¿Cuál es la identidad del animal? : buscando pruebas en una novela

Se analizan las pruebas usadas por alumnos de 11-12 años en una actividad donde deben argumentar de qué animal creen que trata un fragmento de novela. Más concretamente se analiza a) a qué características del animal se refieren y la relación de las pruebas usadas con el texto y b) de qué manera reacciona el grupo cuándo las pruebas aportadas implican una referencia incorrecta basada en el texto o en las que no se identifica la referencia al texto. Los resultados muestran que la mayoría de estudiantes basan sus pruebas en el texto de forma correcta e ignoran o aceptan las pruebas aportadas por los compañeros, debido seguramente a la cultura que comparten. Concluimos que el carácter literario del texto es clave para provocar diferentes interpretaciones que llevan a los alumnos a discutir acerca de la identidad del animal y como consecuencia acerca del uso de pruebas

Gestor d'entorns virtuals per a l'execució de tasques d'altes prestacions

Amb l'evolució de la tecnologia les capacitats de còmput es van incrementant i problemes irresolubles del passat deixen de ser-ho amb els recursos actuals. La majoria d'aplicacions que s'enfronten a aquests problemes són complexes, ja que per aconseguir taxes elevades de rendiment es fa necessari utilitzar el major nombre de recursos possibles, i això les dota d'una arquitectura inherentment distribuïda. Seguint la tendència de la comunitat investigadora, en aquest treball de recerca es proposa una arquitectura per a entorns grids basada en la virtualització de recursos que possibilita la gestió eficient d'aquests recursos. L'experimentació duta a terme ha permès comprovar la viabilitat d'aquesta arquitectura i la millora en la gestió que la utilització de màquines virtuals proporciona.Con la evolución de la tecnología, las capacidades de cómputo se incrementan y problemas irresolubles del pasado dejan de serlo con los recursos actuales. La mayoría de las aplicaciones que se enfrentan a estos problemas son complejas, ya que para conseguir un elevado rendimiento es necesario utilizar el mayor número posible de recursos, lo que requiere de una arquitectura distribuida. Siguiendo la tendencia de la comunidad investigadora, en este trabajo de investigación se propone una arquitectura para entornos grid basada en la virtualización de recursos que posibilita la gestión eficiente de estos recursos. La experimentación llevada a cabo ha permitido comprobar la vialibilidad de esta arquitectura y la mejora en la gestión que supone el uso de máquinas virtuales.As the technology evolves the computational power increases. Past goals, which wre deemed too difficult to achieve, now become computationally solvable. Most applications that focus on that problems are complex; they need a lot of resources to attain good performance, and that imposes a distributed architecture. Following the research community trend, in this work we propose an architectural design for distributed environments based on resource virtualization, which enables efficient resource management. The experimentations held have been able to prove this architecture viability, along with, how could the use of virtual machines enhance resource management

CosmoHub : Interactive exploration and distribution of astronomical data on Hadoop

We present CosmoHub (https://cosmohub.pic.es), a web application based on Hadoop to perform interactive exploration and distribution of massive cosmological datasets. Recent Cosmology seeks to unveil the nature of both dark matter and dark energy mapping the large-scale structure of the Universe, through the analysis of massive amounts of astronomical data, progressively increasing during the last (and future) decades with the digitization and automation of the experimental techniques. CosmoHub, hosted and developed at the Port d'Informacio Científica (PIC), provides support to a worldwide community of scientists, without requiring the end user to know any Structured Query Language (SQL). It is serving data of several large international collaborations such as the Euclid space mission, the Dark Energy Survey (DES), the Physics of the Accelerating Universe Survey (PAUS) and the Marenostrum Institut de Ciencies de l'Espai (MICE) numerical simulations. While originally developed as a PostgreSQL relational database web frontend, this work describes the current version of CosmoHub, built on top of Apache Hive, which facilitates scalable reading, writing and managing huge datasets. As CosmoHub's datasets are seldomly modified, Hive it is a better fit. Over 60 TiB of catalogued information and 50 × 109 astronomical objects can be interactively explored using an integrated visualization tool which includes 1D histogram and 2D heatmap plots. In our current implementation, online exploration of datasets of 109 objects can be done in a timescale of tens of seconds. Users can also download customized subsets of data in standard formats generated in few minutes

