A search for Elves in Mini-EUSO data using CNN-based one-class classifier

TL;DR

This paper introduces a fast, CNN-based one-class classifier to detect Elves, a transient luminous event, in Mini-EUSO space telescope data, enhancing the ability to identify these rare atmospheric phenomena.

Contribution

The paper presents a novel 3D CNN-based one-class classification method specifically designed for detecting Elves in space-based observational data.

Findings

High detection efficiency demonstrated in Mini-EUSO data

Effective filtering of Elves from other transient events

Improved identification accuracy over previous methods

Abstract

Mini-EUSO is a small, near-UV telescope observing the Earth and its atmosphere from the International Space Station. The time resolution of 2.5 microseconds and the instantaneous ground coverage of about $320\times 320$ km$^2$ allows it to detect some Transient Luminous Events, including Elves. Elves, with their almost circular shape and a radius expanding in time form cone-like structures in space-time, which are usually easy to be recognised by the eye, but not simple to filter out from the myriad of other events, many of them not yet categorised. In this work, we present a fast and efficient approach for detecting Elves in the data using a 3D CNN-based one-class classifier.