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1. The Strong Gravitational Lens Finding Challenge

Author

Eric Jullo, Manuel Blanco Valentin, Crescenzo Tortora, Alessandro Sonnenfeld, Emmanuel Bertin, F. Bellagamba, Chun-Liang Li, N. J. Jackson, Remi A. Cabanac, Francois Lanusse, G. A. Verdoes Kleijn, Frederic Courbin, Stephen Serjeant, Massimo Meneghetti, Raphael Gavazzi, Clecio R. Bom, Santiago Velasco-Forero, Quanbin Ma, G. Vernardos, Jean-Paul Kneib, Marc Huertas-Company, D. Tuccillo, Etienne Decencière, Mario Geiger, Martin Makler, R. B. Metcalf, Rémi Flamary, Camille Avestruz, Nan Li, Luitje Koopmans, A. Davies, Christoph Schäfer, P. Hartley, C. Jacobs, C. E. Petrillo, M. Lightman, Amitpal S. Tagore, 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), Centre de Morphologie Mathématique (CMM), Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Joseph Louis LAGRANGE (LAGRANGE), 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), Observatoire de la Côte d'Azur (OCA), Institut national des sciences de l'Univers (INSU - CNRS)-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 et Atmosphères = Laboratory for Studies of Radiation and Matter in Astrophysics and Atmospheres (LERMA), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY), Astronomy, Discrete Technology and Production Automation, Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS), École des Mines de Paris, Université Nice Sophia Antipolis (... - 2019) (UNS), Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Observatoire de la Côte d'Azur, Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Aix Marseille Université (AMU)-Centre National d'Études Spatiales [Toulouse] (CNES), Laboratoire d'Etude du Rayonnement et de la Matière en Astrophysique (LERMA (UMR_8112)), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, PSL Research University (PSL)-PSL Research University (PSL)-Université de Cergy Pontoise (UCP), Université Paris-Seine-Université Paris-Seine-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), 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), Metcalf, R. B., Meneghetti, M., Avestruz, C., Bellagamba, F., Bom, C. R., Bertin, E., Cabanac, R., Courbin, F., Davies, A., Decencière, E., Flamary, R., Gavazzi, R., Geiger, M., Hartley, P., Huertas-Company, M., Jackson, N., Jacobs, C., Jullo, E., Kneib, J.-P., Koopmans, L. V. E., Lanusse, F., Li, C.-L., Ma, Q., Makler, M., Li, N., Lightman, M., Petrillo, C. E., Serjeant, S., Schäfer, C., Sonnenfeld, A., Tagore, A., Tortora, C., Tuccillo, D., Valentín, M. B., Velasco-Forero, S., Verdoes Kleijn, G. A., Vernardos, G., Institut national des sciences de l'Univers (INSU - CNRS)-Université Toulouse III - Paul Sabatier (UT3), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS), 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), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY)

Subjects
Cosmology and Nongalactic Astrophysics (astro-ph.CO), FOS: Physical sciences, Astrophysics, Astrophysics::Cosmology and Extragalactic Astrophysics, 01 natural sciences, Convolutional neural network, law.invention, gravitational lensing: strong – methods: data analysis, law, 0103 physical sciences, Computer vision, [PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det], Instrumentation and Methods for Astrophysics (astro-ph.IM), 010303 astronomy & astrophysics, Physics, 010308 nuclear & particles physics, business.industry, gravitational lensing: strong, Astronomy and Astrophysics, Astrophysics - Astrophysics of Galaxies, methods: data analysis, Lens (optics), Data flow diagram, Support vector machine, Identification (information), Gravitational lens, Space and Planetary Science, Astrophysics of Galaxies (astro-ph.GA), Noise (video), Artificial intelligence, business, Astrophysics - Instrumentation and Methods for Astrophysics, [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph], Test data, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

Large scale imaging surveys will increase the number of galaxy-scale strong lensing candidates by maybe three orders of magnitudes beyond the number known today. Finding these rare objects will require picking them out of at least tens of millions of images and deriving scientific results from them will require quantifying the efficiency and bias of any search method. To achieve these objectives automated methods must be developed. Because gravitational lenses are rare objects reducing false positives will be particularly important. We present a description and results of an open gravitational lens finding challenge. Participants were asked to classify 100,000 candidate objects as to whether they were gravitational lenses or not with the goal of developing better automated methods for finding lenses in large data sets. A variety of methods were used including visual inspection, arc and ring finders, support vector machines (SVM) and convolutional neural networks (CNN). We find that many of the methods will be easily fast enough to analyse the anticipated data flow. In test data, several methods are able to identify upwards of half the lenses after applying some thresholds on the lens characteristics such as lensed image brightness, size or contrast with the lens galaxy without making a single false-positive identification. This is significantly better than direct inspection by humans was able to do. (abridged), 22 pages, 16 figures, 4 tables, accepted version for A&A

