Compact planetary nebulae: Improved IR diagnostic criteria based on classification tree modelling

Abstract

Planetary nebulae (PNe) are strong H$\alpha$ line-emitters and a lot of new PNe discoveries have been made by the SuperCOSMOS AAO/UKST H$\alpha$ Survey (SHS) and the Isaac Newton Telescope Photometric H$\alpha$ Survey (IPHAS). However, the list of auto-generated H$\alpha$-excess candidates from these surveys as well as any photometric survey, prior to spectroscopic follow-up to confirm their nature, contains all varieties of H$\alpha$-line emitters like young stellar objects (YSOs), H II regions, compact PNe and emission line stars of all kinds. The aim of this work is to find new infrared criteria that can better distinguish compact PNe from their mimics using a machine learning approach and the photometric data from the Two-Micron All-Sky Survey and Wide-field Infrared Survey Explorer. Three classification tree models have been developed with the following colour criteria: W1-W4$\ge$7.87 and J-H$<$1.10; H-W2$\ge$2.24 and J-H$<$0.50; and Ks-W3$\ge$6.42 and J-H$<$1.31 providing a list of candidates, characterized by a high probability to be genuine PNe. The contamination of this list of candidates from Ha mimics is low but not negligible. By applying these criteria to the IPHAS list of PN candidates and the entire IPHAS and VPHAS+ DR2 catalogues, we find 141 sources, from which 92 are known PNe, 39 are new very likely compact PNe (without an available classification or uncertain) and 10 are classified as H II regions, Wolf-Rayet stars, AeBe stars and YSOs. The occurrence of false positive identifications in this technique is between 10 and 15 per cent.