Euclid: Detecting Solar System objects in Euclid images and classifying them using Kohonen self-organising maps

TL;DR

This paper presents an automated pipeline for detecting and classifying Solar System objects in Euclid images using a novel detection algorithm and Kohonen neural networks, enabling efficient identification of SSOs in large sky surveys.

Contribution

It introduces a new detection scheme for SSOs in Euclid images and demonstrates the use of unsupervised neural networks for classifying celestial objects.

Findings

Detection of SSOs with 0.1-10 arcsec/h velocity range

High purity and completeness in SSO detection

Effective classification of stars, galaxies, and SSOs using Kohonen networks

Abstract

The ESA Euclid mission will survey more than 14,000 deg$^2$ of the sky in visible and near-infrared wavelengths, mapping the extra-galactic sky to constrain our cosmological model of the Universe. Although the survey focusses on regions further than 15 deg from the ecliptic, it should allow for the detection of more than about $10^5$ Solar System objects (SSOs). After simulating the expected signal from SSOs in Euclid images acquired with the visible camera (VIS), we describe an automated pipeline developed to detect moving objects with an apparent velocity in the range of 0.1-10 arcsec/h, typically corresponding to sources in the outer Solar System (from Centaurs to Kuiper-belt objects). In particular, the proposed detection scheme is based on Sourcextractor software and on applying a new algorithm capable of associating moving objects amongst different catalogues. After applying a suite of filters to improve the detection quality, we study the expected purity and completeness of the SSO detections. We also show how a Kohonen self-organising neural network can be successfully trained (in an unsupervised fashion) to classify stars, galaxies, and SSOs. By implementing an early-stopping method in the training scheme, we show that the network can be used in a predictive way, allowing one to assign the probability of each detected object being a member of each considered class.