☆ 4.7 Article

GLAMER - I. A code for gravitational lensing simulations with adaptive mesh refinement

MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY (2014)

Journal

MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY

Volume 445, Issue 2, Pages 1942-1953

Publisher

OXFORD UNIV PRESS

DOI: 10.1093/mnras/stu1859

Keywords

gravitational lensing: strong; gravitational lensing: weak; gravitational lensing: micro; methods: numerical; cosmology: miscellaneous

Categories

Astronomy & Astrophysics

Funding

GLENCO [259349]

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Abstract

A computer code is described for the simulation of gravitational lensing data. The code incorporates adaptive mesh refinement in choosing which rays to shoot based on the requirements of the source size, location and surface brightness distribution or to find critical curves/caustics. A variety of source surface brightness models are implemented to represent galaxies and quasar emission regions. The lensing mass can be represented by point masses (stars), smoothed simulation particles, analytic halo models, pixelized mass maps or any combination of these. The deflection and beam distortions (convergence and shear) are calculated by modified tree algorithm when haloes, point masses or particles are used and by fast Fourier transform when mass maps are used. The combination of these methods allow for a very large dynamical range to be represented in a single simulation. Individual images of galaxies can be represented in a simulation that covers many square degrees. For an individual strongly lensed quasar, source sizes from the size of the quasar's host galaxy (similar to 100 kpc) down to microlensing scales (similar to 10(-4) pc) can be probed in a self-consistent simulation. Descriptions of various tests of the code's accuracy are given.

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Article Astronomy & Astrophysics

Euclid preparation XVII. Cosmic Dawn Survey: Spitzer Space Telescope observations of the Euclid deep fields and calibration fields

A. Moneti, H. J. McCracken, M. Shuntov, O. B. Kauffmann, P. Capak, I Davidzon, O. Ilbert, C. Scarlata, S. Toft, J. Weaver, R. Chary, J. Cuby, A. L. Faisst, D. C. Masters, C. McPartland, B. Mobasher, D. B. Sanders, R. Scaramella, D. Stern, I Szapudi, H. Teplitz, L. Zalesky, A. Amara, N. Auricchio, C. Bodendorf, D. Bonino, E. Branchini, S. Brau-Nogue, M. Brescia, J. Brinchmann, V Capobianco, C. Carbone, J. Carretero, F. J. Castander, M. Castellano, S. Cavuoti, A. Cimatti, R. Cledassou, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, L. Corcione, A. Costille, M. Cropper, A. Da Silva, H. Degaudenzi, M. Douspis, F. Dubath, C. A. J. Duncan, X. Dupac, S. Dusini, S. Farrens, S. Ferriol, P. Fosalba, M. Frailis, E. Franceschi, M. Fumana, B. Garilli, B. Gillis, C. Giocoli, B. R. Granett, A. Grazian, F. Grupp, S. V. H. Haugan, H. Hoekstra, W. Holmes, F. Hormuth, P. Hudelot, K. Jahnke, S. Kermiche, A. Kiessling, M. Kilbinger, T. Kitching, R. Kohley, M. Kuemmel, M. Kunz, H. Kurki-Suonio, S. Ligori, P. B. Lilje, I Lloro, E. Maiorano, O. Mansutti, O. Marggraf, K. Markovic, F. Marulli, R. Massey, S. Maurogordato, M. Meneghetti, E. Merlin, G. Meylan, M. Moresco, L. Moscardini, E. Munari, S. M. Niemi, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, S. Pires, M. Poncet, L. Popa, L. Pozzetti, F. Raison, R. Rebolo, J. Rhodes, H. Rix, M. Roncarelli, E. Rossetti, R. Saglia, P. Schneider, A. Secroun, G. Seidel, S. Serrano, C. Sirignano, G. Sirri, L. Stanco, P. Tallada-Crespi, A. N. Taylor, I Tereno, R. Toledo-Moreo, F. Torradeflot, Y. Wang, N. Welikala, J. Weller, G. Zamorani, J. Zoubian, S. Andreon, S. Bardelli, S. Camera, J. Gracia-Carpio, E. Medinaceli, S. Mei, G. Polenta, E. Romelli, F. Sureau, M. Tenti, T. Vassallo, A. Zacchei, E. Zucca, C. Baccigalupi, A. Balaguera-Antolinez, F. Bernardeau, A. Biviano, M. Bolzonella, E. Bozzo, C. Burigana, R. Cabanac, A. Cappi, C. S. Carvalho, S. Casas, G. Castignani, C. Colodro-Conde, J. Coupon, H. M. Courtois, D. Di Ferdinando, M. Farina, F. Finelli, P. Flose-Reimberg, S. Fotopoulou, S. Galeotta, K. Ganga, J. Garcia-Bellido, E. Gaztanaga, G. Gozaliasl, I Hook, B. Joachimi, V Kansal, E. Keihanen, C. C. Kirkpatrick, V Lindholm, G. Mainetti, D. Maino, R. Maoli, M. Martinelli, N. Martinet, M. Maturi, R. B. Metcalf, G. Morgante, N. Morisset, A. Nucita, L. Patrizii, D. Potter, A. Renzi, G. Riccio, A. G. Sanchez, D. Sapone, M. Schirmer, M. Schultheis, V Scottez, E. Sefusatti, R. Teyssier, O. Tubio, I Tutusaus, J. Valiviita, M. Viel, H. Hildebrandt

Summary: This study presents a new infrared survey using the Spitzer Space Telescope's IRAC to observe the Euclid deep fields and calibration fields. The resulting mosaics are the deepest possible, covering a large area with precise astrometry and photometric calibration. With the decommissioning of the Spitzer Space Telescope, these mosaics are likely to be the definitive reduction of the IRAC data, making them an essential first step in assembling multi-wavelength data for future extragalactic astronomy research.

