Deep Learning Framework for Enhanced Neutrino Reconstruction of Single-line Events in the ANTARES Telescope

TL;DR

The paper introduces N-Fit, a deep learning framework that significantly improves neutrino event reconstruction in the ANTARES telescope, especially for single-line low-energy events, by refining direction, position, and energy estimations.

Contribution

N-Fit is a novel neural network model that combines advanced deep learning techniques and transfer learning to enhance neutrino event reconstruction in ANTARES, outperforming traditional methods.

Findings

Improved zenithal and azimuthal angle estimation accuracy.

Enhanced energy estimation through transfer learning.

Significant reduction in reconstruction errors on simulations and data.

Abstract

We present the $N$-fit algorithm designed to improve the reconstruction of neutrino events detected by a single line of the ANTARES underwater telescope, usually associated with low energy neutrino events ($\sim$ 100 GeV). $N$-Fit is a neural network model that relies on deep learning and combines several advanced techniques in machine learning --deep convolutional layers, mixture density output layers, and transfer learning. This framework divides the reconstruction process into two dedicated branches for each neutrino event topology --tracks and showers-- composed of sub-models for spatial estimation --direction and position-- and energy inference, which later on are combined for event classification. Regarding the direction of single-line events, the $N$-Fit algorithm significantly refines the estimation of the zenithal angle, and delivers reliable azimuthal angle predictions that were previously unattainable with traditional $\chi^2$-fit methods. Improving on energy estimation of single-line events is a tall order; $N$-Fit benefits from transfer learning to efficiently integrate key characteristics, such as the estimation of the closest distance from the event to the detector. $N$-Fit also takes advantage from transfer learning in event topology classification by freezing convolutional layers of the pretrained branches. Tests on Monte Carlo simulations and data demonstrate a significant reduction in mean and median absolute errors across all reconstructed parameters. The improvements achieved by $N$-Fit highlight its potential for advancing multimessenger astrophysics and enhancing our ability to probe fundamental physics beyond the Standard Model using single-line events from ANTARES data.