Euclid: Identification of asteroid streaks in simulated images using
  deep learning
M. Pöntinen M. Granvik A. A. Nucita L. Conversi B. Altieri B. Carry C. M. O'Riordan D. Scott N. Aghanim A. Amara L. Amendola N. Auricchio M. Baldi D. Bonino E. Branchini M. Brescia S. Camera V. Capobianco C. Carbone J. Carretero M. Castellano S. Cavuoti A. Cimatti R. Cledassou G. Congedo Y. Copin L. Corcione F. Courbin M. Cropper A. Da Silva H. Degaudenzi J. Dinis F. Dubath X. Dupac S. Dusini S. Farrens S. Ferriol M. Frailis E. Franceschi M. Fumana S. Galeotta B. Garilli W. Gillard B. Gillis C. Giocoli A. Grazian S. V. H. Haugan W. Holmes F. Hormuth A. Hornstrup K. Jahnke M. Kümmel S. Kermiche A. Kiessling T. Kitching R. Kohley M. Kunz H. Kurki-Suonio S. Ligori P. B. Lilje I. Lloro E. Maiorano O. Mansutti O. Marggraf K. Markovic F. Marulli R. Massey E. Medinaceli S. Mei M. Melchior Y. Mellier M. Meneghetti G. Meylan M. Moresco L. Moscardini E. Munari S. -M. Niemi T. Nutma C. Padilla S. Paltani F. Pasian K. Pedersen V. Pettorino S. Pires G. Polenta M. Poncet F. Raison A. Renzi J. Rhodes G. Riccio E. Romelli M. Roncarelli E. Rossetti R. Saglia D. Sapone B. Sartoris P. Schneider A. Secroun G. Seidel S. Serrano C. Sirignano G. Sirri L. Stanco P. Tallada-Crespí A. N. Taylor I. Tereno R. Toledo-Moreo F. Torradeflot I. Tutusaus L. Valenziano T. Vassallo G. Verdoes Kleijn Y. Wang J. Weller G. Zamorani J. Zoubian V. Scottez
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.