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

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arxiv 2501.05023 v1 pith:H6VIAWF2 submitted 2025-01-09 astro-ph.IM

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

A. A. Nucita , L. Conversi , A. Verdier , A. Franco , S. Sacquegna , M. P\"ontinen , B. Altieri , B. Carry
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F. De Paolis F. Strafella V. Orofino M. Maiorano V. Kansal R. D. Vavrek M. Miluzio M. Granvik V. Testa N. Aghanim S. Andreon N. Auricchio M. Baldi S. Bardelli E. Branchini M. Brescia J. Brinchmann S. Camera V. Capobianco C. Carbone J. Carretero S. Casas M. Castellano G. Castignani S. Cavuoti A. Cimatti G. Congedo C. J. Conselice Y. Copin F. Courbin H. M. Courtois A. Da Silva H. Degaudenzi A. M. Di Giorgio J. Dinis F. Dubath X. Dupac S. Dusini M. Farina S. Farrens S. Ferriol M. Frailis E. Franceschi M. Fumana S. Galeotta B. Gillis C. Giocoli P. G\'omez-Alvarez A. Grazian F. Grupp S. V. H. Haugan J. Hoar W. Holmes F. Hormuth A. Hornstrup P. Hudelot K. Jahnke M. Jhabvala E. Keih\"anen S. Kermiche A. Kiessling M. Kilbinger R. Kohley B. Kubik M. K\"ummel H. Kurki-Suonio R. Laureijs S. Ligori P. B. Lilje V. Lindholm I. Lloro E. Maiorano O. Mansutti O. Marggraf K. Markovic N. Martinet F. Marulli R. Massey D. C. Masters E. Medinaceli S. Mei Y. Mellier M. Meneghetti G. Meylan M. Moresco L. Moscardini R. Nakajima S.-M. Niemi C. Padilla S. Paltani F. Pasian K. Pedersen V. Pettorino S. Pires G. Polenta M. Poncet L. A. Popa L. Pozzetti F. Raison R. Rebolo A. Renzi J. Rhodes G. Riccio E. Romelli M. Roncarelli E. Rossetti R. Saglia D. Sapone B. Sartoris M. Schirmer P. Schneider A. Secroun G. Seidel S. Serrano C. Sirignano G. Sirri J. Skottfelt L. Stanco J. Steinwagner P. Tallada-Cresp\'i A. N. Taylor I. Tereno R. Toledo-Moreo F. Torradeflot I. Tutusaus L. Valenziano T. Vassallo G. Verdoes Kleijn A. Veropalumbo Y. Wang J. Weller A. Zacchei E. Zucca M. Bolzonella C. Burigana V. Scottez
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classification astro-ph.IM
keywords objectseucliddetectionsolarssossystemapplyingexpected
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The ESA Euclid mission will survey more than 14,000 deg$^2$ of the sky in visible and near-infrared wavelengths, mapping the extra-galactic sky to constrain our cosmological model of the Universe. Although the survey focusses on regions further than 15 deg from the ecliptic, it should allow for the detection of more than about $10^5$ 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 arcsec/h, typically corresponding to sources in the outer Solar System (from Centaurs to Kuiper-belt objects). In particular, the proposed detection scheme is based on Sourcextractor 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.

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  1. Euclid preparation. CosmoPostProcess: A simulation calibrated framework for weak lensing selection bias in richness-selected galaxy clusters

    astro-ph.CO 2026-05 unverdicted novelty 6.0

    CosmoPostProcess delivers simulation-calibrated radial corrections for projection-induced selection bias (20-40% amplitude near 1 h^{-1} Mpc) and baryonic effects in Euclid richness-selected cluster weak lensing profiles.