Model-independent search for neutrino sources with the ANTARES neutrino telescope

TL;DR

This paper introduces a new, model-independent method using machine learning and pattern recognition to analyze the spatial distribution of neutrino candidates, aiming to detect potential sources of high-energy neutrinos across the sky.

Contribution

It presents a novel, flexible analysis technique that does not rely on specific emission models, enhancing the search for diverse neutrino source morphologies.

Findings

Detected a 2.5σ excess in ANTARES data

Observed a 2.1σ excess in IceCube data

Method proves sensitive to various source shapes

Abstract

A novel method to analyse the spatial distribution of neutrino candidates recorded with the ANTARES neutrino telescope is introduced, searching for an excess of neutrinos in a region of arbitrary size and shape from any direction in the sky. Techniques originating from the domains of machine learning, pattern recognition and image processing are used to purify the sample of neutrino candidates and for the analysis of the obtained skymap. In contrast to a dedicated search for a specific neutrino emission model, this approach is sensitive to a wide range of possible morphologies of potential sources of high-energy neutrino emission. The application of these methods to ANTARES data yields a large-scale excess with a post-trial significance of 2.5$\sigma$. Applied to public data from IceCube in its IC40 configuration, an excess consistent with the results from ANTARES is observed with a post-trial significance of 2.1$\sigma$.