Euclid: Detecting Solar System objects in Euclid images and classifying them using Kohonen self-organising maps

Nucita, A. A.; Conversi, L.; Verdier, A.; Franco, A.; Sacquegna, S.; Pöntinen, M.; Altieri, B.; Carry, B.; De Paolis, F.; Strafella, F.; Orofino, V.; Maiorano, M.; Kansal, V.; Vavrek, R. D.; Miluzio, M.; Granvik, Mikael; Testa, V.; Aghanim, N.; Andreon, S.; Auricchio, N.; Baldi, M.; Bardelli, S.; Branchini, E.; Brescia, M.; Brinchmann, J.; Camera, S.; Capobianco, V.; Carbone, C.; Carretero, J.; Casas, S.; Castellano, M.; Castignani, G.; Cavuoti, S.; Cimatti, A.; Congedo, G.; Conselice, C. J.; Copin, Y.; Courbin, F.; Courtois, H. M.; Da Silva, A.; Degaudenzi, H.; Di Giorgio, A. M.; Dinis, J.; Dubath, F.; Dupac, X.; Dusini, S.; Farina, M.; Farrens, S.; Ferriol, S.; Frailis, M.; Franceschi, E.; Fumana, M.; Galeotta, S.; Gillis, B.; Giocoli, C.; Gómez-Alvarez, P.; Grazian, A.; Grupp, F.; Haugan, S. V.H.; Hoar, J.; Holmes, W.; Hormuth, F.; Hornstrup, A.; Hudelot, P.; Jahnke, K.; Jhabvala, M.; Keihänen, E.; Kermiche, S.; Kiessling, A.; Kilbinger, M.; Kohley, R.; Kubik, B.; Kümmel, M.; Kurki-Suonio, H.; Laureijs, R.; Ligori, S.; Lilje, P. B.; Lindholm, V.; Lloro, I.; Maiorano, E.; Mansutti, O.; Marggraf, O.; Markovic, K.; Martinet, N.; Marulli, F.; Massey, R.; Masters, D. C.; Medinaceli, E.; Mei, S.; Mellier, Y.; Meneghetti, M.; Meylan, G.; Moresco, M.; Moscardini, L.; Nakajima, R.; Niemi, S. M.; Padilla, C.; Paltani, S.; Pasian, F.; Pedersen, K.; Pettorino, V.; Pires, S.; Polenta, G.; Poncet, M.; Popa, L. A.; Pozzetti, L.; Raison, F.; Rebolo, R.; Renzi, A.; Rhodes, J.; Riccio, G.; Romelli, E.; Roncarelli, M.; Rossetti, E.; Saglia, R.; Sapone, D.; Sartoris, B.; Schirmer, M.; Schneider, P.; Secroun, A.; Seidel, G.; Serrano, S.; Sirignano, C.; Sirri, G.; Skottfelt, J.; Stanco, L.; Steinwagner, J.; Tallada-Crespí, P.; Taylor, A. N.; Tereno, I.; Toledo-Moreo, R.; Torradeflot, F.; Tutusaus, I.; Valenziano, L.; Vassallo, T.; Verdoes Kleijn, G.; Veropalumbo, A.; Wang, Y.; Weller, J.; Zacchei, A.; Zucca, E.; Bolzonella, M.; Burigana, C.; Scottez, V.

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Euclid: Detecting Solar System objects in Euclid images and classifying them using Kohonen self-organising maps

Nucita, A. A.

Department of Mathematics and Physics E. De Giorgi, University of Salento, Via per Arnesano, CP-I93, 73100 Lecce, Italy; INFN, Sezione di Lecce, Via per Arnesano, CP-193, 73100 Lecce, Italy; INAF-Sezione di Lecce, c/o Dipartimento Matematica e Fisica, Via per Arnesano, 73100 Lecce, Italy.

Conversi, L.

European Space Agency/ESRIN, Largo Galileo Galilei 1, 00044 Frascati, Roma, Italy; ESAC/ESA, Camino Bajo del Castillo, s/n., Urb. Villafranca del Castillo, 28692 Villanueva de la Cañada, Madrid, Spain.

Verdier, A.

Institute of Physics, Laboratory of Astrophysics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Observatoire de Sauverny, 1290 Versoix, Switzerland.

Franco, A.

Sacquegna, S.

Pöntinen, M.

Altieri, B.

Carry, B.

De Paolis, F.

Strafella, F.

Orofino, V.

Maiorano, M.

Kansal, V.

Vavrek, R. D.

Miluzio, M.

Granvik, Mikael

Testa, V.

Aghanim, N.

Andreon, S.

Auricchio, N.

Baldi, M.

Bardelli, S.

Branchini, E.

Brescia, M.

Brinchmann, J.

Camera, S.

Capobianco, V.

Carbone, C.

Carretero, J.