The PAU Survey: A Forward Modeling Approach for Narrow-band Imaging

Weak gravitational lensing is a powerful probe of the dark sector, once measurement systematic errors can be controlled. In Refregier & Amara (2014), a calibration method based on forward modeling, called MCCL, was proposed. This relies on fast image simulations (e.g., UFig; Berge et al. 2013) that capture the key features of galaxy populations and measurement effects. The MCCL approach has been used in Herbel et al. (2017) to determine the redshift distribution of cosmological galaxy samples and, in the process, the authors derived a model for the galaxy population mainly based on broad-band photometry. Here, we test this model by forward modeling the 40 narrow-band photometry given by the novel PAU Survey (PAUS). For this purpose, we apply the same forced photometric pipeline on data and simulations using Source Extractor (Bertin & Arnouts 1996). The image simulation scheme performance is assessed at the image and at the catalogues level. We find good agreement for the distribution of pixel values, the magnitudes, in the magnitude-size relation and the interband correlations. A principal component analysis is then performed, in order to derive a global comparison of the narrow-band photometry between the data and the simulations. We use a `mixing' matrix to quantify the agreement between the observed and simulated sets of Principal Components (PCs). We find good agreement, especially for the first three most significant PCs. We also compare the coefficients of the PCs decomposition. While there are slight differences for some coefficients, we find that the distributions are in good agreement. Together, our results show that the galaxy population model derived from broad-band photometry is in good overall agreement with the PAUS data. This offers good prospect for incorporating spectral information to the galaxy model by adjusting it to the PAUS narrow-band data using forward modeling.Comment: Submitted to JCAP, 28 pages, 15 figures, 3 appendice

The PAU survey: Estimating galaxy photometry with deep learning

With the dramatic rise in high-quality galaxy data expected from Euclid and Vera C. Rubin Observatory, there will be increasing demand for fast high-precision methods for measuring galaxy fluxes. These will be essential for inferring the redshifts of the galaxies. In this paper, we introduce Lumos, a deep learning method to measure photometry from galaxy images. Lumos builds on BKGnet, an algorithm to predict the background and its associated error, and predicts the background-subtracted flux probability density function. We have developed Lumos for data from the Physics of the Accelerating Universe Survey (PAUS), an imaging survey using 40 narrow-band filter camera (PAUCam). PAUCam images are affected by scattered light, displaying a background noise pattern that can be predicted and corrected for. On average, Lumos increases the SNR of the observations by a factor of 2 compared to an aperture photometry algorithm. It also incorporates other advantages like robustness towards distorting artifacts, e.g. cosmic rays or scattered light, the ability of deblending and less sensitivity to uncertainties in the galaxy profile parameters used to infer the photometry. Indeed, the number of flagged photometry outlier observations is reduced from 10% to 2%, comparing to aperture photometry. Furthermore, with Lumos photometry, the photo-z scatter is reduced by ~10% with the Deepz machine learning photo-z code and the photo-z outlier rate by 20%. The photo-z improvement is lower than expected from the SNR increment, however currently the photometric calibration and outliers in the photometry seem to be its limiting factor.Comment: 20 pages, 22 figure

Modeling intrinsic galaxy alignment in the MICE simulation

The intrinsic alignment (IA) of galaxies is potentially a major limitation in deriving cosmological constraints from weak lensing surveys. In order to investigate this effect, we assign intrinsic shapes and orientations to galaxies in the light-cone output of the MICE simulation, spanning ∼5000deg2 and reaching redshift z=1.4. This assignment is based on a semianalytic IA model that uses photometric properties of galaxies as well as the spin and shape of their host halos. Advancing on previous work, we include more realistic distributions of galaxy shapes and a luminosity-dependent galaxy-halo alignment. The IA model parameters are calibrated against COSMOS and BOSS LOWZ observations. The null detection of IA in observations of blue galaxies is accounted for by setting random orientations for these objects. We compare the two-point alignment statistics measured in the simulation against predictions from the analytical IA models NLA and TATT over a wide range of scales, redshifts, and luminosities for red and blue galaxies separately. We find that both models fit the measurements well at scales above 8 h−1Mpc, while TATT outperforms NLA at smaller scales. The IA parameters derived from our fits are in broad agreement with various observational constraints from red galaxies. Lastly, we build a realistic source sample, mimicking DES Year 3 observations and use it to predict the IA contamination to the observed shear statistics. We find this prediction to be within the measurement uncertainty, which might be a consequence of the random alignment of blue galaxies in the simulation.K. H. acknowledges support by the Swiss National Science Foundation (Grant Nos. 173716, 198674), and from the Forschungskredit Grant of the University of Zurich (Project No. K-76106-01-01). J. B. and S. S. are partially supported by NSF Grant AST-2206563. This work was also partly supported by the program “Unidad de Excelencia María de Maeztu CEX2020-001058-M.” CosmoHub is hosted by the Port d’Informació Científica (PIC), maintained through a collaboration of Centro de Investigaciones Energeticas, Medioambientales y Tecnológicas (CIEMAT) and Institut de Física d’Altes Energies (IFAE), with additional support from Universitat Autónoma de Barcelona (UAB). Funding for the DES Projects has been provided by the U.S. Department of Energy, the U.S. National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Center for Supercomputing. Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, the Center for Cosmology and Astro-Particle Physics at the Ohio State University, the Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University, Financiadora de Estudos e Projetos, Fundação Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Científico e Tecnológico and the Ministerio da Ciência, Tecnologia e Inovação, the Deutsche Forschungsgemeinschaft and the Collaborating Institutions in the Dark Energy Survey. The Collaborating Institutions are Argonne National Laboratory, the University of California at Santa Cruz, the University of Cambridge, Centro de Investigaciones Energeticas, Medioambientales y Tecnológicas-Madrid, the University of Chicago, University College London, the DES-Brazil Consortium, the University of Edinburgh, the Eidgenössische Technische Hochschule (ETH) Zürich, Fermi National Accelerator Laboratory, the University of Illinois at Urbana-Champaign, the Institut de Ciencies de l’Espai (IEEC/CSIC), the Institut de Física d’Altes Energies, Lawrence Berkeley National Laboratory, the Ludwig-Maximilians Universität München and the associated Excellence Cluster Universe, the University of Michigan, NSF’s NOIRLab, the University of Nottingham, The Ohio State University, the University of Pennsylvania, the University of Portsmouth, SLAC National Accelerator Laboratory, Stanford University, the University of Sussex, Texas A&M University, and the OzDES Membership Consortium. Based in part on observations at Cerro Tololo Inter-American Observatory at NSF’s NOIRLab (NOIRLab Prop. ID 2012B-0001; PI: J. Frieman), which is managed by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation. The DES data management system is supported by the National Science Foundation under Grant Nos. AST-1138766 and AST-1536171. The DES participants from Spanish institutions are partially supported by MICINN under Grant Nos. ESP2017-89838, PGC2018-094773, PGC2018-102021, SEV-2016-0588, SEV-2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. IFAE is partially funded by the CERCA program of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Program (FP7/2007-2013) including ERC Grant Nos. 240672, 291329, and 306478. We acknowledge support from the Brazilian Instituto Nacional de Ciência e Tecnologia (INCT) do e-Universo (CNPq Grant 465376/2014-2). This manuscript has been authored by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the U.S. Department of Energy, Office of Science, Office of High Energy Physics.Peer reviewe