Published
2019
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2. The structural properties of classical bulges and discs from z ∼ 2

Author

Anton M. Koekemoer, Dale D. Kocevski, Pablo G. Pérez-González, Francesco Shankar, Joel R. Primack, Paola Dimauro, Aldo Rodríguez-Puebla, Ulrike Kuchner, Sandra M. Faber, Fernando Buitrago, Marc Huertas-Company, Simona Mei, David C. Koo, A. Cattaneo, Mariangela Bernardi, B. Häußler, Guillermo Barro, Fernando Caro, Emanuele Daddi, Mara Salvato, Reynier Peletier, D. Tuccillo, Salvato, M. [https://orcid.org/0000-0001-7116-9303], Buitrago, F. [https://orcid.org/0000-0002-2861-9812], Daddi, E. [https://orcid.org/0000-0002-3331-9590], Peletier, R. [https://orcid.org/0000-0001-7621-947X], Huertas Company, M. [https://orcid.org/0000-0002-1416-8483], Spanish Government, AYA2015-63650-P, Laboratoire d'Etude du Rayonnement et de la Matière en Astrophysique (LERMA (UMR_8112)), Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY), Galaxies, Etoiles, Physique, Instrumentation (GEPI), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Ministerio de Economía y Competitividad (MINECO), Ministerio de Economía y Competitividad (España), and Leverhulme Trust

Subjects
Physics, 010308 nuclear & particles physics, Star formation, structure [Galaxies], FOS: Physical sciences, Astronomy and Astrophysics, Astrophysics, Astrophysics::Cosmology and Extragalactic Astrophysics, evolution [Galaxies], Astrophysics - Astrophysics of Galaxies, 01 natural sciences, Redshift, Galaxy, Space and Planetary Science, Bulge, Astrophysics of Galaxies (astro-ph.GA), 0103 physical sciences, Astrophysics::Earth and Planetary Astrophysics, [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph], 010303 astronomy & astrophysics, ComputingMilieux_MISCELLANEOUS, Astrophysics::Galaxy Astrophysics
Abstract

We study the rest-frame optical mass–size relation of bulges and discs from z ∼ 2 to z ∼ 0 for a complete sample of massive galaxies in the CANDELS fields using two-component Sérsic models. Discs and star-forming galaxies follow similar mass–size relations. The mass–size relation of bulges is less steep than the one of quiescent galaxies (best-fitting slope of 0.7 for quiescent galaxies against 0.4 for bulges). We find little dependence of the structural properties of massive bulges and discs with the global morphology of galaxies (disc versus bulge dominated) and the star formation activity (star-forming versus quiescent). This result suggests similar bulge formation mechanisms for most massive galaxies and also that the formation of the bulge component does not significantly affect the disc structure. Our results pose a challenge to current cosmological models that predict distinct structural properties for stellar bulges arising from mergers and disc instabilities., This work has been essentially funded by to a French national PhD scholarship. MHC is also thankful to Google for their unrestricted gift to explore deep-learning techniques for galaxy evolution. PGP-G acknowledges support from the Spanish Government grant AYA2015-63650-P. FS acknowledges partial support from a Leverhulme Trust Research Fellowship.

Published
2019
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3. Deep Learning Identifies High- z Galaxies in a Central Blue Nugget Phase in a Characteristic Mass Range

Author

Berta Margalef-Bentabol, H. Domínguez-Sánchez, Sharon Lapiner, Joel R. Primack, Zhu Chen, Mariangela Bernardi, Marc Huertas-Company, David C. Koo, Daniel Ceverino, C. T. Lee, D. Tuccillo, Gregory F. Snyder, Avishai Dekel, Raymond C. Simons, Laboratoire d'Etude du Rayonnement et de la Matière en Astrophysique (LERMA (UMR_8112)), Sorbonne Université (SU)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Cergy Pontoise (UCP), Université Paris-Seine-Université Paris-Seine-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Racah Institute of Physics, The Hebrew University of Jerusalem (HUJ), Department of Physics and Astronomy [Philadelphia], University of Pennsylvania [Philadelphia], Fudan University [Shanghai], Jet Propulsion Laboratory (JPL), and NASA-California Institute of Technology (CALTECH)

