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

Abstract

Abstract Planetary nebulae (PNe) are strong H α line emitters and a lot of new PNe discoveries have been made by the SuperCOSMOS AAO/UKST H α Survey (SHS) and the Isaac Newton Telescope Photometric H α Survey (IPHAS). However, their resulting list of candidates turned out to be heavily contaminated from H α-line mimics like young stellar objects (YSOs) and/or H ii regions. 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 ≥ 7.87 and J − H < 1.10; H − W2 ≥ 2.24 and J − H < 0.50; and Ks− W3 ≥ 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 H α 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.