Issue |
A&A
Volume 679, November 2023
|
|
---|---|---|
Article Number | A135 | |
Number of page(s) | 18 | |
Section | Numerical methods and codes | |
DOI | https://doi.org/10.1051/0004-6361/202347551 | |
Published online | 29 November 2023 |
Euclid: Identification of asteroid streaks in simulated images using deep learning★
1
Department of Physics,
PO Box 64,
00014
University of Helsinki, Finland
e-mail: mikko.pontinen@helsinki.fi
2
Division of Space Technology, Luleå University of Technology,
Box 848,
98128
Kiruna, Sweden
3
Department of Mathematics and Physics E. De Giorgi, University of Salento,
Via per Arnesano,
CP-I93,
73100
Lecce, Italy
4
INAF-Sezione di Lecce,
c/o Dipartimento Matematica e Fisica, Via per Arnesano,
73100
Lecce, Italy
5
INFN, Sezione di Lecce,
Via per Arnesano,
CP-193,
73100
Lecce, Italy
6
European Space Agency/ESRIN,
Largo Galileo Galilei 1,
00044
Frascati, Roma, Italy
7
ESAC/ESA, Camino Bajo del Castillo,
s/n, Urb. Villafranca del Castillo,
28692
Villanueva de la Cañada, Madrid, Spain
8
Université Côte d’Azur, Observatoire de la Côte d’Azur, CNRS, Laboratoire Lagrange,
Bd de l’Observatoire,
CS 34229,
06304
Nice Cedex 4, France
9
Max-Planck-Institut für Astrophysik,
Karl-Schwarzschild Str. 1,
85741
Garching, Germany
10
Departement of Physics and Astronomy, University of British Columbia,
Vancouver, BC
V6T 1Z1, Canada
11
Université Paris-Saclay, CNRS, Institut d’astrophysique spatiale,
91405
Orsay, France
12
Institute of Cosmology and Gravitation, University of Portsmouth,
Portsmouth
PO1 3FX, UK
13
Institut für Theoretische Physik, University of Heidelberg,
Philosophenweg 16,
69120
Heidelberg, Germany
14
INAF-Osservatorio di Astrofisica e Scienza dello Spazio di Bologna,
Via Piero Gobetti 93/3,
40129
Bologna, Italy
15
Dipartimento di Fisica e Astronomia “Augusto Righi” – Alma Mater Studiorum Università di Bologna,
via Piero Gobetti 93/2,
40129
Bologna, Italy
16
INFN-Sezione di Bologna,
Viale Berti Pichat 6/2,
40127
Bologna, Italy
17
INAF-Osservatorio Astrofisico di Torino,
Via Osservatorio 20,
10025
Pino Torinese (TO), Italy
18
Dipartimento di Fisica, Università di Genova,
Via Dodecaneso 33,
16146
Genova, Italy
19
INFN-Sezione di Genova,
Via Dodecaneso 33,
16146
Genova, Italy
20
Department of Physics “E. Pancini”, University Federico II,
Via Cinthia 6,
80126
Napoli, Italy
21
INAF-Osservatorio Astronomico di Capodimonte,
Via Moiariello 16,
80131
Napoli, Italy
22
Dipartimento di Fisica, Università degli Studi di Torino,
Via P. Giuria 1,
10125
Torino, Italy
23
INFN-Sezione di Torino,
Via P. Giuria 1,
10125
Torino, Italy
24
INAF-IASF Milano,
Via Alfonso Corti 12,
20133
Milano, Italy
25
Institut de Física d’Altes Energies (IFAE), The Barcelona Institute of Science and Technology,
Campus UAB,
08193
Bellaterra (Barcelona), Spain
26
Port d’Informació Científica,
Campus UAB, C. Albareda s/n,
08193
Bellaterra (Barcelona), Spain
27
INAF-Osservatorio Astronomico di Roma,
Via Frascati 33,
00078
Monteporzio Catone, Italy
28
INFN section of Naples,
Via Cinthia 6,
80126
Napoli, Italy
29
Dipartimento di Fisica e Astronomia “Augusto Righi” – Alma Mater Studiorum Università di Bologna,
Viale Berti Pichat 6/2,
40127
Bologna, Italy
30
Centre National d’Etudes Spatiales – Centre spatial de Toulouse,
18 avenue Edouard Belin,
31401
Toulouse Cedex 9, France
31
Institut national de physique nucléaire et de physique des particules,
3 rue Michel-Ange,
75794
Paris Cedex 16, France
32
Institute for Astronomy, University of Edinburgh, Royal Observatory,
Blackford Hill,
Edinburgh
EH9 3HJ, UK
33
University of Lyon, Univ. Claude Bernard Lyon 1, CNRS/IN2P3, IP2I Lyon, UMR 5822,
69622
Villeurbanne, France
34
Institute of Physics, Laboratory of Astrophysics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Observatoire de Sauverny,
1290
Versoix, Switzerland
35
Mullard Space Science Laboratory, University College London,
Holmbury St Mary, Dorking,
Surrey
RH5 6NT, UK
36
Departamento de Física, Faculdade de Ciências, Universidade de Lisboa,
Edifício C8, Campo Grande,
1749-016
Lisboa, Portugal
37
Instituto de Astrofísica e Ciências do Espaço, Faculdade de Ciências, Universidade de Lisboa,
Campo Grande,
1749-016
Lisboa, Portugal
38
Department of Astronomy, University of Geneva,
ch. d’Ecogia 16,
1290
Versoix, Switzerland
39
INFN-Padova,
Via Marzolo 8,
35131
Padova, Italy
40
Université Paris-Saclay, Université Paris Cité, CEA, CNRS, Astrophysique, Instrumentation et Modélisation Paris-Saclay,
