Classification of Galactic Planetary Nebulae Among HASH Candidates
- 1Space Science and Solar Energy Research and Application Center (UZAYMER) , University of Çukurova, 01330, Adana ,
- 2Adana Organised Industrial Zones Vocational School of Technical Science , University of Çukurova, 01410, Adana ,
Abstract
We present a multi-diagnostic classification of 149 new planetary nebula (PN) candidates drawn from the HASH database for which optical spectra are available. Emission-line fluxes were extracted using the automated fitting code alfa. Of the 149 candidates, 26 exhibit emission lines above a 3σ detection threshold and form the working sample. We apply layered diagnostic criteria comprising classical emission-line ratio criteria, BPT and SMB diagnostic diagrams, a symbiotic star (SySt) discriminator based on [O III] auroral-to-nebular ratios, and a group-aware supervised machine-learning classifier (CatBoost; ROC–AUC ∼0.96). Two objects are classified as PN candidates (PN G022.5+14.4 and PN G023.2+13.6) and one as a tentative PN candidate (PN G021.3−05.0), one as a possible symbiotic-star candidate (PN G025.6-05.9), nine as SNRs, three as objects with mixed HII Region/SNR characteristics, and ten as HII regions. Under a strict high-purity operating threshold (p̂thr ≈ 0.75), the CatBoost classifier flags three high-confidence candidates. We find that the machine-learning classification is consistent with the diagnostics derived from the emission-line analysis.
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BibTeX
@article{demirci2026classification,
title = {{Classification of Galactic Planetary Nebulae Among HASH Candidates}},
author = {A Demirci and Y C Atilgan and N Aksaker},
journal = {Monthly Notices of the Royal Astronomical Society},
year = {2026},
month = oct,
doi = {10.1093/mnras/stag2003},
url = {https://academic.oup.com/mnras/advance-article/doi/10.1093/mnras/stag2003/8965451},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Classification of Galactic Planetary Nebulae Among HASH Candidates
AU - A Demirci
AU - Y C Atilgan
AU - N Aksaker
PY - 2026
DA - 2026/10/21
JO - Monthly Notices of the Royal Astronomical Society
DO - 10.1093/mnras/stag2003
UR - https://academic.oup.com/mnras/advance-article/doi/10.1093/mnras/stag2003/8965451
AB - We present a multi-diagnostic classification of 149 new planetary nebula (PN) candidates drawn from the HASH database for which optical spectra are available. Emission-line fluxes were extracted using the automated fitting code alfa. Of the 149 candidates, 26 exhibit emission lines above a 3σ detection threshold and form the working sample. We apply layered diagnostic criteria comprising classical emission-line ratio criteria, BPT and SMB diagnostic diagrams, a symbiotic star (SySt) discriminator based on [O III] auroral-to-nebular ratios, and a group-aware supervised machine-learning classifier (CatBoost; ROC–AUC ∼0.96). Two objects are classified as PN candidates (PN G022.5+14.4 and PN G023.2+13.6) and one as a tentative PN candidate (PN G021.3−05.0), one as a possible symbiotic-star candidate (PN G025.6-05.9), nine as SNRs, three as objects with mixed HII Region/SNR characteristics, and ten as HII regions. Under a strict high-purity operating threshold (p̂_thr ≈ 0.75), the CatBoost classifier flags three high-confidence candidates. We find that the machine-learning classification is consistent with the diagnostics derived from the emission-line analysis.
SN - 0035-8711
SN - 1365-2966
ER -