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12 results on '"G. Castignani"'

1. AMICO galaxy clusters in KiDS-DR3: Constraints on cosmological parameters and on the normalisation of the mass-richness relation from clustering

Author

G. F. Lesci, L. Nanni, F. Marulli, L. Moscardini, A. Veropalumbo, M. Maturi, M. Sereno, M. Radovich, F. Bellagamba, M. Roncarelli, S. Bardelli, G. Castignani, G. Covone, C. Giocoli, L. Ingoglia, and E. Puddu

Subjects
Cosmology and Nongalactic Astrophysics (astro-ph.CO), Space and Planetary Science, FOS: Physical sciences, Astronomy and Astrophysics, Astrophysics::Cosmology and Extragalactic Astrophysics, Astrophysics::Galaxy Astrophysics, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

We analysed the clustering of a photometric sample of galaxy clusters selected from the Third Data Release of the Kilo-Degree Survey, focusing on the redshift-space two-point correlation function (2PCF). We compared our measurements to theoretical predictions of the standard $\Lambda$ cold dark matter ($\Lambda$CDM) cosmological model. We measured the 2PCF of the sample in the cluster centric radial range $r\in[5,80]$ $h^{-1}$Mpc, considering 4934 galaxy clusters with richness $\lambda^*\geq15$ in the redshift range $z\in[0.1,0.6]$. A Markov chain Monte Carlo analysis has been performed to constrain the cosmological parameters $\Omega_{\rm m}$, $\sigma_8$, and $S_8 \equiv \sigma_8(\Omega_{\rm m}/0.3)^{0.5}$, assuming Gaussian priors on the mass-richness relation given by the posteriors obtained from a joint analysis of cluster counts and weak lensing. In addition, we constrained the normalisation of the mass-richness relation, $\alpha$, with fixed cosmological parameters. We obtained $\Omega_{\rm m}=0.28^{+0.05}_{-0.04}$, $\sigma_8=0.82^{+0.14}_{-0.12}$, and $S_8=0.80^{+0.08}_{-0.08}$. The constraint on $S_8$ is consistent within 1$\sigma$ with the results from WMAP and Planck. Furthermore, by fixing the cosmological parameters to those provided by Planck, we obtained $\alpha=0.12^{+0.06}_{-0.06}$, which is fully consistent with the result obtained from the joint analysis of cluster counts and weak lensing performed for this sample., Comment: 8 pages, 5 figures, 1 table. Submitted to A&A

Published
2022

2. Black hole and galaxy co-evolution in radio-loud active galactic nuclei at z ∼ 0.3–4

Author

R. Poitevineau, G. Castignani, and F. Combes

Subjects
Space and Planetary Science, Astronomy and Astrophysics
Abstract

The relation between the mass of the supermassive black hole (SMBH) in the center of galaxies and their bulge mass or central velocity dispersion is well known. This suggests a coevolution between the SMBHs and their galaxy hosts. Our aim is to study this relation, specifically, for radio loud galaxies, and as a function of redshift z. We selected a sample of 42 radio galaxies and active galactic nuclei (AGN) with broad emission lines and spectroscopic redshifts between z = 0.3 − 4 by cross-matching the low radio frequency sources from Very Large Array (VLA) FIRST with spectroscopically confirmed galaxies from wide-field surveys, including Sloan Digital Sky Survey (SDSS) DR14 ugriz and Dark Energy Survey (DES) DR2 grzY in the optical, Wield Infrared Survey Explorer (WISE), and the Galaxy And Mass Assembly (GAMA) spectroscopic survey. We characterized the stellar mass (M⋆), star formation, and black hole properties (mass of the central SMBH, Eddington ratio η, and jet power, Qjet). The relation between SMBH mass, M⋆, η, and z is placed into context by comparing them with scaling relations (MBH–M⋆, MBH/M⋆–z, MBH–Qjet, and Qjet–η) from the literature. On the basis of a multiwavelength spectral energy distribution modeling, our radio sources are broadly consistent with being on the star-forming main sequence. They have sub-Eddington accretion rates, η ≃ 1% on average, as typically found in type I AGN, while higher accretion rates favor more powerful jets to be launched by the central engine. We find overmassive SMBHs in (17 ± 5)% of our radio sources, similarly to previous studies on nearby early-type galaxies. Altogether, an evolutionary scenario in which radio-mode AGN feedback regulates the accretion onto the SMBHs and the stellar mass assembly of the radio sources is discussed, which may explain the observed phenomenology. This pilot study represents a benchmark for future studies using wide-field surveys such as those with Euclid and the Vera Rubin Observatory.

Published
2023
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3. The host galaxy of the recoiling black hole candidate in 3C 186: an old major merger remnant at the center of a z = 1 cluster

Author

T. Morishita, M. Chiaberge, B. Hilbert, E. Lambrides, L. Blecha, S. Baum, S. Bianchi, A. Capetti, G. Castignani, F. D. Macchetto, G. K. Miley, C. P. O’Dea, C. A. Norman, Morishita, T., Chiaberge, M., Hilbert, B., Lambrides, E., Blecha, L., Baum, S., Bianchi, S., Capetti, A., Castignani, G., Macchetto, F. D., Miley, G. K., O’Dea, C. P., and Norman, C. A.

Subjects
Space and Planetary Science, Astrophysics of Galaxies (astro-ph.GA), Astrophysics::High Energy Astrophysical Phenomena, Active galaxies, FOS: Physical sciences, Astronomy and Astrophysics, Astrophysics::Cosmology and Extragalactic Astrophysics, Astrophysics - Astrophysics of Galaxies, Astrophysics::Galaxy Astrophysics, Gravitational waves
Abstract

3C186, a radio-loud quasar at $z=1.0685$, was previously reported to have both velocity and spatial offsets from its host galaxy, and has been considered as a promising candidate for a gravitational wave recoiling black hole triggered by a black hole merger. Another possible scenario is that 3C186 is in an on-going galaxy merger, exhibiting a temporary displacement. In this study, we present analyses of new deep HST/WFC3-IR and ACS images, aiming to characterize the host galaxy and test this alternative scenario. We carefully measure the light-weighted center of the host and reveal a significant spatial offset from the quasar core ($11.1\pm0.1$kpc). The direction of the confirmed offset aligns almost perpendicularly to the radio jet. We do not find evidence of a recent merger, such as a young starburst in disturbed outskirts, but only marginal light concentration in F160W at $\sim30$kpc. The host consists of matured ($>200$Myr) stellar populations and one compact star-forming region. We compare with hydro-dynamical simulations and find that those observed features are consistently seen in late-stage merger remnants. Taken together, those pieces of evidence indicate that the system is not an on-going/young merger remnant, suggesting that the recoiling black hole scenario is still a plausible explanation for the puzzling nature of 3C186., Accepted for publication in ApJ. 14 pages, 8 figures, 2 tables