ASTRONOMY & ASTROPHYSICS (2022)

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Article Astronomy & Astrophysics

Euclid preparation XIII. Forecasts for galaxy morphology with the Euclid Survey using deep generative models

H. Bretonniere, M. Huertas-Company, A. Boucaud, F. Lanusse, E. Jullo, E. Merlin, D. Tuccillo, M. Castellano, J. Brinchmann, C. J. Conselice, H. Dole, R. Cabanac, H. M. Courtois, F. J. Castander, P. A. Duc, P. Fosalba, D. Guinet, S. Kruk, U. Kuchner, S. Serrano, E. Soubrie, A. Tramacere, L. Wang, A. Amara, N. Auricchio, R. Bender, C. Bodendorf, D. Bonino, E. Branchini, S. Brau-Nogue, M. Brescia, V. Capobianco, C. Carbone, J. Carretero, S. Cavuoti, A. Cimatti, R. Cledassou, G. Congedo, L. Conversi, Y. Copin, L. Corcione, A. Costille, M. Cropper, A. Da Silva, H. Degaudenzi, M. Douspis, F. Dubath, C. A. J. Duncan, X. Dupac, S. Dusini, S. Farrens, S. Ferriol, M. Frailis, E. Franceschi, M. Fumana, B. Garilli, W. Gillard, B. Gillis, C. Giocoli, A. Grazian, F. Grupp, S. V. H. Haugan, W. Holmes, F. Hormuth, P. Hudelot, K. Jahnke, S. Kermiche, A. Kiessling, M. Kilbinger, T. Kitching, R. Kohley, M. Kuemmel, M. Kunz, H. Kurki-Suonio, S. Ligori, P. B. Lilje, I. Lloro, E. Maiorano, O. Mansutti, O. Marggraf, K. Markovic, F. Marulli, R. Massey, S. Maurogordato, M. Melchior, M. Meneghetti, G. Meylan, M. Moresco, B. Morin, L. Moscardini, E. Munari, R. Nakajima, S. M. Niemi, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, V. Pettorino, S. Pires, M. Poncet, L. Popa, L. Pozzetti, F. Raison, R. Rebolo, J. Rhodes, M. Roncarelli, E. Rossetti, R. Saglia, P. Schneider, A. Secroun, G. Seidel, C. Sirignano, G. Sirri, L. Stanco, J. -L. Starck, P. Tallada-Crespi, A. N. Taylor, I. Tereno, R. Toledo-Moreo, F. Torradeflot, E. A. Valentijn, L. Valenziano, Y. Wang, N. Welikala, J. Weller, G. Zamorani, J. Zoubian, M. Baldi, S. Bardelli, S. Camera, R. Farinelli, E. Medinaceli, S. Mei, G. Polenta, E. Romelli, M. Tenti, T. Vassallo, A. Zacchei, E. Zucca, C. Baccigalupi, A. Balaguera-Antolinez, A. Biviano, S. Borgani, E. Bozzo, C. Burigana, A. Cappi, C. S. Carvalho, S. Casas, G. Castignani, C. Colodro-Conde, J. Coupon, S. de la Torre, M. Fabricius, M. Farina, P. G. Ferreira, P. Flose-Reimberg, S. Fotopoulou, S. Galeotta, K. Ganga, J. Garcia-Bellido, E. Gaztanaga, G. Gozaliasl, I. M. Hook, B. Joachimi, V. Kansal, A. Kashlinsky, E. Keihanen, C. C. Kirkpatrick, V. Lindholm, G. Mainetti, D. Maino, R. Maoli, M. Martinelli, N. Martinet, H. J. McCracken, R. B. Metcalf, G. Morgante, N. Morisset, J. Nightingale, A. Nucita, L. Patrizii, D. Potter, A. Renzi, G. Riccio, A. G. Sanchez, D. Sapone, M. Schirmer, M. Schultheis, V. Scottez, E. Sefusatti, R. Teyssier, I. Tutusaus, J. Valiviita, M. Viel, L. Whittaker, J. H. Knapen

Summary: We present a machine learning framework to simulate more realistic galaxies for the Euclid Survey. The proposed method combines control on galaxy shape parameters from analytic models with realistic surface brightness distributions learned from real observations. Based on simulations, the researchers estimate that the Euclid Survey will be able to resolve internal morphological structure of galaxies and provide a complete sample of approximately 250 million galaxies.

ASTRONOMY & ASTROPHYSICS (2022)

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Article Astronomy & Astrophysics

SEAGLE III: Towards resolving the mismatch in the dark-matter fraction in early-type galaxies between simulations and observations

Sampath Mukherjee, Leon V. E. Koopmans, Crescenzo Tortora, Matthieu Schaller, R. Benton Metcalf, Joop Schaye, Georgios Vernardos

Summary: The central dark-matter fraction of galaxies, influenced by feedback processes during formation, has been accurately measured using strong gravitational lensing techniques. Comparisons of observational data with simulations show good agreement between the EAGLE and SLACS results, while results from IllustrisTNG and Illustris simulations are lower than observed values. Differences in the stellar feedback model are likely the main cause of this discrepancy.