Subjects
Stellar mass, Phase (waves), FOS: Physical sciences, Astrophysics::Cosmology and Extragalactic Astrophysics, Astrophysics, 01 natural sciences, Convolutional neural network, Luminosity, 0103 physical sciences, Range (statistics), 10. No inequality, 010303 astronomy & astrophysics, Astrophysics::Galaxy Astrophysics, ComputingMilieux_MISCELLANEOUS, Physics, [PHYS]Physics [physics], 010308 nuclear & particles physics, business.industry, Deep learning, Astronomy and Astrophysics, Astrophysics - Astrophysics of Galaxies, Galaxy, Redshift, Space and Planetary Science, Astrophysics of Galaxies (astro-ph.GA), Artificial intelligence, business, [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph]
Abstract

We use machine learning to identify in color images of high-redshift galaxies an astrophysical phenomenon predicted by cosmological simulations. This phenomenon, called the blue nugget (BN) phase, is the compact star-forming phase in the central regions of many growing galaxies that follows an earlier phase of gas compaction and is followed by a central quenching phase. We train a Convolutional Neural Network (CNN) with mock "observed" images of simulated galaxies at three phases of evolution: pre-BN, BN and post-BN, and demonstrate that the CNN successfully retrieves the three phases in other simulated galaxies. We show that BNs are identified by the CNN within a time window of $\sim0.15$ Hubble times. When the trained CNN is applied to observed galaxies from the CANDELS survey at $z=1-3$, it successfully identifies galaxies at the three phases. We find that the observed BNs are preferentially found in galaxies at a characteristic stellar mass range, $10^{9.2-10.3} M_\odot$ at all redshifts. This is consistent with the characteristic galaxy mass for BNs as detected in the simulations, and is meaningful because it is revealed in the observations when the direct information concerning the total galaxy luminosity has been eliminated from the training set. This technique can be applied to the classification of other astrophysical phenomena for improved comparison of theory and observations in the era of large imaging surveys and cosmological simulations., Comment: Accepted for publication in ApJ

Published
2018
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4. A catalog of polychromatic bulge-disk decompositions of ~ 17.600 galaxies in CANDELS

Author

Paola Dimauro, D. Tuccillo, David C. Koo, B. Häußler, Mariangela Bernardi, A. Cattaneo, Dale D. Kocevski, Helena Domínguez-Sánchez, Guillermo Barro, Fernando Buitrago, Marc Huertas-Company, Sandra M. Faber, Emanuele Daddi, Fernando Caro, Aldo Rodríguez-Puebla, Mara Salvato, Francesco Shankar, Joel R. Primack, Anton M. Koekemoer, Christoph T. Lee, Simona Mei, Berta Margalef-Bentabol, Pablo G. Pérez-González, Laboratoire d'Etude du Rayonnement et de la Matière en Astrophysique (LERMA (UMR_8112)), Sorbonne Université (SU)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Cergy Pontoise (UCP), Université Paris-Seine-Université Paris-Seine-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Département d'Astrophysique (ex SAP) (DAP), Institut de Recherches sur les lois Fondamentales de l'Univers (IRFU), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Department of Physics and Astronomy [Philadelphia], University of Pennsylvania [Philadelphia], Galaxies, Etoiles, Physique, Instrumentation (GEPI), Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris Diderot - Paris 7 (UPD7)-Observatoire de Paris, Department of Physics, University of California [Santa Cruz] (UCSC), University of California-University of California, Max-Planck-Institut für Astronomie (MPIA), Max-Planck-Gesellschaft, Max-Planck-Institut für Astrophysik (MPA), École des Mines de Paris, Space Telescope Science Institute (STSci), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Cergy Pontoise (UCP), Université Paris-Seine-Université Paris-Seine-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), University of Pennsylvania, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), University of California [Santa Cruz] (UC Santa Cruz), University of California (UC)-University of California (UC), Centre de Morphologie Mathématique (CMM), and Mines Paris - PSL (École nationale supérieure des mines de Paris)