Casas, S.

Castellano, M.

Castignani, G.

Cavuoti, S.

Cimatti, A.

Congedo, G.

Conselice, C. J.

Copin, Y.

Courbin, F.

Courtois, H. M.

Da Silva, A.

Degaudenzi, H.

Di Giorgio, A. M.

Dinis, J.

Dubath, F.

Dupac, X.

Dusini, S.

Farina, M.

Farrens, S.

Ferriol, S.

Frailis, M.

Franceschi, E.

Fumana, M.

Galeotta, S.

Gillis, B.

Giocoli, C.

Gómez-Alvarez, P.

Grazian, A.

Grupp, F.

Haugan, S. V.H.

Hoar, J.

Holmes, W.

Hormuth, F.

Hornstrup, A.

Hudelot, P.

Jahnke, K.

Jhabvala, M.

Keihänen, E.

Kermiche, S.

Kiessling, A.

Kilbinger, M.

Kohley, R.

Kubik, B.

Kümmel, M.

Kurki-Suonio, H.

Laureijs, R.

Ligori, S.

Lilje, P. B.

Lindholm, V.

Lloro, I.

Maiorano, E.

Mansutti, O.

Marggraf, O.

Markovic, K.

Martinet, N.

Marulli, F.

Massey, R.

Masters, D. C.

Medinaceli, E.

Mei, S.

Mellier, Y.

Meneghetti, M.

Meylan, G.

Moresco, M.

Moscardini, L.

Nakajima, R.

Niemi, S. M.

Padilla, C.

Paltani, S.

Pasian, F.

Pedersen, K.

Pettorino, V.

Pires, S.

Polenta, G.

Poncet, M.

Popa, L. A.

Pozzetti, L.

Raison, F.

Rebolo, R.

Renzi, A.

Rhodes, J.

Riccio, G.

Romelli, E.

Roncarelli, M.

Rossetti, E.

Saglia, R.

Sapone, D.

Sartoris, B.

Schirmer, M.

Schneider, P.

Secroun, A.

Seidel, G.

Serrano, S.

Sirignano, C.

Sirri, G.

Skottfelt, J.

Stanco, L.

Steinwagner, J.

Tallada-Crespí, P.

Taylor, A. N.

Tereno, I.

Toledo-Moreo, R.

Torradeflot, F.

Tutusaus, I.

Valenziano, L.

Vassallo, T.

Verdoes Kleijn, G.

Veropalumbo, A.

Wang, Y.

Weller, J.

Zacchei, A.

Zucca, E.

Bolzonella, M.

Burigana, C.

Scottez, V.

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2025 (English)In: Astronomy and Astrophysics, ISSN 0004-6361, E-ISSN 1432-0746, Vol. 694, article id A116Article in journal (Refereed) Published

Abstract [en]

The ESA Euclid mission will survey more than 14 000 deg2 of the sky in visible and near-infrared wavelengths, mapping the extragalactic sky to constrain our cosmological model of the Universe. Although the survey focusses on regions further than 15° from the ecliptic, it should allow for the detection of more than about 105 Solar System objects (SSOs). After simulating the expected signal from SSOs in Euclid images acquired with the visible camera (VIS), we describe an automated pipeline developed to detect moving objects with an apparent velocity in the range of 0.1–10″ h−1, typically corresponding to sources in the outer Solar System (from Centaurs to Kuiper-belt objects). In particular, the proposed detection scheme is based on SExtractor software and on applying a new algorithm capable of associating moving objects amongst different catalogues. After applying a suite of filters to improve the detection quality, we study the expected purity and completeness of the SSO detections. We also show how a Kohonen self-organising neural network can be successfully trained (in an unsupervised fashion) to classify stars, galaxies, and SSOs. By implementing an early-stopping method in the training scheme, we show that the network can be used in a predictive way, allowing one to assign the probability of each detected object being a member of each considered class.

Place, publisher, year, edition, pages

EDP Sciences , 2025. Vol. 694, article id A116

Keywords [en]

methods: data analysis, methods: numerical, comets: general, Kuiper belt: general, minor planets, asteroids: general, Oort Cloud

National Category

Astronomy, Astrophysics and Cosmology

Research subject

Onboard Space Systems

Identifiers

URN: urn:nbn:se:ltu:diva-111711DOI: 10.1051/0004-6361/202451767Scopus ID: 2-s2.0-85217693752OAI: oai:DiVA.org:ltu-111711DiVA, id: diva2:1939855

Note

Validerad;2025;Nivå 2;2025-02-24 (u5);

Full text license: CC BY 4.0;

For funding information, see: https://www.aanda.org/articles/aa/full_html/2025/02/aa51767-24/aa51767-24.html

Available from: 2025-02-24 Created: 2025-02-24 Last updated: 2025-02-24Bibliographically approved

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Granvik, Mikael

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