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Developing a victorious strategy to the second strong gravitational lensing data challenge

C. R. Bom, B. M. O. Fraga, L. O. Dias, P. Schubert, M. Blanco Valentin, C. Furlanetto, M. Makler, K. Teles, M. Portes de Albuquerque, R. Benton Metcalf

Summary: This paper presents the deep learning strategies and methodology used to design the highest scoring algorithm in the second Strong Gravitational Lensing Challenge (SGLC). The study discusses the approach of using a network with two branches to handle images of different resolutions, as well as the optimization process. The research is significant for efficient, adaptable, and accurate analyses of strong lenses using deep learning frameworks.

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Article Astronomy & Astrophysics

Euclid preparation XXV. The Euclid Morphology Challenge: Towards model-fitting photometry for billions of galaxies

E. Merlin, M. Castellano, H. Bretonniere, M. Huertas-Company, U. Kuchner, D. Tuccillo, F. Buitrago, J. R. Peterson, C. J. Conselice, F. Caro, P. Dimauro, L. Nemani, A. Fontana, M. Kuemmel, B. Haussler, W. G. Hartley, A. Alvarez Ayllon, E. Bertin, P. Dubath, F. Ferrari, L. Ferreira, R. Gavazzi, D. Hernandez-Lang, G. Lucatelli, A. S. G. Robotham, M. Schefer, C. Tortora, N. Aghanim, A. Amara, L. Amendola, N. Auricchio, M. Baldi, R. Bender, C. Bodendorf, E. Branchini, M. Brescia, S. Camera, V. Capobianco, C. Carbone, J. Carretero, F. J. Castander, S. Cavuoti, A. Cimatti, R. Cledassou, G. Congedo, L. Conversi, Y. Copin, L. Corcione, F. Courbin, M. Cropper, A. Da Silva, H. Degaudenzi, J. Dinis, M. Douspis, F. Dubath, C. A. J. Duncan, X. Dupac, S. Dusini, S. Farrens, S. Ferriol, M. Frailis, E. Franceschi, P. Franzetti, S. Galeotta, B. Garilli, B. Gillis, C. Giocoli, A. Grazian, F. Grupp, S. V. H. Haugan, H. Hoekstra, W. Holmes, F. Hormuth, A. Hornstrup, P. Hudelot, K. Jahnke, S. Kermiche, A. Kiessling, T. Kitching, R. Kohley, M. Kunz, H. Kurki-Suonio, S. Ligori, P. B. Lilje, I. Lloro, O. Mansutti, O. Marggraf, K. Markovic, F. Marulli, R. Massey, H. J. McCracken, E. Medinaceli, M. Melchior, M. Meneghetti, G. Meylan, M. Moresco, L. Moscardini, E. Munari, S. M. Niemi, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. J. Percival, G. Polenta, M. Poncet, L. Popa, L. Pozzetti, F. Raison, R. Rebolo, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, E. Rossetti, R. Saglia, D. Sapone, B. Sartoris, P. Schneider, A. Secroun, G. Seidel, C. Sirignano, G. Sirri, J. Skottfelt, J. -L. Starck, P. Tallada-Crespi, A. N. Taylor, I. Tereno, R. Toledo-Moreo, I. Tutusaus, L. Valenziano, T. Vassallo, Y. Wang, J. Weller, A. Zacchei, G. Zamorani, J. Zoubian, S. Andreon, S. Bardelli, A. Boucaud, C. Colodro-Conde, D. Di Ferdinando, J. Gracia-Carpio, V. Lindholm, N. Mauri, S. Mei, C. Neissner, V. Scottez, A. Tramacere, E. Zucca, C. Baccigalupi, A. Balaguera-Antolinez, M. Ballardini, F. Bernardeau, A. Biviano, S. Borgani, A. S. Borlaff, C. Burigana, R. Cabanac, A. Cappi, C. S. Carvalho, S. Casas, G. Castignani, A. R. Cooray, J. Coupon, H. M. Courtois, O. Cucciati, S. Davini, G. De Lucia, G. Desprez, J. A. Escartin, S. Escoffier, M. Farina, K. Ganga, J. Garcia-Bellido, K. George, G. Gozaliasl, H. Hildebrandt, I. Hook, O. Ilbert, S. Ilic, B. Joachimi, V. Kansal, E. Keihanen, C. C. Kirkpatrick, A. Loureiro, J. Macias-Perez, M. Magliocchetti, G. Mainetti, R. Maoli, S. Marcin, M. Martinelli, N. Martinet, S. Matthew, M. Maturi, R. B. Metcalf, P. Monaco, G. Morgante, S. Nadathur, A. A. Nucita, L. Patrizii, V. Popa, C. Porciani, D. Potter, A. Pourtsidou, M. Pontinen, P. Reimberg, A. G. Sanchez, Z. Sakr, M. Schirmer, M. Sereno, J. Stadel, R. Teyssier, C. Valieri, J. Valiviita, S. E. van Mierlo, A. Veropalumbo, M. Viel, J. R. Weaver, D. Scott

Summary: The Euclid mission of the European Space Agency aims to provide high-quality imaging for 1.5 billion galaxies. A software pipeline is being developed to process and analyze the massive amount of data in real time, including a model-fitting algorithm for important photometric and morphological estimates. The Euclid Morphology Challenge compares five model-fitting software packages on simulated data to identify the best algorithm for implementation.