Subjects
Physics, [PHYS]Physics [physics], 010308 nuclear & particles physics, FOS: Physical sciences, Astronomy and Astrophysics, galaxies: fundamental parameters, Astrophysics, Astrophysics::Cosmology and Extragalactic Astrophysics, 01 natural sciences, Astrophysics - Astrophysics of Galaxies, galaxies: bulges, Redshift, Galaxy, Large sample, Space and Planetary Science, Bulge, galaxies: high-redshift, Astrophysics of Galaxies (astro-ph.GA), 0103 physical sciences, Surface brightness, [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph], 010303 astronomy & astrophysics, Astrophysics::Galaxy Astrophysics
Abstract

Understanding how bulges grow in galaxies is critical step towards unveiling the link between galaxy morphology and star-formation. To do so, it is necessary to decompose large sample of galaxies at different epochs into their main components (bulges and disks). This is particularly challenging, especially at high redshifts, where galaxies are poorly resolved. This work presents a catalog of bulge-disk decompositions of the surface brightness profiles of ~17.600 H-band selected galaxies in the CANDELS fields (F160W, Comment: 18 pages, 18 figures, 3 table, submitted to MNRAS after the first referee's report

Published
2018
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5. Improving galaxy morphologies for SDSS with Deep Learning

Author

D Tuccillo, H. Domínguez Sánchez, Mariangela Bernardi, J. L. Fischer, Marc Huertas-Company, Laboratoire d'Etude du Rayonnement et de la Matière en Astrophysique (LERMA (UMR_8112)), Sorbonne Université (SU)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Cergy Pontoise (UCP), Université Paris-Seine-Université Paris-Seine-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Department of Physics and Astronomy [Philadelphia], and University of Pennsylvania [Philadelphia]

Subjects
media_common.quotation_subject, FOS: Physical sciences, Astrophysics, Astrophysics::Cosmology and Extragalactic Astrophysics, 01 natural sciences, Convolutional neural network, Hubble sequence, [PHYS.ASTR.CO]Physics [physics]/Astrophysics [astro-ph]/Cosmology and Extra-Galactic Astrophysics [astro-ph.CO], symbols.namesake, Bulge, 0103 physical sciences, 010303 astronomy & astrophysics, catalogues, Astrophysics::Galaxy Astrophysics, media_common, Physics, 010308 nuclear & particles physics, business.industry, Deep learning, Astronomy and Astrophysics, Pattern recognition, Astrophysics - Astrophysics of Galaxies, Galaxy, Support vector machine, Space and Planetary Science, Sky, Astrophysics of Galaxies (astro-ph.GA), symbols, galaxies: structure, Artificial intelligence, methods: observational, Precision and recall, business
Abstract

We present a morphological catalogue for $\sim$ 670,000 galaxies in the Sloan Digital Sky Survey in two flavours: T-Type, related to the Hubble sequence, and Galaxy Zoo 2 (GZ2 hereafter) classification scheme. By combining accurate existing visual classification catalogues with machine learning, we provide the largest and most accurate morphological catalogue up to date. The classifications are obtained with Deep Learning algorithms using Convolutional Neural Networks (CNNs). We use two visual classification catalogues, GZ2 and Nair & Abraham (2010), for training CNNs with colour images in order to obtain T-Types and a series of GZ2 type questions (disk/features, edge-on galaxies, bar signature, bulge prominence, roundness and mergers). We also provide an additional probability enabling a separation between pure elliptical (E) from S0, where the T-Type model is not so efficient. For the T-Type, our results show smaller offset and scatter than previous models trained with support vector machines. For the GZ2 type questions, our models have large accuracy (> 97\%), precision and recall values (> 90\%) when applied to a test sample with the same characteristics as the one used for training. The catalogue is publicly released with the paper., 18 pages, 21 figures; Accepted for publication in MNRAS

Published
2017
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6. Deep learning for galaxy surface brightness profile fitting

Author

P. Dimauro, H. Domínguez Sánchez, Santiago Velasco-Forero, Marc Huertas-Company, D. Tuccillo, Etienne Decencière, Laboratoire d'Etude du Rayonnement et de la Matière en Astrophysique (LERMA (UMR_8112)), Sorbonne Université (SU)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Cergy Pontoise (UCP), Université Paris-Seine-Université Paris-Seine-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Centre de Morphologie Mathématique (CMM), MINES ParisTech - École nationale supérieure des mines de Paris, and Decencière, Etienne