Published
2022

4. Euclid: Cosmological forecasts from the void size function

Author

S. Contarini, G. Verza, A. Pisani, N. Hamaus, M. Sahlén, C. Carbone, S. Dusini, F. Marulli, L. Moscardini, A. Renzi, C. Sirignano, L. Stanco, M. Aubert, M. Bonici, G. Castignani, H. M. Courtois, S. Escoffier, D. Guinet, A. Kovacs, G. Lavaux, E. Massara, S. Nadathur, G. Pollina, T. Ronconi, F. Ruppin, Z. Sakr, A. Veropalumbo, B. D. Wandelt, A. Amara, N. Auricchio, M. Baldi, D. Bonino, E. Branchini, M. Brescia, J. Brinchmann, S. Camera, V. Capobianco, J. Carretero, M. Castellano, S. Cavuoti, R. Cledassou, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, L. Corcione, F. Courbin, M. Cropper, A. Da Silva, H. Degaudenzi, F. Dubath, C. A. J. Duncan, X. Dupac, A. Ealet, S. Farrens, S. Ferriol, P. Fosalba, M. Frailis, E. Franceschi, B. Garilli, W. Gillard, B. Gillis, C. Giocoli, A. Grazian, F. Grupp, L. Guzzo, S. Haugan, W. Holmes, F. Hormuth, K. Jahnke, M. Kümmel, S. Kermiche, A. Kiessling, M. Kilbinger, M. Kunz, H. Kurki-Suonio, R. Laureijs, S. Ligori, P. B. Lilje, I. Lloro, E. Maiorano, O. Mansutti, O. Marggraf, K. Markovic, R. Massey, M. Melchior, M. Meneghetti, G. Meylan, M. Moresco, E. Munari, S. M. Niemi, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. J. Percival, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. Popa, L. Pozzetti, F. Raison, J. Rhodes, E. Rossetti, R. Saglia, B. Sartoris, P. Schneider, A. Secroun, G. Seidel, G. Sirri, C. Surace, P. Tallada-Crespí, A. N. Taylor, I. Tereno, R. Toledo-Moreo, F. Torradeflot, E. A. Valentijn, L. Valenziano, Y. Wang, J. Weller, G. Zamorani, J. Zoubian, S. Andreon, D. Maino, S. Mei, Institut de Physique des 2 Infinis de Lyon (IP2I Lyon), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Centre de Physique des Particules de Marseille (CPPM), Aix Marseille Université (AMU)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Institut d'Astrophysique de Paris (IAP), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut de recherche en astrophysique et planétologie (IRAP), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3), Astrophysique Interprétation Modélisation (AIM (UMR7158 / UMR_E_9005 / UM_112)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Centre National d'Études Spatiales [Toulouse] (CNES), Laboratoire d'Astrophysique de Marseille (LAM), Aix Marseille Université (AMU)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), AstroParticule et Cosmologie (APC (UMR_7164)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Euclid, Department of Physics, Helsinki Institute of Physics, Agenzia Spaziale Italiana, Università degli Studi di Padova, Ministero dell'Istruzione, dell'Università e della Ricerca, National Aeronautics and Space Administration (US), German Research Foundation, Astronomy, Contarini, S., Verza, G., Pisani, A., Hamaus, N., Sahlén, M., Carbone, C., Dusini, S., Marulli, F., Moscardini, L., Renzi, A., Sirignano, C., Stanco, L., Aubert, M., Bonici, M., Castignani, G., Courtois, H. M., Escoffier, S., Guinet, D., Kovacs, A., Lavaux, G., Massara, E., Nadathur, S., Pollina, G., Ronconi, T., Ruppin, F., Sakr, Z., Veropalumbo, A., Wandelt, B. D., Amara, A., Auricchio, N., Baldi, M., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carretero, J., Castellano, M., Cavuoti, S., Cledassou, R., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Cropper, M., Da Silva, A., Degaudenzi, H., Dubath, F., Duncan, C. A. J., Dupac, X., Ealet, A., Farrens, S., Ferriol, S., Fosalba, P., Frailis, M., Franceschi, E., Garilli, B., Gillard, W., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Guzzo, L., Haugan, S., Holmes, W., Hormuth, F., Jahnke, K., Kümmel, M., Kermiche, S., Kiessling, A., Kilbinger, M., Kunz, M., Kurki-Suonio, H., Laureijs, R., Ligori, S., Lilje, P. B., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Massey, R., Melchior, M., Meneghetti, M., Meylan, G., Moresco, M., Munari, E., Niemi, S. M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L., Pozzetti, L., Raison, F., Rhodes, J., Rossetti, E., Saglia, R., Sartoris, B., Schneider, P., Secroun, A., Seidel, G., Sirri, G., Surace, C., Tallada-Crespí, P., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Valentijn, E. A., Valenziano, L., Wang, Y., Weller, J., Zamorani, G., Zoubian, J., Andreon, S., Maino, D., and Mei, S.

Subjects
isw, Cosmology and Nongalactic Astrophysics (astro-ph.CO), bias, Galaxies: statistics, dark energy / cosmology: theory / galaxies: statistics / catalogs / surveys / methods: data analysis, FOS: Physical sciences, Astrophysics::Cosmology and Extragalactic Astrophysics, Surveys, Catalogs, Cosmology: Theory, Dark energy, Methods: data analysis, 114 Physical sciences, redshift-space distortions, Astronomi, astrofysik och kosmologi, statistics [Galaxies], galaxies, Astronomy, Astrophysics and Cosmology, luminosity function, data analysis [Methods], Astrophysics::Galaxy Astrophysics, dark energy survey, density, Astrophysics::Instrumentation and Methods for Astrophysics, Astronomy and Astrophysics, cosmic voids, matter, gravity, Space and Planetary Science, Theory [Cosmology], Cosmology: theory, [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph], Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

