KM3NeT sensitivity to low energy astrophysical neutrinos

TL;DR

This paper explores how KM3NeT, a neutrino telescope in the Mediterranean, can detect low-energy astrophysical neutrinos by characterizing environmental noise and applying data science tools to improve sensitivity to transient phenomena.

Contribution

It introduces a methodology for noise characterization and compares data science tools to enhance low-energy neutrino detection in KM3NeT.

Findings

Data science tools can improve noise discrimination.

Characterization of environmental noise aids in neutrino detection.

Potential to detect astrophysical transients like supernovae.

Abstract

KM3NeT, a new generation of neutrino telescope, is currently being deployed in the Mediterranean Sea. While its two sites, ORCA and ARCA, were respectively designed for the determination of neutrino mass hierarchy and high-energy neutrino astronomy, this contribution presents a study of the detection potential of KM3NeT in the MeV-GeV energy range. At these low energies, the data rate is dominated by low-energy atmospheric muons and environmental noise due to bioluminescence and K-40 decay. The goal of this study is to characterize the environmental noise in order to optimize the selection of low-energy neutrino interactions and increase the sensitivity of KM3NeT to transient astrophysical phenomena, such as close-by core-collapse Supernovae, solar flares, and extragalactic transients. In this contribution, we will study how using data science tools might improve the sensitivity of KM3NeT in these low-energy neutrino searches. We will first introduce the data sets and the different variables used to characterize KM3NeT's response to the environmental noise. We will then compare the efficiency of various tools in identifying different components in the environmental noise and in disentangling low-energy neutrino interactions from the background events. We will conclude with the implication of low-energy neutrinos for future astrophysical transient searches.