ASTRONOMY & ASTROPHYSICS (2023)

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The probability of galaxy-galaxy strong lensing events in hydrodynamical simulations of galaxy clusters

Massimo Meneghetti, Antonio Ragagnin, Stefano Borgani, Francesco Calura, Giulia Despali, Carlo Giocoli, Gian Luigi Granato, Claudio Grillo, Lauro Moscardini, Elena Rasia, Piero Rosati, Giuseppe Angora, Luigi Bassini, Pietro Bergamini, Gabriel B. Caminha, Giovanni Granata, Amata Mercurio, Robert Benton Metcalf, Priyamvada Natarajan, Mario Nonino, Giada Venusta Pignataro, Cinthia Ragone-Figueroa, Eros Vanzella, Ana Acebron, Klaus Dolag, Giuseppe Murante, Giuliano Taffoni, Luca Tornatore, Luca Tortorelli, Milena Valentini

Summary: This article investigates the galaxy-galaxy strong lensing phenomenon in galaxy clusters and finds a discrepancy between theoretical expectations and observations. The study shows that the galaxy formation model implemented in simulations affects the probability of strong lensing, but current models struggle to simultaneously reproduce the stellar mass function and the internal structure of galaxies.

ASTRONOMY & ASTROPHYSICS (2022)

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Article Astronomy & Astrophysics

Euclid preparation XXII. Selection of quiescent galaxies from mock photometry using machine learning

A. Humphrey, L. Bisigello, P. A. C. Cunha, M. Bolzonella, S. Fotopoulou, K. Caputi, C. Tortora, G. Zamorani, P. Papaderos, D. Vergani, J. Brinchmann, M. Moresco, A. Amara, N. Auricchio, M. Baldi, R. Bender, D. Bonino, E. Branchini, M. Brescia, S. Camera, V. Capobianco, C. Carbone, J. Carretero, F. J. Castander, M. Castellano, S. Cavuoti, A. Cimatti, R. Cledassou, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, L. Corcione, F. Courbin, M. Cropper, A. Da Silva, H. Degaudenzi, M. Douspis, F. Dubath, C. A. J. Duncan, X. Dupac, S. Dusini, S. Farrens, S. Ferriol, M. Frailis, E. Franceschi, M. Fumana, P. Gomez-Alvarez, S. Galeotta, B. Garilli, W. Gillard, B. Gillis, C. Giocoli, A. Grazian, F. Grupp, L. Guzzo, S. V. H. Haugan, W. Holmes, F. Hormuth, K. Jahnke, M. Kuemmel, S. Kermiche, A. Kiessling, M. Kilbinger, 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, S. Maurogordato, H. J. McCracken, E. Medinaceli, M. Melchior, M. Meneghetti, E. Merlin, G. Meylan, L. Moscardini, E. Munari, R. Nakajima, S. M. Niemi, J. Nightingale, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, V. Pettorino, S. Pires, M. Poncet, L. Popa, L. Pozzetti, F. Raison, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, E. Rossetti, R. Saglia, D. Sapone, B. Sartoris, R. Scaramella, P. Schneider, M. Scodeggio, A. Secroun, G. Seidel, C. Sirignano, G. Sirri, L. Stanco, P. Tallada-Crespi, D. Tavagnacco, A. N. Taylor, I. Tereno, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, L. Valenziano, T. Vassallo, Y. Wang, J. Weller, A. Zacchei, J. Zoubian, S. Andreon, S. Bardelli, A. Boucaud, R. Farinelli, J. Gracia-Carpio, D. Maino, N. Mauri, S. Mei, N. Morisset, F. Sureau, M. Tenti, A. Tramacere, E. Zucca, C. Baccigalupi, A. Balaguera-Antolinez, A. Biviano, A. Blanchard, S. Borgani, E. Bozzo, C. Burigana, R. Cabanac, A. Cappi, C. S. Carvalho, S. Casas, G. Castignani, C. Colodro-Conde, A. R. Cooray, J. Coupon, H. M. Courtois, O. Cucciati, S. Davini, G. De Lucia, H. Dole, J. A. Escartin, S. Escoffier, M. Fabricius, M. Farina, F. Finelli, K. Ganga, J. Garcia-Bellido, K. George, F. Giacomini, G. Gozaliasl, I. Hook, M. Huertas-Company, B. Joachimi, V. Kansal, A. Kashlinsky, E. Keihanen, C. C. Kirkpatrick, V. Lindholm, G. Mainetti, R. Maoli, S. Marcin, M. Martinelli, N. Martinet, M. Maturi, R. B. Metcalf, G. Morgante, A. A. Nucita, L. Patrizii, A. Peel, J. E. Pollack, V. Popa, C. Porciani, D. Potter, P. Reimberg, A. G. Sanchez, M. Schirmer, M. Schultheis, V. Scottez, E. Sefusatti, J. Stadel, R. Teyssier, C. Valieri, J. Valiviita, M. Viel, F. Calura, H. Hildebrandt

Summary: The Euclid Space Telescope will provide deep imaging and spectroscopy across a large area of the sky, enabling the detection of billions of astronomical sources. To analyze this vast dataset, a novel machine-learning-based methodology called ARIADNE pipeline has been developed. This pipeline combines multiple learning methods to achieve higher accuracy in classifying quiescent galaxies and deriving photometric redshifts.