Euclid Consortium: S. Contarini et al., The Euclid mission – with its spectroscopic galaxy survey covering a sky area over 15 000 deg2 in the redshift range 0.9, We acknowledge the grant ASI n.2018-23-HH.0. SC, FM and LM acknowledge the use of computational resources from the parallel computing cluster of the Open Physics Hub (https://site.unibo.it/openphysicshub/en) at the Physics and Astronomy Department in Bologna. GV is supported by Universitá degli Studi di Padova and in part by the project “Combining Cosmic Microwave Background and Large Scale Structure data: an Integrated Approach for Addressing Fundamental Questions in Cosmology”, funded by the MIUR Progetti di Rilevante Interesse Nazionale (PRIN) Bando 2017 – grant 2017YJYZAH. AP is supported by NASA ROSES grant 12-EUCLID12-0004, and NASA grant 15-WFIRST15-0008 to the Nancy Grace Roman Space Telescope Science Investigation Team “Cosmology with the High Latitude Survey”. NH is supported by the Excellence Cluster ORIGINS, which is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC-2094 – 390783311. MS acknowledges support by the P. E. Filén fellowship and a fellowship at the Swedish Collegium for Advanced Study (SCAS). LM acknowledges support from PRIN MIUR 2017 WSCC32 “Zooming into dark matter and proto-galaxies with massive lensing clusters”. AR acknowledges funding from Italian Ministry of Education, University and Research (MIUR) through the ‘Dipartimenti di eccellenza’ project Science of the Universe. He is supported in part by the project “Combining Cosmic Microwave Background and Large Scale Structure data: an Integrated Approach for Addressing Fundamental Questions in Cosmology”, funded by the MIUR Progetti di Rilevante Interesse Nazionale (PRIN) Bando 2017 – grant 2017YJYZAH We acknowledge use of the Python libraries NumPy (Harris et al. 2020), Matplotlib (Hunter 2007) and ChainConsumer (Hinton 2016). This work has made use of Cosmo-Hub (Carretero et al. 2017; Tallada et al. 2020). CosmoHub has been developed by the Port d’Informació Científica (PIC), maintained through a collaboration ofthe Institut de Física d’Altes Energies (IFAE) and the Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT) and the Institute of Space Sciences (CSIC & IEEC), and was partially funded by the “Plan Estatalde Investigación Científica y Técnica y de Innovación” program of the Spanish government. The Euclid Consortium acknowledges the European Space Agency and a number of agencies and institutes that have supported the development of Euclid, in particular the Academy of Finland, the Agenzia Spaziale Italiana, the Belgian Science Policy, the Canadian Euclid Consortium, the French Centre National d’Etudes Spatiales, the Deutsches Zentrum für Luft- und Raumfahrt, the Danish Space Research Institute, the Fundação para a Ciência e a Tecnologia, the Ministerio de Ciencia e Innovación, the National Aeronautics and Space Administration, the National Astronomical Observatory of Japan, the Netherlandse Onderzoekschool Voor Astronomie, the Norwegian Space Agency, the Romanian Space Agency, the State Secretariat for Education, Research and Innovation (SERI) at the Swiss Space Office (SSO), and the United Kingdom Space Agency.

Published
2022
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7. Euclid preparation : XXIII. Derivation of galaxy physical properties with deep machine learning using mock fluxes and H-band images

