Learning Efficient Representations of Neutrino Telescope Events

TL;DR

This paper introduces om2vec, a transformer-based variational autoencoder that creates compact, descriptive representations of neutrino telescope events, improving analysis efficiency and flexibility in handling large, high-dimensional data.

Contribution

The paper presents a novel transformer-based autoencoder approach for efficient representation learning of neutrino telescope data, enhancing analysis capabilities.

Findings

Latent representations improve data analysis flexibility.

Enhanced computational efficiency in event reconstruction.

Better handling of high-dimensional, large-scale data.

Abstract

Neutrino telescopes detect rare interactions of particles produced in some of the most extreme environments in the Universe. This is accomplished by instrumenting a cubic-kilometer scale volume of naturally occurring transparent medium with light sensors. Given their substantial size and the high frequency of background interactions, these telescopes amass an enormous quantity of large variance, high-dimensional data. These attributes create substantial challenges for analyzing and reconstructing interactions, particularly when utilizing machine learning (ML) techniques. In this paper, we present a novel approach, called om2vec, that employs transformer-based variational autoencoders to efficiently represent the detected photon arrival time distributions of neutrino telescope events by learning compact and descriptive latent representations. We demonstrate that these latent representations offer enhanced flexibility and improved computational efficiency, thereby facilitating downstream tasks in data analysis.