AI techniques for uncovering resolved Planetary Nebula candidates from Wide-field VPHAS+ survey data

Abstract

AI and deep learning techniques are playing an increasing role in astronomy to deal with the data avalanche. Here we describe an application for finding resolved Planetary Nebulae (PNe) in crowded, wide-field, narrow-band Hα survey imagery in the Galactic plane, to test and facilitate more objective, reproducible, efficient and reliable trawls for them. PNe are important to study late-stage stellar evolution of low to intermediate-mass stars. However, the confirmed ∼3800 Galactic PNe fall far short of the numbers expected. Traditional visual searching for resolved PNe is time-consuming due to the large data size and areal coverage of modern astronomical surveys. The training and validation dataset of our algorithm was built with IPHAS survey and true PNe from the HASH database. Our algorithm correctly identified 444 PNe in the validation set of 454 ones, with only 16 explicable ‘false’ positives, achieving a precision rate of 96.5% and a recall rate of 97.8%. After transfer learning, it was then applied to VPHAS+ survey, examining 979 out of 2284 survey fields, each covering 1○ × 1○. It returned ∼20,000 detections, including 2637 known PNe and other kinds of catalogued non-PNe. A total of 815 new high-quality PNe candidates were found, 31 of which were selected as top-quality targets for optical spectroscopic follow-up. We found 74% of them are true, likely and possible PNe. Representative preliminary confirmatory spectroscopy results are presented here to demonstrate the effectiveness of our techniques with full details to be given in paper-II.