Browsing by Author "Bouche, Nicolas"

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    An atlas of MUSE observations towards twelve massive lensing clusters
    (2021) Richard, Johan; Claeyssens, Adelaide; Lagattuta, David; Guaita, Lucia; Bauer, Franz Erik; Pello, Roser; Carton, David; Bacon, Roland; Soucail, Genevieve; Lyon, Gonzalo Prieto; Kneib, Jean-Paul; Mahler, Guillaume; Clement, Benjamin; Mercier, Wilfried; Variu, Andrei; Tamone, Amelie; Ebeling, Harald; Schmidt, Kasper B.; Nanayakkara, Themiya; Maseda, Michael; Weilbacher, Peter M.; Bouche, Nicolas; Bouwens, Rychard J.; Wisotzki, Lutz; de la Vieuville, Geoffroy; Martinez, Johany; Patricio, Vera
    Context. Spectroscopic surveys of massive galaxy clusters reveal the properties of faint background galaxies thanks to the magnification provided by strong gravitational lensing.Aims. We present a systematic analysis of integral-field-spectroscopy observations of 12 massive clusters, conducted with the Multi Unit Spectroscopic Explorer (MUSE). All data were taken under very good seeing conditions (similar to 0 ''.6) in effective exposure times between two and 15 h per pointing, for a total of 125 h. Our observations cover a total solid angle of similar to 23 arcmin(2) in the direction of clusters, many of which were previously studied by the MAssive Clusters Survey, Frontier Fields (FFs), Grism Lens-Amplified Survey from Space and Cluster Lensing And Supernova survey with Hubble programmes. The achieved emission line detection limit at 5 sigma for a point source varies between (0.77-1.5) x 10(-18) erg s(-1) cm(-2) at 7000 angstrom.Methods. We present our developed strategy to reduce these observational data, detect continuum sources and line emitters in the datacubes, and determine their redshifts. We constructed robust mass models for each cluster to further confirm our redshift measurements using strong-lensing constraints, and identified a total of 312 strongly lensed sources producing 939 multiple images.Results. The final redshift catalogues contain more than 3300 robust redshifts, of which 40% are for cluster members and similar to 30% are for lensed Lyman-alpha emitters. Fourteen percent of all sources are line emitters that are not seen in the available HST images, even at the depth of the FFs (similar to 29 AB). We find that the magnification distribution of the lensed sources in the high-magnification regime (mu=2-25) follows the theoretical expectation of N(z) proportional to mu(-2). The quality of this dataset, number of lensed sources, and number of strong-lensing constraints enables detailed studies of the physical properties of both the lensing cluster and the background galaxies. The full data products from this work, including the datacubes, catalogues, extracted spectra, ancillary images, and mass models, are made available to the community.
  • No Thumbnail Available
    Item
    Galaxy Spectra neural Network (GaSNet). II. Using deep learning for spectral classification and redshift predictions
    (2024) Zhong, Fucheng; Napolitano, Nicola R.; Heneka, Caroline; Li, Rui; Bauer, Franz Erik; Bouche, Nicolas; Comparat, Johan; Kim, Young-Lo; Krogager, Jens-Kristian; Longhetti, Marcella; Loveday, Jonathan; Roukema, Boudewijn F.; Rouse, Benedict L.; Salvato, Mara; Tortora, Crescenzo; Assef, Roberto J.; Cassara, Letizia P.; Costantin, Luca; Croom, Scott M.; Davies, Luke J. M.; Fritz, Alexander; Guiglion, Guillaume; Humphrey, Andrew; Pompei, Emanuela; Ricci, Claudio; Sifon, Cristobal; Tempel, Elmo; Zafar, Tayyaba
    The size and complexity reached by the large sky spectroscopic surveys require efficient, accurate, and flexible automated tools for data analysis and science exploitation. We present the Galaxy Spectra Network/GaSNet-II, a supervised multinetwork deep learning tool for spectra classification and redshift prediction. GaSNet-II can be trained to identify a customized number of classes and optimize the redshift predictions. Redshift errors are determined via an ensemble/pseudo-Monte Carlo test obtained by randomizing the weights of the network-of-networks structure. As a demonstration of the capability of GaSNet-II, we use 260k Sloan Digital Sky Survey spectra from Data Release 16, separated into 13 classes including 140k galactic, and 120k extragalactic objects. GaSNet-II achieves 92.4 per cent average classification accuracy over the 13 classes and mean redshift errors of approximately 0.23 per cent for galaxies and 2.1 per cent for quasars. We further train/test the pipeline on a sample of 200k 4MOST (4-metre Multi-Object Spectroscopic Telescope) mock spectra and 21k publicly released DESI (Dark Energy Spectroscopic Instrument) spectra. On 4MOST mock data, we reach 93.4 per cent accuracy in 10-class classification and mean redshift error of 0.55 per cent for galaxies and 0.3 per cent for active galactic nuclei. On DESI data, we reach 96 per cent accuracy in (star/galaxy/quasar only) classification and mean redshift error of 2.8 per cent for galaxies and 4.8 per cent for quasars, despite the small sample size available. GaSNet-II can process similar to 40k spectra in less than one minute, on a normal Desktop GPU. This makes the pipeline particularly suitable for real-time analyses and feedback loops for optimization of Stage-IV survey observations.