Search Results for author: F. Courbin

Found 6 papers, 4 papers with code

Multi-band morpho-Spectral Component Analysis Deblending Tool (MuSCADeT): Deblending colourful objects

1 code implementation1 Mar 2016 R. Joseph, F. Courbin, J. -L. Starck

To confront our algorithm with real data, we use HST images of the strong lensing galaxy cluster MACS J1149+2223 and we show that MuSCADeT performs better than traditional profile-fitting techniques in deblending the foreground lensing galaxies from background lensed galaxies.

Instrumentation and Methods for Astrophysics Astrophysics of Galaxies

Sparse Lens Inversion Technique (SLIT): lens and source separability from linear inversion of the source reconstruction problem

2 code implementations24 Sep 2018 R. Joseph, F. Courbin, J. -L. Starck, S. Birrer

Our inversion technique for a fixed mass distribution can be incorporated in future lens modelling technique iterating over the lens mass parameters.

Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics

Time Delay Lens Modelling Challenge

no code implementations15 Jun 2020 X. Ding, T. Treu, S. Birrer, G. C. -F. Chen, J. Coles, P. Denzel, M. Frigo A. Galan, P. J. Marshall, M. Millon, A. More, A. J. Shajib, D. Sluse, H. Tak, D. Xu, M. W. Auger, V. Bonvin, H. Chand, F. Courbin, G. Despali, C. D. Fassnacht, D. Gilman, S. Hilbert, S. R. Kumar, Y. -Y. Lin, J. W. Park, P. Saha, S. Vegetti, L. Van de Vyvere, L. L. R. Williams

With this time delay lens modelling challenge we aim to assess the level of precision and accuracy of the modelling techniques that are currently fast enough to handle of order 50 lenses, via the blind analysis of simulated datasets.

Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics

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

no code implementations5 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.

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