ASTRONOMY & ASTROPHYSICS (2023)

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Article Astronomy & Astrophysics

Euclid preparation - XXIII. Derivation of galaxy physical properties with deep machine learning using mock fluxes and H-band images

L. Bisigello, C. J. Conselice, M. Baes, M. Bolzonella, M. Brescia, S. Cavuoti, O. Cucciati, A. Humphrey, L. K. Hunt, C. Maraston, L. Pozzetti, C. Tortora, S. E. van Mierlo, N. Aghanim, N. Auricchio, M. Baldi, R. Bender, C. Bodendorf, D. Bonino, E. Branchini, J. Brinchmann, S. Camera, V. Capobianco, C. Carbone, J. Carretero, F. J. Castander, M. Castellano, A. Cimatti, G. Congedo, L. Conversi, Y. Copin, L. Corcione, F. Courbin, M. Cropper, A. Da Silva, H. Degaudenzi, M. Douspis, F. Dubath, C. A. J. Duncan, X. Dupac, S. Dusini, S. Farrens, S. Ferriol, M. Frailis, E. Franceschi, P. Franzetti, M. Fumana, B. Garilli, W. Gillard, B. Gillis, C. Giocoli, A. Grazian, F. Grupp, L. Guzzo, S. V. H. Haugan, W. Holmes, F. Hormuth, A. Hornstrup, K. Jahnke, M. Kuemmel, S. Kermiche, A. Kiessling, M. Kilbinger, 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, S. Maurogordato, E. Medinaceli, M. Meneghetti, E. Merlin, G. Meylan, M. Moresco, L. Moscardini, E. Munari, S. M. Niemi, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, V. Pettorino, G. Polenta, M. Poncet, L. Popa, F. Raison, A. Renzi, J. Rhodes, G. Riccio, H-W Rix, E. Romelli, M. Roncarelli, C. Rosset, E. Rossetti, R. Saglia, D. Sapone, B. Sartoris, P. Schneider, M. Scodeggio, A. Secroun, G. Seidel, C. Sirignano, G. Sirri, L. Stanco, P. Tallada-Crespi, D. Tavagnacco, A. N. Taylor, I. Tereno, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, E. A. Valentijn, L. Valenziano, T. Vassallo, Y. Wang, A. Zacchei, G. Zamorani, J. Zoubian, S. Andreon, S. Bardelli, A. Boucaud, C. Colodro-Conde, D. Di Ferdinando, J. Gracia-Carpio, V. Lindholm, D. Maino, S. Mei, V. Scottez, F. Sureau, M. Tenti, E. Zucca, A. S. Borlaff, M. Ballardini, A. Biviano, E. Bozzo, C. Burigana, R. Cabanac, A. Cappi, C. S. Carvalho, S. Casas, G. Castignani, A. Cooray, J. Coupon, H. M. Courtois, J. Cuby, S. Davini, G. De Lucia, G. Desprez, H. Dole, J. A. Escartin, S. Escoffier, M. Farina, S. Fotopoulou, K. Ganga, J. Garcia-Bellido, K. George, F. Giacomini, G. Gozaliasl, H. Hildebrandt, I. Hook, M. Huertas-Company, V. Kansal, E. Keihanen, C. C. Kirkpatrick, A. Loureiro, J. F. Macias-Perez, M. Magliocchetti, G. Mainetti, S. Marcin, M. Martinelli, N. Martinet, R. B. Metcalf, P. Monaco, G. Morgante, S. Nadathur, A. A. Nucita, L. Patrizii, A. Peel, D. Potter, A. Pourtsidou, M. Poentinen, P. Reimberg, A. G. Sanchez, Z. Sakr, M. Schirmer, E. Sefusatti, M. Sereno, J. Stadel, R. Teyssier, C. Valieri, J. Valiviita, M. Viel

Summary: Next-generation telescopes will allow us to infer physical properties for millions of galaxies, and machine-learning methods are efficient tools to handle this massive amount of data. In this study, we investigate the accuracy of deep-learning algorithms in measuring redshifts, stellar masses, and SFRs for observed galaxies. We find that deep-learning neural networks and CNNs perform well and have better accuracy compared to traditional methods. Our results show that redshifts can be measured within a normalized error of <0.15 for 99.9% of galaxies, stellar masses within a factor of two for 99.5% of galaxies, and SFRs within a factor of two for approximately 70% of the sample.

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Article Astronomy & Astrophysics

Euclid preparation: XXVIII. Forecasts for ten different higher-order weak lensing statistics