Author

Collaboration, Euclid, Bisigello, L, Conselice, C J, Baes, M, Bolzonella, M, Brescia, M, Cavuoti, S, Cucciati, O, Humphrey, A, Hunt, L K, Maraston, C, Pozzetti, L, Tortora, C, van Mierlo, S E, Aghanim, N, Auricchio, N, Baldi, M, Bender, R, Bodendorf, C, Bonino, D, Branchini, E, Brinchmann, J, Camera, S, Capobianco, V, Carbone, C, Carretero, J, Castander, F J, Castellano, M, Cimatti, A, Congedo, G, Conversi, L, Copin, Y, Corcione, L, Courbin, F, Cropper, M, Da Silva, A, Degaudenzi, H, Douspis, M, Dubath, F, Duncan, C A J, Dupac, X, Dusini, S, Farrens, S, Ferriol, S, Frailis, M, Franceschi, E, Franzetti, P, Fumana, M, Garilli, B, Gillard, W, Gillis, B, Giocoli, C, Grazian, A, Grupp, F, Guzzo, L, Haugan, S V H, Holmes, W, Hormuth, F, Hornstrup, A, Jahnke, K, Kümmel, M, Kermiche, S, Kiessling, A, Kilbinger, M, Kohley, R, Kunz, M, Kurki-Suonio, H, Ligori, S, Lilje, P B, Lloro, I, Maiorano, E, Mansutti, O, Marggraf, O, Markovic, K, Marulli, F, Massey, R, Maurogordato, S, Medinaceli, E, Meneghetti, M, Merlin, E, Meylan, G, Moresco, M, Moscardini, L, Munari, E, Niemi, S M, Padilla, C, Paltani, S, Pasian, F, Pedersen, K, Pettorino, V, Polenta, G, Poncet, M, Popa, L, Raison, F, Renzi, A, Rhodes, J, Riccio, G, Rix, H -W, Romelli, E, Roncarelli, M, Rosset, C, Rossetti, E, Saglia, R, Sapone, D, Sartoris, B, Schneider, P, Scodeggio, M, Secroun, A, Seidel, G, Sirignano, C, Sirri, G, Stanco, L, Tallada-Crespí, P, Tavagnacco, D, Taylor, A N, Tereno, I, Toledo-Moreo, R, Torradeflot, F, Tutusaus, I, Valentijn, E A, Valenziano, L, Vassallo, T, Wang, Y, Zacchei, A, Zamorani, G, Zoubian, J, Andreon, S, Bardelli, S, Boucaud, A, Colodro-Conde, C, Ferdinando, D Di, Graciá-Carpio, J, Lindholm, V, Maino, D, Mei, S, Scottez, V, Sureau, F, Tenti, M, Zucca, E, Borlaff, A S, Ballardini, M, Biviano, A, Bozzo, E, Burigana, C, Cabanac, R, Cappi, A, Carvalho, C S, Casas, S, Castignani, G, Cooray, A, Coupon, J, Courtois, H M, Cuby, J, Davini, S, De Lucia, G, Desprez, G, Dole, H, Escartin, J A, Escoffier, S, Farina, M, Fotopoulou, S, Ganga, K, Garcia-Bellido, J, George, K, Giacomini, F, Gozaliasl, G, Hildebrandt, H, Hook, I, Huertas-Company, M, Kansal, V, Keihanen, E, Kirkpatrick, C C, Loureiro, A, Macías-Pérez, J F, Magliocchetti, M, Mainetti, G, Marcin, S, Martinelli, M, Martinet, N, Metcalf, R B, Monaco, P, Morgante, G, Nadathur, S, Nucita, A A, Patrizii, L, Peel, A, Potter, D, Pourtsidou, A, Pöntinen, M, Reimberg, P, Sánchez, A G, Sakr, Z, Schirmer, M, Sefusatti, E, Sereno, M, Stadel, J, Teyssier, R, Valieri, C, Valiviita, J, Viel, M, Astronomy, Euclid Collaboration, L. Bisigello, C. J. Conselice, M. Bae, 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. Douspi, F. Dubath, C. A. J. Duncan, X. Dupac, S. Dusini, S. Farren, S. Ferriol, M. Fraili, E. Franceschi, P. Franzetti, M. Fumana, B. Garilli, W. Gillard, B. Gilli, C. Giocoli, A. Grazian, F. Grupp, L. Guzzo, S. V. H. Haugan, W. Holme, F. Hormuth, A. Hornstrup, K. Jahnke, M. Kümmel, 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. Rhode, G. Riccio, H. -W. Rix, E. Romelli, M. Roncarelli, C. Rosset, E. Rossetti, R. Saglia, D. Sapone, B. Sartori, P. Schneider, M. Scodeggio, A. Secroun, G. Seidel, C. Sirignano, G. Sirri, L. Stanco, P. Tallada-Crespí, D. Tavagnacco, A. N. Taylor, I. Tereno, R. Toledo-Moreo, F. Torradeflot, I. Tutusau, 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. Graciá-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. Casa, G. Castignani, A. Cooray, J. Coupon, H. M. Courtoi, 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. Macías-Pérez, 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. Pöntinen, P. Reimberg, A. G. Sánchez, Z. Sakr, M. Schirmer, E. Sefusatti, M. Sereno, J. Stadel, R. Teyssier, C. Valieri, J. Valiviita, M. Viel, Bisigello, L., Conselice, C. J., Baes, M., Bolzonella, M., Brescia, M., Cavuoti, S., Cucciati, O., Humphrey, A., Hunt, L. K., Maraston11, C., Pozzetti, L., Tortora, C., van Mierlo, S. E., Aghanim, N., Auricchio, N., Baldi, M., Bender, R., Bodendorf, C., Bonino, D., Branchini, E., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Castander, F. J., Castellano, M., Cimatti, A., Congedo, G., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Cropper, M., Da Silva, A., Degaudenzi, H., Douspis, M., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Farrens, S., Ferriol, S., Frailis, M., Franceschi, E., Franzetti, P., Fumana, M., Garilli, B., Gillard, W., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Guzzo, L., Haugan, S. V. H., Holmes, W., Hormuth, F., Hornstrup, A., Jahnke, K., Kümmel, M., Kermiche, S., Kiessling, A., Kilbinger, M., Kohley, R., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Marulli, F., Massey, R., Maurogordato, S., Medinaceli, E., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Niemi, S. M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Pettorino, V., Polenta, G., Poncet, M., Popa, L., Raison, F., Renzi, A., Rhodes, J., Riccio, G., Rix, H. -W., Romelli, E., Roncarelli, M., Rosset, C., Rossetti, E., Saglia, R., Sapone, D., Sartoris, B., Schneider, P., Scodeggio, M., Secroun, A., Seidel, G., Sirignano, C., Sirri, G., Stanco, L., Tallada-Crespí, P., Tavagnacco, D., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Wang, Y., Zacchei, A., Zamorani, G., Zoubian, J., Andreon, S., Boucaud, S. Bardelli A., Colodro-Conde, C., Di Ferdinando, D., Graciá-Carpio, J., Lindholm, V., Maino, D., Mei, S., Scottez, V., Sureau, F., Tenti, M., Zucca, E., Borlaff, A. S., Ballardini, M., Biviano, A., Bozzo, E., Burigana, C., Cabanac, R., Cappi, A., Carvalho, C. S., Casas, S., Castignani, G., Cooray, A., Coupon, J., Courtois, H. M., Cuby, J., Davini, S., De Lucia, G., Desprez, G., Dole, H., Escartin, J. A., Escoffier, S., Farina, M., Fotopoulou, S., Ganga, K., Garcia-Bellido, J., George, K., Giacomini, F., Gozaliasl, G., Hildebrandt, H., Hook, I., Huertas-Company, M., Kansal, V., Keihanen, E., Kirkpatrick, C. C., Loureiro, A., Macías-Pérez, J. F., Magliocchetti, M., Mainetti, G., Marcin, S., Martinelli, M., Martinet, N., Metcalf, R. B., Monaco, P., Morgante, G., Nadathur, S., Nucita, A. A., Patrizii, L., Peel, A., Potter, D., Pourtsidou, A., Pöntinen, M., Reimberg, P., Sánchez, A. G., Sakr, Z., Schirmer, M., Sefusatti, E., Sereno, M., Stadel, J., Teyssier, R., Valieri, C., Valiviita111, J., Viel, M., Institut de Physique des 2 Infinis de Lyon (IP2I Lyon), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Astrophysique Interprétation Modélisation (AIM (UMR_7158 / UMR_E_9005 / UM_112)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Centre de Physique des Particules de Marseille (CPPM), Aix Marseille Université (AMU)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Observatoire de la Côte d'Azur (OCA), Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Centre National d'Études Spatiales [Toulouse] (CNES), AstroParticule et Cosmologie (APC (UMR_7164)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Institut d'Astrophysique de Paris (IAP), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut de recherche en astrophysique et planétologie (IRAP), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Astrophysique de Marseille (LAM), Aix Marseille Université (AMU)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Etude du Rayonnement et de la Matière en Astrophysique (LERMA), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université de Cergy Pontoise (UCP), Université Paris-Seine-Université Paris-Seine-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physique Subatomique et de Cosmologie (LPSC), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Centre de Calcul de l'IN2P3 (CC-IN2P3), and Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)

Subjects
FOS: Physical sciences, Euclid, Astronomy and Astrophysics, Astrophysics::Cosmology and Extragalactic Astrophysics, galaxies: general, galaxies: general, galaxies: photometry, galaxies: star formation, galaxies: evolution, Astrophysics - Astrophysics of Galaxies, Cosmology, galaxies: photometry, Physics and Astronomy, [SDU]Sciences of the Universe [physics], Space and Planetary Science, Astrophysics of Galaxies (astro-ph.GA), galaxies: star formation, photometry [galaxies], star formation [galaxies], galaxies: evolution, Astrophysics::Galaxy Astrophysics, evolution [galaxies], general [galaxies]
Abstract

Next generation telescopes, like Euclid, Rubin/LSST, and Roman, will open new windows on the Universe, allowing us to infer physical properties for tens of millions of galaxies. Machine learning methods are increasingly becoming the most efficient tools to handle this enormous amount of data, because they are often faster and more accurate than traditional methods. We investigate how well redshifts, stellar masses, and star-formation rates (SFR) can be measured with deep learning algorithms for observed galaxies within data mimicking the Euclid and Rubin/LSST surveys. We find that Deep Learning Neural Networks and Convolutional Neutral Networks (CNN), which are dependent on the parameter space of the training sample, perform well in measuring the properties of these galaxies and have a better accuracy than methods based on spectral energy distribution fitting. CNNs allow the processing of multi-band magnitudes together with $H_{\scriptscriptstyle\rm E}$-band images. We find that the estimates of stellar masses improve with the use of an image, but those of redshift and SFR do not. Our best results are deriving i) the redshift within a normalised error of less than 0.15 for 99.9$\%$ of the galaxies with S/N>3 in the $H_{\scriptscriptstyle\rm E}$-band; ii) the stellar mass within a factor of two ($\sim0.3 \rm dex$) for 99.5$\%$ of the considered galaxies; iii) the SFR within a factor of two ($\sim0.3 \rm dex$) for $\sim$70$\%$ of the sample. We discuss the implications of our work for application to surveys as well as how measurements of these galaxy parameters can be improved with deep learning., accepted for publication in MNRAS, 21 pages, 22 figures, 6 tables

