Search for PN candidates with machine learning

However, their resulting list of candidates turned out to be heavily contaminated from H{alpha}-line mimics like young stellar objects (YSOs) and/or HII 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: W_1_-W_4_>=7.87 and J-H<1.10; H-W_2_>=2.24 and J-H<0.50; and K_s_-W_3_>=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{alpha} 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 HII regions, Wolf-Rayet stars, AeBe stars, and YSOs.