V. Ajani, M. Baldi, A. Barthelemy, A. Boyle, P. Burger, V. F. Cardone, S. Cheng, S. Codis, C. Giocoli, J. Harnois-Deraps, S. Heydenreich, V. Kansal, M. Kilbinger, L. Linke, C. Llinares, N. Martinet, C. Parroni, A. Peel, S. Pires, L. Porth, I. Tereno, C. Uhlemann, M. Vicinanza, S. Vinciguerra, N. Aghanim, N. Auricchio, D. Bonino, E. Branchini, M. Brescia, J. Brinchmann, S. Camera, V. Capobianco, C. Carbone, J. Carretero, F. J. Castander, M. Castellano, S. Cavuoti, A. Cimatti, R. Cledassou, G. Congedo, C. J. Conselice, L. Conversi, L. Corcione, F. Courbin, M. Cropper, A. Da Silva, H. Degaudenzi, A. M. Di Giorgio, J. Dinis, M. Douspis, F. Dubath, X. Dupac, S. Farrens, S. Ferriol, P. Fosalba, M. Frailis, E. Franceschi, S. Galeotta, B. Garilli, B. Gillis, A. Grazian, F. Grupp, H. Hoekstra, W. Holmes, A. Hornstrup, P. Hudelot, K. Jahnke, M. Jhabvala, M. Kummel, T. Kitching, M. Kunz, H. Kurki-Suonio, P. B. Lilje, I. Lloro, E. Maiorano, O. Mansutti, O. Marggraf, K. Markovic, F. Marulli, R. Massey, S. Mei, Y. Mellier, M. Meneghetti, M. Moresco, L. Moscardini, S. -M. Niemi, J. Nightingale, T. Nutma, C. Padilla, S. Paltani, K. Pedersen, V. Pettorino, G. Polenta, M. Poncet, L. A. Popa, F. Raison, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, E. Rossetti, R. Saglia, D. Sapone, B. Sartoris, P. Schneider, T. Schrabback, A. Secroun, G. Seidel, S. Serrano, C. Sirignano, L. Stanco, J. L. Starck, P. Tallada-Crespi, A. N. Taylor, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, E. A. Valentijn, L. Valenziano, T. Vassallo, Y. Wang, J. Weller, G. Zamorani, J. Zoubian, S. Andreon, S. Bardelli, A. Boucaud, E. Bozzo, C. Colodro-Conde, D. Di Ferdinando, G. Fabbian, M. Farina, J. Gracia-Carpio, E. Keihanen, V. Lindholm, D. Maino, N. Mauri, C. Neissner, M. Schirmer, V. Scottez, E. Zucca, Y. Akrami, C. Baccigalupi, A. Balaguera-Antolinez, M. Ballardini, F. Bernardeau, A. Biviano, A. Blanchard, S. Borgani, A. S. Borlaff, C. Burigana, R. Cabanac, A. Cappi, C. S. Carvalho, S. Casas, G. Castignani, T. Castro, K. C. Chambers, A. R. Cooray, J. Coupon, H. M. Courtois, S. Davini, S. de la Torre, G. De Lucia, G. Desprez, H. Dole, J. A. Escartin, S. Escoffier, I. Ferrero, F. Finelli, K. Ganga, J. Garcia-Bellido, K. George, F. Giacomini, G. Gozaliasl, H. Hildebrandt, A. Jimenez Munoz, B. Joachimi, J. J. E. Kajava, C. C. Kirkpatrick, L. Legrand, A. Loureiro, M. Magliocchetti, R. Maoli, S. Marcin, M. Martinelli, C. J. A. P. Martins, S. Matthew, L. Maurin, R. B. Metcalf, P. Monaco, G. Morgante, S. Nadathur, A. A. Nucita, V. Popa, D. Potter, A. Pourtsidou, M. Pontinen, P. Reimberg, A. G. Sanchez, Z. Sakr, A. Schneider, E. Sefusatti, M. Sereno, A. Shulevski, A. Spurio Mancini, J. Steinwagner, R. Teyssier, J. Valiviita, A. Veropalumbo, M. Viel, I. A. Zinchenko

Summary: Recent cosmic shear studies have found that higher-order statistics (HOS) developed by different teams surpass standard two-point estimators in statistical precision due to their sensitivity to non-Gaussian characteristics of large-scale structure. The Higher-Order Weak Lensing Statistics (HOWLS) project aims to assess, compare, and combine the constraining power of ten different HOS using simulated data. Results show that each HOS outperforms two-point statistics by a factor of two individually, and when combined, the improvement increases to 4.5 times.

ASTRONOMY & ASTROPHYSICS (2023)

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Article Astronomy & Astrophysics

Euclid preparation XXX. Performance assessment of the NISP red grism through spectroscopic simulations for the wide and deep surveys

