Ponente
Descripción
KM3NeT is a next-generation neutrino telescope currently under construction in the Mediterranean Sea. It consists of two detectors, ARCA and ORCA, both equipped with multi-PMT optical modules designed to detect the Cherenkov light produced by charged particles originating from neutrino interactions in the surrounding medium. ARCA, optimized for energies from TeV to PeV, is dedicated to the study of cosmic neutrinos, while ORCA focuses on atmospheric neutrino oscillations in the GeV energy range. Despite not yet being fully completed, KM3NeT is already taking data with partial configurations, such as ORCA18, which comprises 18 detection units.
In this work, we explore the detection prospects for a Beyond Standard Model (BSM) particle known as the Heavy Neutral Lepton (HNL). The HNL signature left in ORCA is particularly distinctive: it is expected to produce two spatially separated cascades of light, an event topology not anticipated from any Standard Model process in the same energy regime. Using a dedicated simulation based on the SIREN lepton injector to model HNL signals in KM3NeT/ORCA18, we assess the potential of modern Deep Learning techniques - such as ParticleNeT - together with Boosted Decision Trees (BDTs) implemented with the XGBoost library, to reconstruct and discriminate this unique signal.