Published
2023
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8. Euclid preparation : XX. The Complete Calibration of the Color-Redshift Relation survey: LBT observations and data release

Author

Euclid Collaboration, Saglia, R., De Nicola, S., Fabricius, M., Guglielmo, V., Snigula, J., Zöller, R., Bender, R., Heidt, J., Masters, D., Stern, D., Paltani, S., Amara, A., Auricchio, N., Baldi, M., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Castellano, M., Cavuoti, S., Cledassou, R., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Cropper, M., Da Silva, A., Degaudenzi, H., Douspis, M., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Farrens, S., Frailis, M., Franceschi, E., Galeotta, S., Garilli, B., Gillard, W., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Haugan, S. V. H., Hoekstra, H., Holmes, W., Hormuth, F., Hornstrup, A., Jahnke, K., Kümmel, M., Kermiche, S., Kiessling, A., Kunz, M., Kurki-Suonio, H., Laureijs, R., Ligori, S., Lilje, P. B., Lloro, I., Maiorano, E., Marggraf, O., Markovic, K., Marulli, F., Massey, R., McCracken, H. J., Melchior, M., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Niemi, S. M., Padilla, C., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Pires, S., Poncet, M., Popa, L., Pozzetti, L., Raison, F., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Rossetti, E., Sapone, D., Sartoris, B., Schneider, P., Secroun, A., Seidel, G., Sirignano, C., Sirri, G., Stanco, L., Tallada-Crespí, P., Tavagnacco, D., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Wang, Y., Zacchei, A., Zamorani, G., Zoubian, J., Andreon, S., Bardelli, S., Graciá-Carpio, J., Maino, D., Mauri, N., Tramacere, A., Zucca, E., Ayllon, A. Alvarez, Aussel, H., Baccigalupi, C., Balaguera-Antolínez, A., Ballardini, M., Biviano, A., Bolzonella, M., Bozzo, E., Burigana, C., Cabanac, R., Cappi, A., Carvalho, C. S., Casas, S., Castignani, G., Cooray, A., Coupon, J., Courtois, H. M., Davini, S., Desprez, G., Dole, H., Escartin, J. A., Escoffier, S., Farina, M., Fotopoulou, S., Ganga, K., Garcia-Bellido, J., George, K., Giacomini, F., Gozaliasl, G., Hildebrandt, H., Hook, I., Ilbert, O., Kansal, V., Kashlinsky, A., Keihanen, E., Kirkpatrick, C. C., Loureiro, A., Macías-Pérez, J., Magliocchetti, M., Mainetti, G., Maoli, R., Martinelli, M., Martinet, N., Metcalf, R. B., Morgante, G., Nadathur, S., Nucita, A. A., Patrizii, L., Popa, V., Porciani, C., Potter, D., Pourtsidou, A., Reimberg, P., Sánchez, A. G., Sakr, Z., Schirmer, M., Sefusatti, E., Sereno, M., Stadel, J., Teyssier, R., Valieri, C., Valiviita, J., Veropalumbo, A., Viel, M., Department of Physics, Research Program in Systems Oncology, Helsinki Institute of Physics, Saglia, R., De Nicola, S., Fabricius, M., Guglielmo, V., Snigula, J., Z??ller, R., Bender, R., Heidt, J., Masters, D., Stern, D., Paltani, S., Amara, A., Auricchio, N., Baldi, M., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Castellano, M., Cavuoti, S., Cledassou, R., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Cropper, M., Da Silva, A., Degaudenzi, H., Douspis, M., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Farrens, S., Frailis, M., Franceschi, E., Galeotta, S., Garilli, B., Gillard, W., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Haugan, S. V. H., Hoekstra, H., Holmes, W., Hormuth, F., Hornstrup, A., Jahnke, K., K??mmel, M., Kermiche, S., Kiessling, A., Kunz, M., Kurki-Suonio, H., Laureijs, R., Ligori, S., Lilje, P. B., Lloro, I., Maiorano, E., Marggraf, O., Markovic, K., Marulli, F., Massey, R., Mccracken, H. J., Melchior, M., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Niemi, S. M., Padilla, C., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Pires, S., Poncet, M., Popa, L., Pozzetti, L., Raison, F., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Rossetti, E., Sapone, D., Sartoris, B., Schneider, P., Secroun, A., Seidel, G., Sirignano, C., Sirri, G., Stanco, L., Tallada-Cresp??, P., Tavagnacco, D., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Wang, Y., Zacchei, A., Zamorani, G., Zoubian, J., Andreon, S., Bardelli, S., Graci??-Carpio, J., Maino, D., Mauri, N., Tramacere, A., Zucca, E., Alvarez Ayllon, A., Aussel, H., Baccigalupi, C., Balaguera-Antol??nez, A., Ballardini, M., Biviano, A., Bolzonella, M., Bozzo, E., Burigana, C., Cabanac, R., Cappi, A., Carvalho, C. S., Casas, S., Castignani, G., Cooray, A., Coupon, J., Courtois, H. M., Davini, S., Desprez, G., Dole, H., Escartin, J. A., Escoffier, S., Farina, M., Fotopoulou, S., Ganga, K., Garcia-Bellido, J., George, K., Giacomini, F., Gozaliasl, G., Hildebrandt, H., Hook, I., Ilbert, O., Kansal, V., Kashlinsky, A., Keihanen, E., Kirkpatrick, C. C., Loureiro, A., Mac??as-P??rez, J., Magliocchetti, M., Mainetti, G., Maoli, R., Martinelli, M., Martinet, N., Metcalf, R. B., Morgante, G., Nadathur, S., Nucita, A. A., Patrizii, L., Popa, V., Porciani, C., Potter, D., Pourtsidou, A., Reimberg, P., S??nchez, A. G., Sakr, Z., Schirmer, M., Sefusatti, E., Sereno, M., Stadel, J., Teyssier, R., Valieri, C., Valiviita, J., Veropalumbo, A., Viel, M., Centre de Physique des Particules de Marseille (CPPM), Aix Marseille Université (AMU)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Astrophysique de Marseille (LAM), Aix Marseille Université (AMU)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Euclid, Intelligent Systems, Astronomy, R. Saglia, S. De Nicola, M. Fabriciu, V. Guglielmo, J. Snigula, R. Zöller, R. Bender, J. Heidt, D. Master, D. Stern, S. Paltani, A. Amara, N. Auricchio, M. Baldi, C. Bodendorf, D. Bonino, E. Branchini, M. Brescia, J. Brinchmann, S. Camera, V. Capobianco, C. Carbone, J. Carretero, M. Castellano, S. Cavuoti, R. Cledassou, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, L. Corcione, F. Courbin, M. Cropper, A. Da Silva, H. Degaudenzi, M. Douspi, F. Dubath, C. A. J. Duncan, X. Dupac, S. Dusini, S. Farren, M. Fraili, E. Franceschi, S. Galeotta, B. Garilli, W. Gillard, B. Gilli, C. Giocoli, A. Grazian, F. Grupp, S. V. H. Haugan, H. Hoekstra, W. Holme, F. Hormuth, A. Hornstrup, K. Jahnke, M. Kümmel, S. Kermiche, A. Kiessling, M. Kunz, H. Kurki-Suonio, R. Laureij, S. Ligori, P. B. Lilje, I. Lloro, E. Maiorano, O. Marggraf, K. Markovic, F. Marulli, R. Massey, H. J. McCracken, M. Melchior, G. Meylan, M. Moresco, L. Moscardini, E. Munari, S. M. Niemi, C. Padilla, F. Pasian, K. Pedersen, W. J. Percival, V. Pettorino, S. Pire, M. Poncet, L. Popa, L. Pozzetti, F. Raison, A. Renzi, J. Rhode, G. Riccio, E. Romelli, E. Rossetti, D. Sapone, B. Sartori, P. Schneider, A. Secroun, G. Seidel, C. Sirignano, G. Sirri, L. Stanco, P. Tallada-Crespí, D. Tavagnacco, A. N. Taylor, I. Tereno, R. Toledo-Moreo, F. Torradeflot, I. Tutusau, E. A. Valentijn, L. Valenziano, T. Vassallo, Y. Wang, A. Zacchei, G. Zamorani, J. Zoubian, S. Andreon, S. Bardelli, J. Graciá-Carpio, D. Maino, N. Mauri, A. Tramacere, E. Zucca, A. Alvarez Ayllon, H. Aussel, C. Baccigalupi, A. Balaguera-Antolínez, M. Ballardini, A. Biviano, M. Bolzonella, E. Bozzo, C. Burigana, R. Cabanac, A. Cappi, C. S. Carvalho, S. Casa, G. Castignani, A. Cooray, J. Coupon, H. M. Courtoi, S. Davini, 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, O. Ilbert, V. Kansal, A. Kashlinsky, E. Keihanen, C. C. Kirkpatrick, A. Loureiro, J. Macías-Pérez, M. Magliocchetti, G. Mainetti, R. Maoli, M. Martinelli, N. Martinet, R. B. Metcalf, G. Morgante, S. Nadathur, A. A. Nucita, L. Patrizii, V. Popa, C. Porciani, D. Potter, A. Pourtsidou, P. Reimberg, A. G. Sánchez, Z. Sakr, M. Schirmer, E. Sefusatti, M. Sereno, J. Stadel, R. Teyssier, C. Valieri, J. Valiviita, A. Veropalumbo, M. Viel, Zöller, R., Kümmel, M., Tallada-Crespí, P., Graciá-Carpio, J., Balaguera-Antolínez, A., Macías-Pérez, J., and Sánchez, A. G.