CosmoHub: Interactive exploration and distribution of astronomical data on Hadoop

We present CosmoHub (https://cosmohub.pic.es), a web application based on Hadoop to perform interactive exploration and distribution of massive cosmological datasets. Recent Cosmology seeks to unveil the nature of both dark matter and dark energy mapping the large-scale structure of the Universe, through the analysis of massive amounts of astronomical data, progressively increasing during the last (and future) decades with the digitization and automation of the experimental techniques. CosmoHub, hosted and developed at the Port d'Informaci\'o Cient\'ifica (PIC), provides support to a worldwide community of scientists, without requiring the end user to know any Structured Query Language (SQL). It is serving data of several large international collaborations such as the Euclid space mission, the Dark Energy Survey (DES), the Physics of the Accelerating Universe Survey (PAUS) and the Marenostrum Institut de Ci\`encies de l'Espai (MICE) numerical simulations. While originally developed as a PostgreSQL relational database web frontend, this work describes the current version of CosmoHub, built on top of Apache Hive, which facilitates scalable reading, writing and managing huge datasets. As CosmoHub's datasets are seldomly modified, Hive it is a better fit. Over 60 TiB of catalogued information and $50 \times 10^9$ astronomical objects can be interactively explored using an integrated visualization tool which includes 1D histogram and 2D heatmap plots. In our current implementation, online exploration of datasets of $10^9$ objects can be done in a timescale of tens of seconds. Users can also download customized subsets of data in standard formats generated in few minutes

The PAU Survey: Intrinsic alignments and clustering of narrow-band photometric galaxies

We present the first measurements of the projected clustering and intrinsic alignments (IA) of galaxies observed by the Physics of the Accelerating Universe Survey (PAUS). With photometry in 40 narrow optical passbands (4500 Å–8500 Å), the quality of photometric redshift estimation is σz ∼ 0.01(1 + z) for galaxies in the 19 deg2 Canada-France-Hawaii Telescope Legacy Survey W3 field, allowing us to measure the projected 3D clustering and IA for flux-limited, faint galaxies (i < 22.5) out to z ∼ 0.8. To measure two-point statistics, we developed, and tested with mock photometric redshift samples, ‘cloned’ random galaxy catalogues which can reproduce data selection functions in 3D and account for photometric redshift errors. In our fiducial colour-split analysis, we made robust null detections of IA for blue galaxies and tentative detections of radial alignments for red galaxies (∼1 − 3σ), over scales of 0.1 − 18 h−1 Mpc. The galaxy clustering correlation functions in the PAUS samples are comparable to their counterparts in a spectroscopic population from the Galaxy and Mass Assembly survey, modulo the impact of photometric redshift uncertainty which tends to flatten the blue galaxy correlation function, whilst steepening that of red galaxies. We investigate the sensitivity of our correlation function measurements to choices in the random catalogue creation and the galaxy pair-binning along the line of sight, in preparation for an optimised analysis over the full PAUS area