Euclid: Identification of asteroid streaks in simulated images using StreakDet software

TL;DR

This paper presents the development and testing of StreakDet software for automated detection of asteroid streaks in simulated Euclid telescope images, achieving high completeness but with false positives.

Contribution

The study introduces an optimized pipeline using StreakDet and post-processing to detect asteroid streaks in Euclid images, improving detection accuracy and reducing false positives.

Findings

StreakDet detects 96.9% of bright asteroid streaks longer than 15 pixels.

False positives are significantly reduced with multi-streak analysis.

Room remains for detecting shorter and fainter streaks.

Abstract

The ESA Euclid space telescope could observe up to 150 000 asteroids as a side product of its primary cosmological mission. Asteroids appear as trailed sources, that is streaks, in the images. Owing to the survey area of 15 000 square degrees and the number of sources, automated methods have to be used to find them. Euclid is equipped with a visible camera, VIS (VISual imager), and a near-infrared camera, NISP (Near-Infrared Spectrometer and Photometer), with three filters. We aim to develop a pipeline to detect fast-moving objects in Euclid images, with both high completeness and high purity. We tested the StreakDet software to find asteroids from simulated Euclid images. We optimized the parameters of StreakDet to maximize completeness, and developed a post-processing algorithm to improve the purity of the sample of detected sources by removing false-positive detections. StreakDet finds 96.9% of the synthetic asteroid streaks with apparent magnitudes brighter than 23rd magnitude and streak lengths longer than 15 pixels ($10\,{\rm arcsec\,h^{-1}}$), but this comes at the cost of finding a high number of false positives. The number of false positives can be radically reduced with multi-streak analysis, which utilizes all four dithers obtained by Euclid. StreakDet is a good tool for identifying asteroids in Euclid images, but there is still room for improvement, in particular, for finding short (less than 13 pixels, corresponding to 8$\,{\rm arcsec\,h^{-1}}$) and/or faint streaks (fainter than the apparent magnitude of 23).