Subjects
Cosmology and Nongalactic Astrophysics (astro-ph.CO), catalogs, Galaxies: distances and redshifts, surveys, Astrophysics - cosmology and nongalactic astrophysics, Astrophysics::Instrumentation and Methods for Astrophysics, Euclid, FOS: Physical sciences, Galaxies - distances and redshifts, Astronomy and Astrophysics, cosmos, Astrophysics::Cosmology and Extragalactic Astrophysics, Surveys, 115 Astronomy, Space science, Cosmology, deep imaging survey, Space and Planetary Science, distances and redshifts, catalogs, surveys [galaxies], distances and redshifts [Galaxies], galaxies: distances and redshifts, Catalogs, [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph], Astrophysics::Galaxy Astrophysics
Abstract

The Complete Calibration of the Color-Redshift Relation survey (C3R2) is a spectroscopic programme designed to empirically calibrate the galaxy color-redshift relation to the Euclid depth (I_E=24.5), a key ingredient for the success of Stage IV dark energy projects based on weak lensing cosmology. A spectroscopic calibration sample as representative as possible of the galaxies in the Euclid weak lensing sample is being collected, selecting galaxies from a self-organizing map (SOM) representation of the galaxy color space. Here, we present the results of a near-infrared H- and K-bands spectroscopic campaign carried out using the LUCI instruments at the LBT. For a total of 251 galaxies, we present new highly-reliable redshifts in the 1.3, A&A language edited version

Published
2022
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9. Euclid preparation: IX. EuclidEmulator2 -- Power spectrum emulation with massive neutrinos and self-consistent dark energy perturbations