Subjects
Domain adaptation, media_common.quotation_subject, Big data, FOS: Physical sciences, Astrophysics, 01 natural sciences, Convolutional neural network, galaxies: high-redshift, 0103 physical sciences, Surface brightness, 010303 astronomy & astrophysics, catalogues, media_common, Physics, Effective radius, 010308 nuclear & particles physics, business.industry, Deep learning, Astronomy and Astrophysics, Astrophysics - Astrophysics of Galaxies, methods: data analysis, Galaxy, [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], Space and Planetary Science, Sky, Astrophysics of Galaxies (astro-ph.GA), [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], galaxies: structure, Artificial intelligence, [PHYS.ASTR] Physics [physics]/Astrophysics [astro-ph], business, [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph], Algorithm
Abstract

Numerous ongoing and future large area surveys (e.g. DES, EUCLID, LSST, WFIRST), will increase by several orders of magnitude the volume of data that can be exploited for galaxy morphology studies. The full potential of these surveys can only be unlocked with the development of automated, fast and reliable analysis methods. In this paper we present DeepLeGATo, a new method for two-dimensional photometric galaxy profile modeling, based on convolutional neural networks. Our code is trained and validated on analytic profiles (HST/CANDELS F160W filter) and it is able to retrieve the full set of parameters of one- component S\'ersic models: total magnitude, effective radius, S\'ersic index, axis ratio. We show detailed comparisons between our code and GALFIT. On simulated data, our method is more accurate than GALFIT and 3000 time faster on GPU (50 times when run on the same CPU). On real data, DeepLeGATo trained on simulations behaves similarly to GALFIT on isolated galaxies. With a fast domain adaptation step made with the 0.1 - 0.8 per cent the size of the training set, our code is easily capable to reproduce the results obtained with GALFIT even on crowded regions. DeepLeGATo does not require any human intervention beyond the training step, rendering it much automated than traditional profiling methods. The development of this method for more complex models (two-component galaxies, variable PSF, dense sky regions) could constitute a fundamental tool in the era of big data in astronomy., Comment: 17 pages, 9 figures

Published
2017

7. Selection of High-z Radio-Loud Quasars, and Their Luminosity Function

Author

C. R. Benn, D. Tuccillo, and José Ignacio González-Serrano

Subjects
QSOS, Physics, education.field_of_study, Astrophysics::High Energy Astrophysical Phenomena, Population, Quasar, Astrophysics::Cosmology and Extragalactic Astrophysics, Astrophysics, Redshift, Flattening, Luminosity, Astrophysics::Solar and Stellar Astrophysics, education, Luminosity distance, Astrophysics::Galaxy Astrophysics, Luminosity function (astronomy)
Abstract

We present the selection techniques based on the use of Neural Networks and on the cross match between FIRST and SDSS that lead to the spectroscopic identification of 15 new Radio Loud QSOs in the redshift range 3. 6 ≤ z ≤ 4. 4. These QSOs did not have previous spectroscopical identification in SDSS or other works. Our selection method is highly complete (97 %) and it allows the estimation of the binned luminosity function of radio-loud quasar at \(z \sim 4\) with unprecedented accuracy. Our luminosity function is compared with the results of other samples of RL QSOs in similar ranges of redshift and with the whole population of QSOs (RL+RQ). The evolution of the luminosity function with redshift was for many years interpreted as a flattening of the bright end slope, but has recently been re-interpreted as strong evolution of the break luminosity for high-z QSOs and our results, for the radio-loud population, are consistent with this. We also find indications of a constant radio-loud fraction for QSOs at high z. Our next investigation will select RL QSOs candidates in the range of redshift 4. 4 ≤ z ≤ 5. 7, and will make use of data in the radio (FIRST), in the optical (SDSS DR10) and in the infrared (UKIDSS Large Area Survey DR10, and WISE).

Published
2016
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8. Il controllo di gestione nel trasporto pubblico locale: il caso CTP Napoli

Author

F. Agliata, M. G. Pirozzi, D. Tuccillo, MAGLIO, ROBERTO, F., Agliata, Maglio, Roberto, M. G., Pirozzi, and D., Tuccillo

Subjects
tpl, balanced scorecard, performance
Abstract

Si applicano le moderne tecniche di controllo di gestione al settore del trasporto pubblico locale, analizzando in particolare la possibilità di adozione della balanced scorecard alla CTP della provincia di Napoli