91191
Gif-sur-Yvette, France
41
INAF-Osservatorio Astronomico di Trieste,
Via G. B. Tiepolo 11,
34143
Trieste, Italy
42
Aix-Marseille Université, CNRS/IN2P3, CPPM,
Marseille, France
43
INAF-Osservatorio Astronomico di Padova,
Via dell’Osservatorio 5,
35122
Padova, Italy
44
Institute of Theoretical Astrophysics, University of Oslo,
PO Box 1029
Blindern,
0315
Oslo, Norway
45
Jet Propulsion Laboratory, California Institute of Technology,
4800 Oak Grove Drive,
Pasadena, CA,
91109, USA
46
von Hoerner & Sulger GmbH,
SchloßPlatz 8,
68723
Schwetzingen, Germany
47
Technical University of Denmark,
Elektrovej 327,
2800 Kgs.
Lyngby, Denmark
48
Cosmic Dawn Center (DAWN),
Denmark
49
Max-Planck-Institut für Astronomie,
Königstuhl 17,
69117
Heidelberg, Germany
50
Universitäts-Sternwarte München, Fakultät für Physik, Ludwig-Maximilians-Universität München,
Scheinerstrasse 1,
81679
München, Germany
51
Université de Genève, Département de Physique Théorique and Centre for Astroparticle Physics,
24 quai Ernest-Ansermet,
1211
Genève 4, Switzerland
52
Helsinki Institute of Physics,
Gustaf Hällströmin katu 2, University of Helsinki,
Helsinki, Finland
53
NOVA optical infrared instrumentation group at ASTRON,
Oude Hoogeveensedijk 4,
7991PD
Dwingeloo, The Netherlands
54
Universität Bonn, Argelander-Institut für Astronomie,
Auf dem Hügel 71,
53121
Bonn, Germany
55
Department of Physics, Institute for Computational Cosmology, Durham University,
South Road,
Durham
DH1 3LE, UK
56
Université Paris Cité, CNRS, Astroparticule et Cosmologie,
75013
Paris, France
57
University of Applied Sciences and Arts of Northwestern Switzerland, School of Engineering,
5210
Windisch, Switzerland
58
Institut d’Astrophysique de Paris,
98bis Boulevard Arago,
75014
Paris, France
59
Institut d’Astrophysique de Paris, UMR 7095, CNRS, and Sorbonne Université,
98 bis boulevard Arago,
75014
Paris, France
60
CEA Saclay, DFR/IRFU, Service d’Astrophysique,
Bât. 709,
91191
Gif-sur-Yvette, France
61
European Space Agency/ESTEC,
Keplerlaan 1,
2201 AZ
Noordwijk, The Netherlands
62
Kapteyn Astronomical Institute, University of Groningen,
PO Box 800,
9700 AV
Groningen, The Netherlands
63
Leiden Observatory, Leiden University,
Niels Bohrweg 2,
2333 CA
Leiden, The Netherlands
64
Department of Physics and Astronomy, University of Aarhus,
Ny Munkegade 120,
8000
Aarhus C, Denmark
65
Université Paris-Saclay, Université Paris Cité, CEA, CNRS, AIM,
91191
Gif-sur-Yvette, France
66
Space Science Data Center, Italian Space Agency,
via del Politecnico snc,
00133
Roma, Italy
67
Max Planck Institute for Extraterrestrial Physics,
Giessenbachstr. 1,
85748
Garching, Germany
68
Dipartimento di Fisica e Astronomia “G. Galilei”, Università di Padova,
Via Marzolo 8,
35131
Padova, Italy
69
Dipartimento di Fisica e Astronomia, Università di Bologna,
Via Gobetti 93/2,
40129
Bologna, Italy
70
Departamento de Física, FCFM, Universidad de Chile,
Blanco Encalada
2008,
Santiago, Chile
71
Institut d’Estudis Espacials de Catalunya (IEEC),
Carrer Gran Capitá 2–4,
08034
Barcelona, Spain
72
Institut de Ciencies de l’Espai (IEEC-CSIC), Campus UAB, Carrer de Can Magrans,
s/n Cerdanyola del Vallés,
08193
Barcelona, Spain
73
Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT),
Avenida Complutense 40,
28040
Madrid, Spain
74
Instituto de Astrofísica e Ciências do Espaço, Faculdade de Ciências, Universidade de Lisboa,
Tapada da Ajuda,
1349-018
Lisboa, Portugal
75
Universidad Politécnica de Cartagena, Departamento de Electrónica y Tecnología de Computadoras,
Plaza del Hospital 1,
30202
Cartagena, Spain
76
Institut de Recherche en Astrophysique et Planétologie (IRAP), Université de Toulouse, CNRS, UPS, CNES,
14 Av. Edouard Belin,
31400
Toulouse, France
77
INFN-Bologna,
Via Irnerio 46,
40126
Bologna, Italy
78
Infrared Processing and Analysis Center, California Institute of Technology,
Pasadena, CA
91125, USA
79
Junia, EPA department,
41 Bd Vauban,
59800
Lille, France
Received:
24
July
2023
Accepted:
25
September
2023
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 Euclid space telescope, and the instruments of Euclid 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 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.
Key words: methods: data analysis / techniques: image processing / minor planets, asteroids: general / space vehicles / surveys / methods: numerical
© The Authors 2023
Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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