Euclid

• Verfasst von: Pöntinen, Mikko [VerfasserIn]
• Verfasst von: Amendola, Luca [VerfasserIn]
• Verfasst von: Jahnke, Knud [VerfasserIn]
• Verfasst von: Seidel, Gregor [VerfasserIn]
• Titel: Euclid
• Titelzusatz: identification of asteroid streaks in simulated images using deep learning
• Verf.angabe: 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, and V. Scottez
• E-Jahr: 2023
• Jahr: November 2023
• Umfang: 18 S.
• Illustrationen: Illustrationen
• Fussnoten: Online veröffentlicht: 29 November 2023 ; Gesehen am 15.05.2024
• Titel Quelle: Enthalten in: Astronomy and astrophysics
• Ort Quelle: Les Ulis : EDP Sciences, 1969
• Jahr Quelle: 2023
• Band/Heft Quelle: 679(2023) vom: Nov., Artikel-ID A135, Seite 1-18
• ISSN Quelle: 1432-0746
• DOI: doi:10.1051/0004-6361/202347551
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1888708018

Abstract

The material composition of asteroids is an essential piece of knowledge in the quest to understand the formation and evolution of the Solar System. Visual to near-infrared spectra or multiband photometry is required to constrain the material composition of asteroids, but we currently have such data, especially in the near-infrared wavelengths, for only a limited number of asteroids. This is a significant limitation considering the complex orbital structures of the asteroid populations. Up to 150 000 asteroids will be visible in the images of the upcoming ESA <i>Euclid<i/> space telescope, and the instruments of <i>Euclid<i/> will offer multiband visual to near-infrared photometry and slitless near-infrared spectra of these objects. Most of the 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 Streak Det 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 <i>Euclid<i/> images by using deep learning. We built, trained, and tested a three-step machine-learning pipeline with simulated <i>Euclid<i/> 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 <i>Euclid<i/> 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.