L. Gabarra, C. Mancini, L. Rodriguez Munoz, G. Rodighiero, C. Sirignano, M. Scodeggio, M. Talia, S. Dusini, W. Gillard, B. R. Granett, E. Maiorano, M. Moresco, L. Paganin, E. Palazzi, L. Pozzetti, A. Renzi, E. Rossetti, D. Vergani, V. Allevato, L. Bisigello, G. Castignani, B. De Caro, M. Fumana, K. Ganga, B. Garilli, M. Hirschmann, F. La Franca, C. Laigle, F. Passalacqua, M. Schirmer, L. Stanco, A. Troja, L. Y. A. Yung, G. Zamorani, J. Zoubian, S. Anselmi, F. Oppizzi, G. Verza, N. Aghanim, A. Amara, N. Auricchio, M. Baldi, R. Bender, C. Bodendorf, D. Bonino, E. Branchini, M. Brescia, J. Brinchmann, S. Camera, V. Capobianco, C. Carbone, J. Carretero, F. J. Castander, M. Castellano, S. Cavuoti, R. Cledassou, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, L. Corcione, A. Costille, F. Courbin, A. Da Silva, H. Degaudenzi, J. Dinis, F. Dubath, X. Dupac, A. Ealet, S. Farrens, S. Ferriol, M. Frailis, E. Franceschi, P. Franzetti, S. Galeotta, B. Gillis, C. Giocoli, A. Grazian, F. Grupp, L. Guzzo, W. Holmes, A. Hornstrup, P. Hudelot, K. Jahnke, M. Kuemmel, S. Kermiche, A. Kiessling, M. Kilbinger, T. Kitching, R. Kohley, B. Kubik, M. Kunz, H. Kurki-Suonio, S. Ligori, P. B. Lilje, I. Lloro, O. Mansutti, O. Marggraf, K. Markovic, F. Marulli, R. Massey, S. Maurogordato, S. Mei, M. Meneghetti, G. Meylan, L. Moscardini, E. Munari, R. C. Nichol, S. -M. Niemi, J. Nightingale, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. J. Percival, V. Pettorino, G. Polenta, M. Poncet, F. Raison, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, R. Saglia, D. Sapone, P. Schneider, A. Secroun, G. Seidel, S. Serrano, G. Sirri, C. Surace, P. Tallada-Crespi, D. Tavagnacco, A. N. Taylor, I. Tereno, R. Toledo-Moreo, F. Torradeflot, M. Trifoglio, I. Tutusaus, E. A. Valentijn, L. Valenziano, T. Vassallo, Y. Wang, J. Weller, A. Zacchei, S. Andreon, H. Aussel, S. Bardelli, M. Bolzonella, A. Boucaud, E. Bozzo, C. Colodro-Conde, D. Di Ferdinando, M. Farina, J. Gracia-Carpio, E. Keihanen, V. Lindholm, D. Maino, N. Mauri, Y. Mellier, C. Neissner, V. Scottez, M. Tenti, E. Zucca, Y. Akrami, C. Baccigalupi, M. Ballardini, F. Bernardeau, A. Biviano, A. S. Borlaff, E. Borsato, C. Burigana, R. Cabanac, A. Cappi, C. S. Carvalho, S. Casas, T. Castro, K. Chambers, A. R. Cooray, J. Coupon, H. M. Courtois, S. Davini, S. de la Torre, G. De Lucia, G. Desprez, H. Dole, J. A. Escartin, S. Escoffier, I. Ferrero, F. Finelli, S. Fotopoulou, J. Garcia-Bellido, K. George, F. Giacomini, G. Gozaliasl, H. Hildebrandt, I. Hook, O. Ilbert, A. Jimenez Munoz, J. J. E. Kajava, V. Kansal, C. C. Kirkpatrick, L. Legrand, A. Loureiro, J. Macias-Perez, M. Magliocchetti, G. Mainetti, R. Maoli, S. Marcin, M. Martinelli, N. Martinet, C. J. A. P. Martins, S. Matthew, L. Maurin, R. B. Metcalf, G. Morgante, S. Nadathur, A. A. Nucita, L. Patrizii, V. Popa, C. Porciani, D. Potter, M. Poentinen, A. G. Sanchez, Z. Sakr, A. Schneider, E. Sefusatti, M. Sereno, A. Shulevski, A. Spurio Mancini, J. Stadel, J. Steinwagner, R. Teyssier, J. Valiviita, A. Veropalumbo, M. Viel, I. A. Zinchenko

Summary: This work focuses on the simulation campaign of the Euclid Near-Infrared Spectrometer and Photometer (NISP), investigating its spectroscopic capabilities in galaxy evolutionary studies. The spectra of 4992 star-forming galaxies were simulated and analyzed, with emphasis on continuum and emission line detection.

ASTRONOMY & ASTROPHYSICS (2023)

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Article Astronomy & Astrophysics

Euclid preparation XXIX. Water ice in spacecraft Part I: The physics of ice formation and contamination

M. Schirmer, K. Thuermer, B. Bras, M. Cropper, J. Martin-Fleitas, Y. Goueffon, R. Kohley, A. Mora, M. Portaluppi, G. D. Racca, A. D. Short, S. Szmolka, L. M. Gaspar Venancio, M. Altmann, Z. Balog, U. Bastian, M. Biermann, D. Busonero, C. Fabricius, F. Grupp, C. Jordi, W. Loeffler, A. Sagrista Selles, N. Aghanim, A. Amara, L. Amendola, M. Baldi, C. Bodendorf, D. Bonino, E. Branchini, M. Brescia, J. Brinchmann, S. Camera, G. P. Candini, V. Capobianco, C. Carbone, J. Carretero, M. Castellano, S. Cavuoti, A. Cimatti, R. Cledassou, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, L. Corcione, F. Courbin, A. Da Silva, H. Degaudenzi, A. M. Di Giorgio, 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, S. V. H. Haugan, H. Hoekstra, W. Holmes, F. Hormuth, A. Hornstrup, K. Jahnke, S. Kermiche, A. Kiessling, M. Kilbinger, T. Kitching, 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, Y. Mellier, M. Meneghetti, E. Merlin, G. Meylan, M. Moresco, L. Moscardini, E. Munari, R. Nakajima, S. -M. Niemi, J. W. Nightingale, T. Nutma, C. Padilla, S. Paltani, F. Pasian, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, 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, J. Skottfelt, L. Stanco, P. Tallada-Crespi, A. N. Taylor, I. Tereno, R. Toledo-Moreo, I. Tutusaus, E. A. Valentijn, L. Valenziano, T. Vassallo, Y. Wang, J. Weller, A. Zacchei, J. Zoubian, S. Andreon, S. Bardelli, P. Battaglia, E. Bozzo, C. Colodro-Conde, M. Farina, J. Gracia-Carpio, E. Keihaenen, V. Lindholm, D. Maino, N. Mauri, N. Morisset, V. Scottez, M. Tenti, E. Zucca, Y. Akrami, C. Baccigalupi, M. Ballardini, A. Biviano, A. Blanchard, A. S. Borlaff, C. Burigana, R. Cabanac, A. Cappi, C. S. Carvalho, S. Casas, G. Castignani, T. Castro, K. C. Chambers, A. R. Cooray, J. Coupon, H. M. Courtois, J. -G. Cuby, S. Davini, G. De Lucia, G. Desprez, S. Di Domizio, H. Dole, J. A. Escartin, S. Escoffier, I. Ferrero, L. Gabarra, K. Ganga, J. Garcia-Bellido, K. George, F. Giacomini, G. Gozaliasl, H. Hildebrandt, J. J. E. Kajava, V. Kansal, C. C. Kirkpatrick, L. Legrand, P. Liebing, A. Loureiro, G. Maggio, M. Magliocchetti, G. Mainetti, R. Maoli, S. Marcin, M. Martinelli, N. Martinet, C. J. A. P. Martins, S. Matthew, M. Maturi, L. Maurin, R. B. Metcalf, P. Monaco, G. Morgante, S. Nadathur, A. A. Nucita, L. Patrizii, J. E. Pollack, V. Popa, D. Potter, M. Poentinen, A. G. Sanchez, Z. Sakr, A. Schneider, M. Sereno, A. Shulevski, P. Simon, J. Steinwagner, R. Teyssier, J. Valiviita

