In the past decade, the IceCube observatory has established the presence of a diffuse flux of high-energy neutrinos (≥ 100 TeV to 10 PeV) that is consistent with an astrophysical origin [IceCube Collaboration 2013], [Aartsen et al. 2016]. The population of sources responsible for this flux remains largely unknown.
Among the candidate sources of neutrinos, blazars have recently been suggested as promising emitters of the high-energy events detected by IceCube [IceCube Collab. 2018]. Our recent studies have provided evidence of a statistically significant spatial correlation between blazars from the 5th Roma-BZCat catalog (5BZCat) [Massaro et al. 2015] and the IceCube southern [Buson et al. 2022 a,b], and northern [Buson et al. 2023] celestial hemisphere data.
In this contribution, we present a Python-based tool, that performs an extended unbinned likelihood maximization on the recently released public IceCube’s 10-year neutrino point source event sample [Aartsen et al. 2020]. Upon its development and testing phase, the software will be released publicly as an opensource and user-friendly code.