First Discovery and Confirmation of PN Candidates Found from AI and Deep Learning Techniques Applied to VPHAS+ Survey Data

TL;DR

This study demonstrates the successful use of AI, specifically a Swin-Transformer algorithm, to identify planetary nebula candidates in the VPHAS+ survey, confirmed through spectroscopic follow-up, revealing many previously missed faint PNe.

Contribution

It introduces a novel AI-based method for detecting elusive PNe in crowded Galactic Plane fields, improving discovery rates over traditional techniques.

Findings

70.97% of candidates confirmed as PNe or likely PNe

Spectroscopic follow-up validated AI candidate effectiveness

Discovery of new Hα sources including a SNR fragment

Abstract

Context. We have developed deep learning (DL) and AI-based tools to search extant narrow-band wide-field H$\alpha$ surveys of the Galactic Plane for elusive planetary nebulae (PNe) which are hidden in dense star fields towards the Galactic center. They are faint, low-surface brightness, usually resolved sources, which are not discovered by previous automatic searches that depend on photometric data for point-like sources. These sources are very challenging to find by traditional visual inspection in such crowded fields and many have been missed. We have successfully adopted a novel 'Swin-Transformer' AI algorithm, which we described in detail in the preceding Techniques paper (Paper I). Aims. Here, we present preliminary results from our first spectroscopic follow-up run for 31 top-quality PN candidates found by the algorithm from the high-resolution H$\alpha$ survey VPHAS+. This survey has not yet undergone extensive manual, systematic searching. Methods. Our candidate PNe were observed with the SpUpNIC spectrograph on the 1.9 m telescope at the South African Astronomical Observatory (SAAO) in June 2023. We performed standard IRAF spectroscopic reduction and then followed our normal HASH PN identification and classification procedures. Results. Our reduced spectra confirmed that these candidates include 22 true, likely, and possible PNe (70.97\%), 3 emission-line galaxies, 2 emission-line stars, 2 late-type star contaminants, and 2 other H$\alpha$ sources including a newly identified detached fragment of SNR RCW 84. We present the imaging and spectral data of these candidates and a preliminary analysis of their properties. These data provide strong input to help evaluate and refine the behavior of the AI algorithm when searching for PNe in wide-field H$\alpha$ surveys.