Euclid: Identification of asteroid streaks in simulated images using deep learning

no code implementations • 5 Oct 2023 • 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

First, a convolutional neural network (CNN) detected streaks and their coordinates in full images, aiming to maximize the completeness (recall) of detections.

Testing Convolutional Neural Networks for finding strong gravitational lenses in KiDS

no code implementations • 12 Jul 2018 • C. E. Petrillo, C. Tortora, S. Chatterjee, G. Vernardos, L. V. E. Koopmans, G. Verdoes Kleijn, N. R. Napolitano, G. Covone, L. S. Kelvin, A. M. Hopkins

We have tested the ConvNet lens-finders on a sample of 21789 Luminous Red Galaxies (LRGs) selected from KiDS and we have analyzed and compared the results with our previous ConvNet lens-finder on the same sample.

Astrophysics of Galaxies Instrumentation and Methods for Astrophysics

Finding Strong Gravitational Lenses in the Kilo Degree Survey with Convolutional Neural Networks

3 code implementations • 24 Feb 2017 • C. E. Petrillo, C. Tortora, S. Chatterjee, G. Vernardos, L. V. E. Koopmans, G. Verdoes Kleijn, N. R. Napolitano, G. Covone, P. Schneider, A. Grado, J. McFarland

The volume of data that will be produced by new-generation surveys requires automatic classification methods to select and analyze sources.

Astrophysics of Galaxies

KiDS-450: Cosmological parameter constraints from tomographic weak gravitational lensing

no code implementations • 16 Jun 2016 • H. Hildebrandt, M. Viola, C. Heymans, S. Joudaki, K. Kuijken, C. Blake, T. Erben, B. Joachimi, D. Klaes, L. Miller, C. B. Morrison, R. Nakajima, G. Verdoes Kleijn, A. Amon, A. Choi, G. Covone, J. T. A. de Jong, A. Dvornik, I. Fenech Conti, A. Grado, J. Harnois-Déraps, R. Herbonnet, H. Hoekstra, F. Köhlinger, J. McFarland, A. Mead, J. Merten, N. Napolitano, J. A. Peacock, M. Radovich, P. Schneider, P. Simon, E. A. Valentijn, J. L. van den Busch, E. van Uitert, L. Van Waerbeke

We use shear measurements for nearly 15 million galaxies, determined with a new improved `self-calibrating' version of $lens$fit validated using an extensive suite of image simulations.

Cosmology and Nongalactic Astrophysics