Published
2011

10. Neural-network selection of high-redshift radio quasars, and the luminosity function at z ∼ 4

Author

D. Tuccillo, Chris Benn, José Ignacio González-Serrano, and Ministerio de Ciencia e Innovación (España)

Subjects
luminosity function, mass function [Galaxies], active [Galaxies], Radio galaxy, FOS: Physical sciences, Astrophysics, Surveys, high-redshift [Galaxies], observations [Cosmology], Selection (genetic algorithm), Luminosity function (astronomy), Physics, Luminous infrared galaxy, Artificial neural network, Galaxies: luminosity function, mass function, Cosmology: observations, Galaxies: high-redshift, Astronomy, general [Quasars], Astronomy and Astrophysics, Quasar, Galaxies: active, Astrophysics - Astrophysics of Galaxies, Redshift, Quasars: general, Space and Planetary Science, Astrophysics of Galaxies (astro-ph.GA)
Abstract

We obtain a sample of 87 radio-loud quasi-stellar objects (QSOs) in the redshift range 3.6 1 mJy) with star-like objects having r < 20.2 in Sloan Digital Sky Survey (SDSS) Data Release 7. Of these 87 QSOs, 80 are spectroscopically classified in previous work (mainly SDSS), and form the training set for a search for additional such sources. We apply our selection to 2916 FIRST-DR7 pairs and find 15 likely candidates. Seven of these are confirmed as high-redshift quasars, bringing the total to 87. The candidates were selected using a neural-network, which yields 97 per cent completeness (fraction of actual high-z QSOs selected as such) and an efficiency (fraction of candidates which are high-z QSOs) in the range of 47-60 per cent. We use this sample to estimate the binned optical luminosity function (LF) of radio-loud QSOs at z ∼ 4, and also the LF of the total QSO population and its comoving density. Our results suggest that the radio-loud fraction at high z is similar to that at low z and that other authors may be underestimating the fraction at high z. Finally, we determine the slope of the optical LF and obtain results consistent with previous studies of radio-loud QSOs and of the whole population of QSOs. The evolution of the LF with redshift was for many years interpreted as a flattening of the bright-end slope, but has recently been re-interpreted as strong evolution of the break luminosity for high-z QSOs, and our results, for the radio-loud population, are consistent with this., This work has been funded by the Spanish Ministerio de Ciencia e Innovacion (MICINN) under project AYA2011-29517-C03-02.

Published
2015

11. Deep Learning Identifies High-z Galaxies in a Central Blue Nugget Phase in a Characteristic Mass Range.

Author

M. Huertas-Company, J. R. Primack, A. Dekel, D. C. Koo, S. Lapiner, D. Ceverino, R. C. Simons, G. F. Snyder, M. Bernardi, Z. Chen, H. Domínguez-Sánchez, C. T. Lee, B. Margalef-Bentabol, and D. Tuccillo

Subjects
NEURAL circuitry, HUBBLE constant, STELLAR mass, DENSITY of stars
Abstract

We use machine learning to identify in color images of high-redshift galaxies an astrophysical phenomenon predicted by cosmological simulations. This phenomenon, called the blue nugget (BN) phase, is the compact star-forming phase in the central regions of many growing galaxies that follows an earlier phase of gas compaction and is followed by a central quenching phase. We train a convolutional neural network (CNN) with mock “observed” images of simulated galaxies at three phases of evolution— pre-BN, BN, and post-BN—and demonstrate that the CNN successfully retrieves the three phases in other simulated galaxies. We show that BNs are identified by the CNN within a time window of ∼0.15 Hubble times. When the trained CNN is applied to observed galaxies from the CANDELS survey at z = 1–3, it successfully identifies galaxies at the three phases. We find that the observed BNs are preferentially found in galaxies at a characteristic stellar mass range, 109.2–10.3M⊙ at all redshifts. This is consistent with the characteristic galaxy mass for BNs as detected in the simulations and is meaningful because it is revealed in the observations when the direct information concerning the total galaxy luminosity has been eliminated from the training set. This technique can be applied to the classification of other astrophysical phenomena for improved comparison of theory and observations in the era of large imaging surveys and cosmological simulations. [ABSTRACT FROM AUTHOR]

Published
2018
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12. Impairment of Assets

Author

CAPALBO, Francesco, F. Agliata , A. Allini, M. Bisogno, A. Caldarelli, F. Capalbo, F. Di Darlo, C. Fiondella, W. Forte, A. Incollingo, M. Lucchese, R. Macchioni, M. Maffei, R. Maglio, F. Manes Rossi, G. Sannino, P. Trataglia Polcini, D. Tuccillo, Lucio Potito, and Capalbo, Francesco

