Learning Efficient Representations of Neutrino Telescope Events
This work addresses data analysis bottlenecks in neutrino astronomy, offering a domain-specific solution that is incremental in its method adaptation.
The paper tackled the challenge of analyzing high-dimensional, large-variance data from neutrino telescopes by introducing om2vec, a transformer-based variational autoencoder that learns compact latent representations, resulting in enhanced flexibility and improved computational efficiency for downstream tasks.
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 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 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.