Chasing the Neutrino Blazar Candidates

In our study of the correlations between IceCube-detected neutrino events and γ -ray properties of blazars, we recognize the inherent challenges posed by the limited detection of neutrinos. In this paper, we explore few-shot learning to deal with the class imbalance and few-shot issues presented in the incremental version of the 12 yr Fermi-LAT γ -ray source catalog (4FGL_ DR3). Specifically, we train a triplet network to transform the blazars with neutrino emission (NBs) and nonblazar samples into an embedding space where their similarities can be measured. With two-way three-shot learning, 199 out of 3708 blazars without neutrino emission (non-NBs) are considered as the potential blazars emitting neutrinos (NB candidates, or NBCs for short), with a similarity score against NBs exceeding 98%. Moreover, the Kolmogorov–Smirnov test supports our identification of NBCs.