Subjects
IAS IFRS, Bilancio, Impairment, IAS IFRS, Bilancio, Impairment
Published
2016

13. The Accounting and Managerial Implications of IAS/IFRS in the Financial Statements of Italian Insurance Companies

Author

MAGLIO R, AGLIATA F, TUCCILLO D, Maglio, Roberto, D., Tuccillo, F., Agliata, AA.VV., Maglio, R, Agliata, F, and Tuccillo, D

Subjects
insurance companie, Ifr, performance
Abstract

This survey initially aims to briefly consider from a business point of view the typical aspects of insurance and the setting up of national/italian accounting practice on the insurance companies’ economic components and assets. Secondly, it draws attention to the problems and the development of the IASB Insurance Project (in light of the contents of the comment letters received by the IASB as a result of the adoption of ED / 2010 / 8 on insurance contracts) and finally, (the last paragraph in relation to the previous discussion) it makes an analysis of how and whether those assumptions on the IASB models already mentioned can be considered applied; it also attempts a comparison, only where possible, including the probable results deriving from the adoption of a Fair value accounting system (as in Phase I) or the fulfilment Present value of cash flows (chosen in Phase II).

Published
2012

14. Validation of an Eco-Friendly Automated Method for the Determination of Glucose and Fructose in Wines.

Author

Dini I, Tuccillo D, Coppola D, De Biasi MG, Morelli E, and Mancusi A

Subjects
Humans, Glucose analysis, Fructose analysis, Carbohydrates analysis, Sugars analysis, Wine analysis
Abstract

Fermentable sugar dosage helps oenologists to establish a harvest's moment and control the fermentation process of the musts. The official analyses recommended for their determination are long, laborious, and must be carried out by specialized personnel. On the contrary, instrumental analysis automation limits human errors, increases precision, and reduces the time and cost of the analyses. In the food production sector, to use methods other than those recommended by supranational bodies in official reports, it is necessary to validate the analytical processes to establish the conformity of the results between the new methods and the reference ones. This work validated an automated enzymatic apparatus to determine the sum of glucose and fructose levels in wine samples. The validation was carried out on wine samples (dry red wine, dry white wine, moderately sweet wine, and sweet wine) containing different sugar concentrations by comparing data obtained using the OIV-MA-AS311-02 method performed by a specialized operator (reference method) and the same method performed by an automated apparatus. The difference between the results' means obtained with the two procedures was significant. Nevertheless, the automated procedure was considered suitable for the intended use since the differences between the averages were lower than the measurement uncertainty at the same concentration, and the repeatability results were better for the automated procedure than the reference method.

Published
2023
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15. Dose titration of intramuscular interferon beta-1a reduces the severity and incidence of flu-like symptoms during treatment initiation.

Author

Matson MA, Zimmerman TR Jr, Tuccillo D, Tang Y, and Deykin A

Subjects
Adolescent, Adult, Antineoplastic Agents administration & dosage, Double-Blind Method, Drug-Related Side Effects and Adverse Reactions, Female, Humans, Incidence, Interferon beta-1a, Interferon-beta administration & dosage, Male, Middle Aged, Multiple Sclerosis drug therapy, Severity of Illness Index, Time Factors, Antineoplastic Agents adverse effects, Influenza, Human chemically induced, Influenza, Human prevention & control, Interferon-beta adverse effects
Abstract

Background/objective: Flu-like symptoms (FLS) are common side effects of interferon beta (IFNβ) therapy and can negatively affect the willingness of patients with multiple sclerosis to initiate therapy. Although dose titration is commonly used to reduce the severity and incidence of IFNβ-related FLS during treatment initiation, these benefits have not been confirmed in a well controlled study. The objective of this randomized, dose-blinded, parallel-group study was to assess the effect of dose titration on the severity and incidence of FLS during the initial 8 weeks of once-weekly intramuscular (IM) IFNβ-1a administration., Methods: Healthy volunteers were randomized 1:1:1 to one of three IM IFNβ-1a regimens: 3-week titration (weekly quarter-dose increments over 3 weeks to full dose [30 µg]); 6-week titration (biweekly quarter-dose increments over 6 weeks to full dose); or no titration (full dose over 8 weeks). At weekly clinic visits, the severity of each FLS was rated 1 hour pre-injection and 4-6 hours and 12-15 hours post-injection. Study endpoints included post-injection change in FLS severity and post-injection FLS incidence (percentage of subjects with a ≥2-point increase in total FLS severity score) at each time point., Clinical Trial Registration: Clinicaltrials.gov identifier: NCT01119677., Results: Of 234 subjects enrolled, 194 (83%) completed the study. At 8 weeks, FLS severity was significantly reduced at both post-injection time points with 3-week titration (76% reduction at 4-6 hours, p < 0.001; 37% reduction at 12-15 hours; p < 0.001) and 6-week titration (50% reduction at 4-6 hours, p < 0.001; 32% reduction at 12-15 hours; p = 0.002) compared with no titration. The incidence of FLS was also significantly reduced at both time points with both titration regimens. Safety profiles for both titration regimens were consistent with the current IM IFNβ-1a label. Study limitations included that there is currently no validated assessment tool for evaluating the severity of FLS, that the study enrolled healthy volunteers, that different proportions of females were randomized to the 3-week-titration group than to the 6-week and no-titration groups, and that evaluation of the potential impact of titration on symptoms occurring substantially later after injection was not part of the study protocol., Conclusion: Dose titration during initiation of IM IFNβ-1a reduces FLS severity and incidence in healthy volunteers compared with no titration.