Author

Yu Wang, Marco Castellano, J. Dakin, Natalia Auricchio, Enrico Bozzo, R. Cledassou, Mischa Knabenhans, Stefano Cavuoti, A. Da Silva, E. Medinaceli, C. Padilla, Benjamin J. Metcalf, Andrea Zacchei, T. Vassallo, Rafael Toledo-Moreo, C. Neissner, Giulio Fabbian, Chiara Sirignano, L. Stanco, E. Keihänen, Enzo Branchini, A. Secroun, N. Martinet, L. Whittaker, S. Serrano, M. Frailis, Carlo Baccigalupi, Ismael Tereno, Michele Moresco, Ralf Bender, Pedro G. Ferreira, S. de la Torre, M. Tenti, A. Cappi, W. A. Holmes, Jason Rhodes, J. Carretero, Luca Valenziano, S. Niemi, E. Romelli, Carlo Burigana, Gianluca Castignani, Joachim Stadel, Federico Marulli, Jussi Valiviita, Luigi Guzzo, F. Raison, Hélène M. Courtois, K. Markovic, C. Colodro-Conde, Carlo Giocoli, Leonardo Corcione, Ricard Casas, E. Franceschi, Stefano Andreon, Javier Graciá-Carpio, F. Sureau, Sebastiano Ligori, A. Renzi, C. A. J. Duncan, Hannu Kurki-Suonio, Remi A. Cabanac, Y. Copin, S. Paltani, S. Casas, N. Welikala, E. Munari, G. Sirri, Stefano Camera, Matteo Viel, S. Galeotta, C. J. Conselice, B. Garilli, Peter W. Battaglia, Doug Potter, B. Morin, Ole Marggraf, C. S. Carvalho, Domenico Sapone, I. Lloro, Jean Coupon, Andy Taylor, Ariel G. Sánchez, E. Zucca, Sandrine Pires, Ghassem Gozaliasl, X. Dupac, Lauro Moscardini, Aurel Schneider, Mauro Roncarelli, S. Farrens, Roberto P. Saglia, F. Grupp, Stefano Marelli, Pablo Fosalba, L. Conversi, V. Capobianco, Davide Maino, Felix Hormuth, S. Maurogordato, L. Patrizii, Richard Massey, Knud Jahnke, M. Poncet, H. Israel, P. Tallada Crespí, Giuseppe Riccio, Massimo Brescia, D. Di Ferdinando, S. Bardelli, G. Polenta, B. Kubik, Romain Teyssier, Marco Baldi, F. Torradeflot, Peter Schneider, Fabio Pasian, Steen Hannestad, G. Meylan, V. Scottez, Martin Kunz, A. Balaguera-Antolínez, Valeria Pettorino, Carmelita Carbone, Fabio Finelli, C. Bodendorf, N. Mauri, Massimo Meneghetti, Thomas Tram, P. B. Lilje, Andrea Biviano, G. Congedo, S. Kermiche, C. C. Kirkpatrick, Knabenhans, M., Stadel, J., Potter, D., Dakin, J., Hannestad, S., Tram, T., Marelli, S., Schneider, A., Teyssier, R., Fosalba, P., Andreon, S., Auricchio, N., Baccigalupi, C., Balaguera-Antolinez, A., Baldi, M., Bardelli, S., Battaglia, P., Bender, R., Biviano, A., Bodendorf, C., Bozzo, E., Branchini, E., Brescia, M., Burigana, C., Cabanac, R., Camera, S., Capobianco, V., Cappi, A., Carbone, C., Carretero, J., Carvalho, C. S., Casas, R., Casas, S., Castellano, M., Castignani, G., Cavuoti, S., Cledassou, R., Colodro-Conde, C., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Coupon, J., Courtois, H. M., Da Silva, A., De La Torre, S., Ferdinando, D. D., Duncan, C. A. J., Dupac, X., Fabbian, G., Farrens, S., Ferreira, P. G., Finelli, F., Frailis, M., Franceschi, E., Galeotta, S., Garilli, B., Giocoli, C., Gozaliasl, G., Gracia-Carpio, J., Grupp, F., Guzzo, L., Holmes, W., Hormuth, F., Israel, H., Jahnke, K., Keihanen, E., Kermiche, S., Kirkpatrick, C. C., Kubik, B., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Maino, D., Marggraf, O., Markovic, K., Martinet, N., Marulli, F., Massey, R., Mauri, N., Maurogordato, S., Medinaceli, E., Meneghetti, M., Metcalf, B., Meylan, G., Moresco, M., Morin, B., Moscardini, L., Munari, E., Neissner, C., Niemi, S. M., Padilla, C., Paltani, S., Pasian, F., Patrizii, L., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Raison, F., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Saglia, R., Sanchez, A. G., Sapone, D., Schneider, P., Scottez, V., Secroun, A., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Sureau, F., Crespi, P. T., Taylor, A. N., Tenti, M., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Valenziano, L., Valiviita, J., Vassallo, T., Viel, M., Wang, Y., Welikala, N., Whittaker, L., Zacchei, A., Zucca, E., Joseph Louis LAGRANGE (LAGRANGE), Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Observatoire de la Côte d'Azur, COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Université Côte d'Azur (UCA)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Euclid Collaboration, M. Knabenhan, J. Stadel, D. Potter, J. Dakin, S. Hannestad, T. Tram, S. Marelli, A. Schneider, R. Teyssier, S. Andreon, N. Auricchio, C. Baccigalupi, A. Balaguera-Antolínez, M. Baldi, S. Bardelli, P. Battaglia, R. Bender, A. Biviano, C. Bodendorf, E. Bozzo, E. Branchini, M. Brescia, C. Burigana, R. Cabanac, S. Camera, V. Capobianco, A. Cappi, C. Carbone, J. Carretero, C. S. Carvalho, R. Casa, S. Casa, M. Castellano, G. Castignani, S. Cavuoti, R. Cledassou, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, L. Corcione, J. Coupon, H. M. Courtoi, A. Da Silva, S. de la Torre, D. Di Ferdinando, C. A. J. Duncan, X. Dupac, G. Fabbian, S. Farren, P. G. Ferreira, F. Finelli, M. Fraili, E. Franceschi, S. Galeotta, B. Garilli, C. Giocoli, G. Gozaliasl, J. Graciá-Carpio, F. Grupp, L. Guzzo, W. Holme, F. Hormuth, H. Israel, K. Jahnke, E. Keihanen, S. Kermiche, C. C. Kirkpatrick, B. Kubik, M. Kunz, H. Kurki-Suonio, S. Ligori, P. B. Lilje, I. Lloro, D. Maino, O. Marggraf, K. Markovic, N. Martinet, F. Marulli, R. Massey, N. Mauri, S. Maurogordato, E. Medinaceli, M. Meneghetti, B. Metcalf, G. Meylan, M. Moresco, B. Morin, L. Moscardini, E. Munari, C. Neissner, S. M. Niemi, C. Padilla, S. Paltani, F. Pasian, L. Patrizii, V. Pettorino, S. Pire, G. Polenta, M. Poncet, F. Raison, A. Renzi, J. Rhode, G. Riccio, E. Romelli, M. Roncarelli, R. Saglia, A. G. Sánchez, D. Sapone, P. Schneider, V. Scottez, A. Secroun, S. Serrano, C. Sirignano, G. Sirri, L. Stanco, F. Sureau, P. Tallada Crespí, A. N. Taylor, M. Tenti, I. Tereno, R. Toledo-Moreo, F. Torradeflot, L. Valenziano, J. Valiviita, T. Vassallo, M. Viel, Y. Wang, N. Welikala, L. Whittaker, A. Zacchei, E. Zucca, Laboratoire d'Astrophysique de Marseille (LAM), Aix Marseille Université (AMU)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de la Côte d'Azur, COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS), Department of Physics, Research Program in Systems Oncology, and Helsinki Institute of Physics

Subjects
Cosmology and Nongalactic Astrophysics (astro-ph.CO), General relativity, CODE, FOS: Physical sciences, Astrophysics::Cosmology and Extragalactic Astrophysics, Parameter space, KATRIN, 01 natural sciences, methods: numerical, Settore FIS/05 - Astronomia e Astrofisica, MATTER POWER SPECTRUM, 0103 physical sciences, POLYNOMIAL CHAOS, ALGORITHM, cosmological parameters, OPTIMIZATION, 010303 astronomy & astrophysics, Physics, methods: statistical, 010308 nuclear & particles physics, Matter power spectrum, Spectral density, Astronomy and Astrophysics, N-BODY SIMULATIONS, 115 Astronomy, Space science, Redshift, Computational physics, Space and Planetary Science, [SDU]Sciences of the Universe [physics], Metric (mathematics), Dark energy, astro-ph.CO, cosmological parameter, large-scale structure of Universe, Neutrino, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

