large Area Telescope catalog (4FGL) unassociated gamma-ray source regions using the X-Ray Telescope (XRT) and Ultraviolet/Optical Telescope (UVOT) aboard the Neil Gehrels $\mathit{Swift}$ Observatory ($\mathit{Swift}$) provides new XRT and UVOT source detections and localizations to help identify potential low-energy counterparts to unassociated $\mathit{Fermi}$ gamma-ray sources. We present a catalog of 218 singlet and 70 multiplet $\mathit{Swift}$ X-ray sources detected within the positional uncertainty ellipses of 244 unassociated $\mathit{Fermi}$ gamma-ray sources from the 4FGL-DR4 catalog, 144 of which are not previously cataloged by Kerby et al. (2021b). For each X-ray source, we derive its X-ray flux and photon index, then use simultaneous UVOT observations with optical survey data to estimate its $V$-band magnitude. We use these parameters as inputs for a multi-layer perceptron (MLP) neural network classifier (NNC) trained to classify sources as blazars, pulsars, or ambiguous gamma-ray sources. For the 213 singlet sources with X-ray and optical data, we classify 173 as likely blazars ($P_\mathrm{bzr} > 0.99$) and 6 as likely pulsars ($P_\mathrm{bzr} < 0.01$), with 34 sources yielding ambiguous results. Including 70 multiplet X-ray sources, we increase the number of $P_\mathrm{bzr} > 0.99$ to 227 and $P_\mathrm{bzr} < 0.01$ to 16. For the subset of these classifications that have been previously studied, a large majority agree with prior classifications, supporting the validity of using this NNC to classify the unknown and newly detected gamma-ray sources. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_10023 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Classification of a New X-ray Catalog of Likely Counterparts to 4FGL-DR4 Unassociated Gamma-ray Sources Using a Neural Network Neumann, Kyle D. Falcone, Abraham D. DiKerby, Stephen Deppe, Sierra Ferrara, Elizabeth C. Kennea, Jamie A. Cenko, Brad Grove, Eric High Energy Astrophysical Phenomena Our survey of the fourth $\mathit{Fermi}$ Large Area Telescope catalog (4FGL) unassociated gamma-ray source regions using the X-Ray Telescope (XRT) and Ultraviolet/Optical Telescope (UVOT) aboard the Neil Gehrels $\mathit{Swift}$ Observatory ($\mathit{Swift}$) provides new XRT and UVOT source detections and localizations to help identify potential low-energy counterparts to unassociated $\mathit{Fermi}$ gamma-ray sources. We present a catalog of 218 singlet and 70 multiplet $\mathit{Swift}$ X-ray sources detected within the positional uncertainty ellipses of 244 unassociated $\mathit{Fermi}$ gamma-ray sources from the 4FGL-DR4 catalog, 144 of which are not previously cataloged by Kerby et al. (2021b). For each X-ray source, we derive its X-ray flux and photon index, then use simultaneous UVOT observations with optical survey data to estimate its $V$-band magnitude. We use these parameters as inputs for a multi-layer perceptron (MLP) neural network classifier (NNC) trained to classify sources as blazars, pulsars, or ambiguous gamma-ray sources. For the 213 singlet sources with X-ray and optical data, we classify 173 as likely blazars ($P_\mathrm{bzr} > 0.99$) and 6 as likely pulsars ($P_\mathrm{bzr} < 0.01$), with 34 sources yielding ambiguous results. Including 70 multiplet X-ray sources, we increase the number of $P_\mathrm{bzr} > 0.99$ to 227 and $P_\mathrm{bzr} < 0.01$ to 16. For the subset of these classifications that have been previously studied, a large majority agree with prior classifications, supporting the validity of using this NNC to classify the unknown and newly detected gamma-ray sources. |
| title | Classification of a New X-ray Catalog of Likely Counterparts to 4FGL-DR4 Unassociated Gamma-ray Sources Using a Neural Network |
| topic | High Energy Astrophysical Phenomena |
| url | https://arxiv.org/abs/2512.10023 |