Published
2011
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16. An open-label, multicenter study to evaluate the safe and effective use of the single-use autoinjector with an Avonex® prefilled syringe in multiple sclerosis subjects.

Author

Phillips JT, Fox E, Grainger W, Tuccillo D, Liu S, and Deykin A

Subjects
Adjuvants, Immunologic administration & dosage, Adjuvants, Immunologic adverse effects, Adult, Aged, Biomarkers, Pharmacological blood, Female, Humans, Interferon beta-1a, Interferon-beta administration & dosage, Interferon-beta adverse effects, Male, Middle Aged, Multiple Sclerosis blood, Neopterin blood, Patient Acceptance of Health Care psychology, Patient Acceptance of Health Care statistics & numerical data, Patient Preference statistics & numerical data, Self Administration methods, Self Administration statistics & numerical data, Syringes, Adjuvants, Immunologic therapeutic use, Injections, Intramuscular instrumentation, Injections, Intramuscular psychology, Interferon-beta therapeutic use, Multiple Sclerosis drug therapy, Multiple Sclerosis psychology, Self Administration psychology
Abstract

Background: The ability to self-inject in patients with multiple sclerosis (MS) has been associated with a reduced risk of missed injections and drug discontinuation, and a beneficial effect on patients' independence. However, injection anxiety, needle phobia and disease-related disability are major barriers to a patient's ability to self-administer treatment. Use of an autoinjector may improve patients' ability to self-inject. This study evaluated the safe and effective use of Avonex Pen™ (prefilled pen), a single use autoinjector, for intramuscular delivery of interferon beta-1a (IM IFNβ-1a, Avonex) in MS patients., Methods: This was a Phase IIIb, open-label, single-country, multicenter trial in MS patients currently using IM IFNβ-1a prefilled syringes. Patients received weekly 30 mcg IM IFNβ-1a treatment over 4 weeks. On Day 1, patients self-administered IM IFNβ-1a using a prefilled syringe at the clinic. On Day 8, patients received training on the prefilled pen and self-administered IM IFNβ-1a using the device. On Day 15, patients self-administered IM IFNβ-1a at home using the prefilled pen. A final injection occurred at the clinic on Day 22 when patients self-administered IM IFNβ-1a using the prefilled pen while clinic staff observed and completed a detailed questionnaire documenting patients' ability to self-inject with the device. Serum neopterin levels were evaluated pre and post-injection on Days 1 and 8. Adverse events were monitored throughout., Results: Seventy-one (96%) patients completed the study. The overall success rate in safely and effectively using the prefilled pen was 89%. No device malfunctions occurred. One unsuccessful administration occurred at Day 22 due to patient error; no patient injury resulted. Patients gave the prefilled pen high ratings (8.7-9.3) on a 10-point scale for ease of use (0 = extremely difficult, 10 = extremely easy). Ninety-four percent of patients preferred the prefilled pen over the prefilled syringe. Induction of serum neopterin levels, serving as a biomarker for type 1 interferon action, was similar to that of the prefilled syringe. The prefilled pen demonstrated a safety profile comparable to the prefilled syringe., Conclusions: The prefilled pen is a safe and effective device for administration of IM IFNβ-1a and represents an alternative method for self-injection for MS patients using this therapy., Trial Registration: This study is registered at clinicaltrials.gov, identifier: NCT00828204.

Published
2011
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