We present a new, updated version of the EuclidEmulator (called EuclidEmulator2), a fast and accurate predictor for the nonlinear correction of the matter power spectrum. Percent-level accurate emulation is now supported in the eight-dimensional parameter space of $w_0w_a$CDM$+\sum m_\nu$models between redshift $z=0$ and $z=3$ for spatial scales within the range 0.01 $h$/Mpc $\leq k \leq$ 10 $h$/Mpc. In order to achieve this level of accuracy, we have had to improve the quality of the underlying N-body simulations used as training data: (1) we use self-consistent linear evolution of non-dark matter species such as massive neutrinos, photons, dark energy and the metric field, (2) we perform the simulations in the so-called N-body gauge, which allows one to interpret the results in the framework of general relativity, (3) we run over 250 high-resolution simulations with $3000^3$ particles in boxes of 1 (Gpc/$h$)${}^3$ volumes based on paired-and-fixed initial conditions and (4) we provide a resolution correction that can be applied to emulated results as a post-processing step in order to drastically reduce systematic biases on small scales due to residual resolution effects in the simulations. We find that the inclusion of the dynamical dark energy parameter $w_a$ significantly increases the complexity and expense of creating the emulator. The high fidelity of EuclidEmulator2 is tested in various comparisons against N-body simulations as well as alternative fast predictors like Halofit, HMCode and CosmicEmu. A blind test is successfully performed against the Euclid Flagship v2.0 simulation. Nonlinear correction factors emulated with EuclidEmulator2 are accurate at the level of 1% or better for 0.01 $h$/Mpc $\leq k \leq$ 10 $h$/Mpc and $z\leq3$ compared to high-resolution dark matter only simulations. EuclidEmulator2 is publicly available at https://github.com/miknab/EuclidEmulator2 ., Comment: 28 pages, 19 figures, submitted to MNRAS

Published
2020
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10. Using contextual data to predict risky driving events: A novel methodology from explainable artificial intelligence.

Author

Masello L, Castignani G, Sheehan B, Guillen M, and Murphy F

Subjects
Humans, Artificial Intelligence, Risk Factors, Risk Assessment, Accidents, Traffic prevention & control, Automobile Driving
Abstract

Usage-based insurance has allowed insurers to dynamically tailor insurance premiums by understanding when and how safe policyholders drive. However, telematics information can also be used to understand the driving contexts experienced by the driver within each trip (e.g., road types, weather, traffic). Since different combinations of these conditions affect exposure to accidents, this understanding introduces predictive opportunities in driving risk assessment. This paper investigates the relationships between driving context combinations and risk using a naturalistic driving dataset of 77,859 km. In particular, XGBoost and Random Forests are used to determine the predictive significance of driving contexts for near-misses, speeding and distraction events. Moreover, the most important contextual factors in predicting these risky events are identified and ranked through Shapley Additive Explanations. The results show that the driving context has significant power in predicting driving risk. Speed limit, weather temperature, wind speed, traffic conditions and road slope appear in the top ten most relevant features for most risky events. Analysing contextual feature variations and their influence on risky events showed that low-speed limits increase the predicted frequency of speeding and phone unlocking events, whereas high-speed limits decrease harsh accelerations. Low temperatures decrease the expected frequency of harsh manoeuvres, and precipitations increase harsh acceleration, harsh braking, and distraction events. Furthermore, road slope, intersections and pavement quality are the most critical factors among road layout attributes. The methodology presented in this study aims to support road safety stakeholders and insurers by providing insights to study the contextual risk factors that influence road accident frequency and driving risk., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Author affiliations with Motion-S: Leandro Masello and German Castignani, (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Published
2023
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11. On the impact of advanced driver assistance systems on driving distraction and risky behaviour: An empirical analysis of irish commercial drivers.

Author

Masello L, Sheehan B, Castignani G, Shannon D, and Murphy F

Subjects
Humans, Protective Devices, Motor Vehicles, Risk-Taking, Accidents, Traffic prevention & control, Automobile Driving
Abstract

Advanced driver assistance systems (ADAS) present promising benefits in mitigating road collisions. However, these benefits are limited when risky drivers continue engaging in distraction events. While there is evidence that real-time warnings help improve driving behaviour, the sustained benefits of warning-based ADAS on reducing driving distraction in light commercial vehicle (LCV) drivers remain unclear. This research determines the effect of receiving instant distraction warnings over two years using a naturalistic driving dataset comprising around one million trips from 373 LCV drivers in the Republic of Ireland. Furthermore, the study applies Association Rule Mining (ARM) to find the contextual variables (e.g., speed limit, road type, traffic conditions) that increase the likelihood of distraction events. The results show that warning-based ADAS providing real-time warnings helps reduce distraction events triggering driver inattention, forward collision, and lane departure warnings. Over half of the studied fleet reduced these warnings by at least 50% - lane departure after two months and driver inattention and forward collision after six months. It is found that both passive and active monitoring systems, coupled with coaching and rewards, significantly reduce aggressive driving behaviours tied to harsh acceleration (by 76%) and harsh braking (by 65%). The results of ARM show that the driving context introduces explanatory information for road safety programs. Low-speed urban roads and the summer season increase the likelihood of driver inattention and forward collision warnings. In contrast, high-speed rural roads increase the likelihood of lane departure warnings. These research findings support road safety stakeholders in developing risk assessments based on warning-based ADAS, targeted campaigns to reduce driving distraction, and driving coaching programs., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Published
2023
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12. Regulatory and Technical Constraints: An Overview of the Technical Possibilities and Regulatory Limitations of Vehicle Telematic Data.

Author

McDonnell K, Murphy F, Sheehan B, Masello L, Castignani G, and Ryan C

Abstract

A telematics device is a vehicle instrument that comes preinstalled by the vehicle manufacturer or can be added later. The device records information about driving behavior, including speed, acceleration, and turning force. When connected to vehicle computers, the device can also provide additional information regarding the mechanical usage and condition of the vehicle. All of this information can be transmitted to a central database via mobile networks. The information provided has led to new services such as Usage Based Insurance (UBI). A range of consultants, industry commentators and academics have produced an abundance of projections on how telematics information will allow the introduction of services from personalized insurance, bespoke entertainment and advertise and vehicle energy optimization, particularly for Electric Vehicles (EVs). In this paper we examine these potential services against a backdrop of nascent regulatory limitations and against the technical capacity of the devices. Using a case study approach, we examine three applications that can use telematics information. We find that the expectations of service providers will be significantly tempered by regulatory and technical hurdles. In our discussion we detail these limitations and suggest a more realistic rollout of ancillary services.

Published
2021
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