Summary: Material outgassing in vacuum causes molecular contamination, especially from water, which alters the properties of optical systems in cryogenic spacecraft like Euclid. Excessive ice formation in Euclid requires a risky thermal decontamination procedure. A comprehensive analysis on the effects of ice on data in an astrophysical space survey has not been done before, and this paper aims to understand the impact of iced optics on Euclid's data by studying other spacecraft's outgassing records and examining thin ice films.

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Article Astronomy & Astrophysics

Euclid preparation XXVII. A UV-NIR spectral atlas of compact planetary nebulae for wavelength calibration

K. Paterson, M. Schirmer, Y. Copin, J. -C. Cuillandre, W. Gillard, L. A. Gutierrez Soto, L. Guzzo, H. Hoekstra, T. Kitching, S. Paltani, W. J. Percival, M. Scodeggio, L. Stanghellini, P. N. Appleton, R. Laureijs, Y. Mellier, N. Aghanim, B. Altieri, A. Amara, N. Auricchio, M. Baldi, R. Bender, C. Bodendorf, D. Bonino, E. Branchini, M. Brescia, J. Brinchmann, S. Camera, V. Capobianco, C. Carbone, J. Carretero, F. J. Castander, M. Castellano, S. Cavuoti, A. Cimatti, R. Cledassou, G. Congedo, C. J. Conselice, L. Conversi, L. Corcione, F. Courbin, A. Da Silva, H. Degaudenzi, J. Dinis, M. Douspis, F. Dubath, X. Dupac, S. Ferriol, M. Frailis, E. Franceschi, M. Fumana, S. Galeotta, B. Garilli, B. Gillis, C. Giocoli, A. Grazian, F. Grupp, S. V. H. Haugan, W. Holmes, A. Hornstrup, P. Hudelot, K. Jahnke, M. Kuemmel, A. Kiessling, M. Kilbinger, R. Kohley, B. Kubik, 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. Meneghetti, G. Meylan, M. Moresco, L. Moscardini, R. Nakajima, S. -M. Niemi, J. W. Nightingale, T. Nutma, C. Padilla, F. Pasian, K. Pedersen, G. Polenta, M. Poncet, L. A. Popa, F. Raison, A. Renzi, J. Rhodes, G. Riccio, H. -W. Rix, E. Romelli, M. Roncarelli, E. Rossetti, R. Saglia, B. Sartoris, P. Schneider, A. Secroun, G. Seidel, S. Serrano, C. Sirignano, G. Sirri, J. Skottfelt, L. Stanco, P. Tallada-Crespi, A. N. Taylor, I. Tereno, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, L. Valenziano, T. Vassallo, Y. Wang, J. Weller, G. Zamorani, J. Zoubian, S. Andreon, S. Bardelli, E. Bozzo, C. Colodro-Conde, D. Di Ferdinando, M. Farina, J. Gracia-Carpio, E. Keihaenen, V. Lindholm, D. Maino, N. Mauri, V. Scottez, M. Tenti, E. Zucca, Y. Akrami, C. Baccigalupi, M. Ballardini, A. Biviano, A. S. Borlaff, C. Burigana, R. Cabanac, A. Cappi, C. S. Carvalho, S. Casas, G. Castignani, T. Castro, K. C. Chambers, A. R. Cooray, J. Coupon, H. M. Courtois, S. Davini, G. De Lucia, G. Desprez, J. A. Escartin, S. Escoffier, I. Ferrero, L. Gabarra, J. Garcia-Bellido, K. George, F. Giacomini, G. Gozaliasl, H. Hildebrandt, I. Hook, J. J. E. Kajava, V. Kansal, C. C. Kirkpatrick, L. Legrand, A. Loureiro, M. Magliocchetti, G. Mainetti, R. Maoli, S. Marcin, M. Martinelli, N. Martinet, C. J. A. P. Martins, S. Matthew, L. Maurin, R. B. Metcalf, P. Monaco, G. Morgante, S. Nadathur, L. Patrizii, J. Pollack, C. Porciani, D. Potter, M. Poentinen, A. G. Sanchez, Z. Sakr, A. Schneider, E. Sefusatti, M. Sereno, A. Shulevski, J. Stadel, J. Steinwagner, C. Valieri, J. Valiviita, A. Veropalumbo, M. Viel, I. A. Zinchenko

Summary: The Euclid mission will survey the extragalactic sky and obtain spectroscopic redshifts for 30 million galaxies using the NISP instrument. To ensure wavelength calibration accuracy, the team used emission lines from compact planetary nebulae (PNe) to recalibrate the data. They obtained high-resolution spectra from ground-based instruments and created an atlas of heliocentric vacuum wavelengths for further analysis.

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