Euclid: Identification of asteroid streaks in simulated images using deep learning
Abstract
Up to 150000 asteroids will be visible in the images of the ESA Euclid space telescope, and the instruments of Euclid offer multiband visual to near-infrared photometry and slitless spectra of these objects. Most asteroids will appear as streaks in the images. Due to the large number of images and asteroids, automated detection methods are needed. A non-machine-learning approach based on the StreakDet software was previously tested, but the results were not optimal for short and/or faint streaks. We set out to improve the capability to detect asteroid streaks in Euclid images by using deep learning. We built, trained, and tested a three-step machine-learning pipeline with simulated Euclid images. First, a convolutional neural network (CNN) detected streaks and their coordinates in full images, aiming to maximize the completeness (recall) of detections. Then, a recurrent neural network (RNN) merged snippets of long streaks detected in several parts by the CNN. Lastly, gradient-boosted trees (XGBoost) linked detected streaks between different Euclid exposures to reduce the number of false positives and improve the purity (precision) of the sample. The deep-learning pipeline surpasses the completeness and reaches a similar level of purity of a non-machine-learning pipeline based on the StreakDet software. Additionally, the deep-learning pipeline can detect asteroids 0.25-0.5 magnitudes fainter than StreakDet. The deep-learning pipeline could result in a 50% increase in the number of detected asteroids compared to the StreakDet software. There is still scope for further refinement, particularly in improving the accuracy of streak coordinates and enhancing the completeness of the final stage of the pipeline, which involves linking detections across multiple exposures.
Keywords
Cite
@article{arxiv.2310.03845,
title = {Euclid: Identification of asteroid streaks in simulated images using deep learning},
author = {M. Pöntinen and M. Granvik and A. A. Nucita and L. Conversi and B. Altieri and B. Carry and C. M. O'Riordan and D. Scott and N. Aghanim and A. Amara and L. Amendola and N. Auricchio and M. Baldi and D. Bonino and E. Branchini and M. Brescia and S. Camera and V. Capobianco and C. Carbone and J. Carretero and M. Castellano and S. Cavuoti and A. Cimatti and R. Cledassou and G. Congedo and Y. Copin and L. Corcione and F. Courbin and M. Cropper and A. Da Silva and H. Degaudenzi and J. Dinis and F. Dubath and X. Dupac and S. Dusini and S. Farrens and S. Ferriol and M. Frailis and E. Franceschi and M. Fumana and S. Galeotta and B. Garilli and W. Gillard and B. Gillis and C. Giocoli and A. Grazian and S. V. H. Haugan and W. Holmes and F. Hormuth and A. Hornstrup and K. Jahnke and M. Kümmel and S. Kermiche and A. Kiessling and T. Kitching and R. Kohley and M. Kunz and H. Kurki-Suonio and S. Ligori and P. B. Lilje and I. Lloro and E. Maiorano and O. Mansutti and O. Marggraf and K. Markovic and F. Marulli and R. Massey and E. Medinaceli and S. Mei and M. Melchior and Y. Mellier and M. Meneghetti and G. Meylan and M. Moresco and L. Moscardini and E. Munari and S. -M. Niemi and T. Nutma and C. Padilla and S. Paltani and F. Pasian and K. Pedersen and V. Pettorino and S. Pires and G. Polenta and M. Poncet and F. Raison and A. Renzi and J. Rhodes and G. Riccio and E. Romelli and M. Roncarelli and E. Rossetti and R. Saglia and D. Sapone and B. Sartoris and P. Schneider and A. Secroun and G. Seidel and S. Serrano and C. Sirignano and G. Sirri and L. Stanco and P. Tallada-Crespí and A. N. Taylor and I. Tereno and R. Toledo-Moreo and F. Torradeflot and I. Tutusaus and L. Valenziano and T. Vassallo and G. Verdoes Kleijn and Y. Wang and J. Weller and G. Zamorani and J. Zoubian and V. Scottez},
journal= {arXiv preprint arXiv:2310.03845},
year = {2023}
}
Comments
18 pages, 11 figures