Browsing by Author "Gavazzi, R."

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    The COSMOS-Web ring : In-depth characterization of an Einstein ring lensing system at z ∼ 2
    (EDP Sciences, 2024-07-01) Mercier, W.; Shuntov, M.; Gavazzi, R.; Nightingale, J. W.; Arango, R.; Ilbert, O.; Amvrosiadis, A.; Ciesla, L.; Casey, C. M.; Jin, S.; Faisst, A. L.; Andika, I. T.; Drakos, N. E.; Enia, A.; Franco, M.; Gillman, S.; Gozaliasl, G.; Hayward, C. C.; Huertas-Company, M.; Kartaltepe, J. S.; Koekemoer, A. M.; Laigle, C.; Le Borgne, D.; Magdis, G.; Mahler, G.; Maraston, C.; Martin, C. L.; Massey, R.; McCracken, H. J.; Moutard, T.; Paquereau, L.; Rhodes, J. D.; Robertson, B. E.; Sanders, D. B.; Toft, S.; Trebitsch, M.; Tresse, L.; Vijayan, A. P.; Department of Computer Science; Department of Applied Physics; Aix-Marseille University; Cosmic Dawn Center; Sorbonne Université; Durham University; California Institute of Technology; Technical University of Munich; University of Hawai'i at Hilo; Universitá di Bologna; University of Texas at Austin; Simons Foundation; Instituto de Astrofísica de Canarias; Rochester Institute of Technology; Space Telescope Science Institute; Institut d 'Astrophysique de Paris; University of Portsmouth; University of California, Santa Barbara; Sorbonne University; University of California, Santa Cruz; University of Hawai’i at Manoa; University of Groningen
    Aims. We provide an in-depth analysis of the COSMOS-Web ring, an Einstein ring at z ≈ 2 that we serendipitously discovered during the data reduction of the COSMOS-Web survey and that could be the most distant lens discovered to date. Methods. We extracted the visible and near-infrared photometry of the source and the lens from more than 25 bands. We combined these observations with far-infrared detections to study the dusty nature of the source and we derived the photometric redshifts and physical properties of both the lens and the source with three different spectral energy distribution (SED) fitting codes. Using JWST/NIRCam images, we also produced two lens models to (i) recover the total mass of the lens, (ii) derive the magnification of the system, (iii) reconstruct the morphology of the lensed source, and (iv) measure the slope of the total mass density profile of the lens. Results. We find the lens to be a very massive elliptical galaxy at z = 2.02 ± 0.02 with a total mass within the Einstein radius of Mtot(<θEin = (3.66 ± 0.36) × 1011 M· and a total stellar mass of M∗ = 1.37-0.11+0.14 × 1011 M·. We also estimate it to be compact and quiescent with a specific star formation rate below 10-13 yr. Compared to stellar-to-halo mass relations from the literature, we find that the total mass of the lens within the Einstein radius is consistent with the presence of a dark matter (DM) halo of total mass Mh = 1.09-0.57+1.46 × 1013 M·. In addition, the background source is a M∗ = (1.26 ± 0.17) × 1010 M· star-forming galaxy (SFR ≈ (78 ± 15) M· yr) at z = 5.48 ± 0.06. The morphology reconstructed in the source plane shows two clear components with different colors. Dust attenuation values from SED fitting and nearby detections in the far infrared also suggest that the background source could be at least partially dust-obscured. Conclusions. We find the lens at z ≈ 2. Its total, stellar, and DM halo masses are consistent within the Einstein ring, so we do not need any unexpected changes in our description of the lens such as changing its initial mass function or including a non-negligible gas contribution. The most likely solution for the lensed source is at z ≈ 5.5. Its reconstructed morphology is complex and highly wavelength dependent, possibly because it is a merger or a main sequence galaxy with a heterogeneous dust distribution.
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    Euclid preparation : XXXIII. Characterization of convolutional neural networks for the identification of galaxy-galaxy strong-lensing events
    (EDP Sciences, 2024-01-01) Leuzzi, L.; Meneghetti, M.; Angora, G.; Metcalf, R. B.; Moscardini, L.; Rosati, P.; Bergamini, P.; Calura, F.; Clément, B.; Gavazzi, R.; Gentile, F.; Lochner, M.; Grillo, C.; Vernardos, G.; Aghanim, N.; Amara, A.; Amendola, L.; Auricchio, N.; Bodendorf, C.; Bonino, D.; Branchini, E.; Brescia, M.; Brinchmann, J.; Camera, S.; Capobianco, V.; Carbone, C.; Carretero, J.; Castellano, M.; Cavuoti, S.; Cimatti, A.; Cledassou, R.; Congedo, G.; Conselice, C. J.; Conversi, L.; Copin, Y.; Corcione, L.; Courbin, F.; Cropper, M.; Da Silva, A.; Degaudenzi, H.; Dinis, J.; Dubath, F.; Dupac, X.; Dusini, S.; Farrens, S.; Niemi, S. M.; Schneider, P.; Wang, Y.; Gozaliasl, G.; Sánchez, A. G.; , Euclid Collaboration; Department of Computer Science; Universitá di Bologna; Istituto di Astrofisica Spaziale e Fisica Cosmica di Bologna; University of Ferrara; Swiss Federal Institute of Technology Lausanne; Aix-Marseille Université; University of the Western Cape; University of Milan; Université Paris-Saclay; University of Portsmouth; Heidelberg University ; Max Planck Institute for Extraterrestrial Physics; National Institute for Astrophysics (INAF); University of Genoa; Osservatorio Astronomico di Capodimonte; Universidade do Porto; Istituto Nazionale di Astrofisica (INAF); Institute for High Energy Physics; Osservatorio Astronomico di Roma; Centre national d'études spatiales; University of Edinburgh; University of Manchester; ESRIN - ESA Centre for Earth Observation; Université Claude Bernard Lyon 1; University College London; University of Lisbon; University of Geneva; Urbanización Villafranca Del Castillo; National Institute for Nuclear Physics; European Space Research and Technology Centre; University of Bonn; California Institute of Technology
    Forthcoming imaging surveys will increase the number of known galaxy-scale strong lenses by several orders of magnitude. For this to happen, images of billions of galaxies will have to be inspected to identify potential candidates. In this context, deep-learning techniques are particularly suitable for finding patterns in large data sets, and convolutional neural networks (CNNs) in particular can efficiently process large volumes of images. We assess and compare the performance of three network architectures in the classification of strong-lensing systems on the basis of their morphological characteristics. In particular, we implemented a classical CNN architecture, an inception network, and a residual network. We trained and tested our networks on different subsamples of a data set of 40 000 mock images whose characteristics were similar to those expected in the wide survey planned with the ESA mission Euclid, gradually including larger fractions of faint lenses. We also evaluated the importance of adding information about the color difference between the lens and source galaxies by repeating the same training on single- and multiband images. Our models find samples of clear lenses with 90% precision and completeness. Nevertheless, when lenses with fainter arcs are included in the training set, the performance of the three models deteriorates with accuracy values of ~0.87 to ~0.75, depending on the model. Specifically, the classical CNN and the inception network perform similarly in most of our tests, while the residual network generally produces worse results. Our analysis focuses on the application of CNNs to high-resolution space-like images, such as those that the Euclid telescope will deliver. Moreover, we investigated the optimal training strategy for this specific survey to fully exploit the scientific potential of the upcoming observations. We suggest that training the networks separately on lenses with different morphology might be needed to identify the faint arcs. We also tested the relevance of the color information for the detection of these systems, and we find that it does not yield a significant improvement. The accuracy ranges from ~0.89 to ~0.78 for the different models. The reason might be that the resolution of the Euclid telescope in the infrared bands is lower than that of the images in the visual band.