Publicaciones

Esta sección ofrece el acceso a la base de datos de Publicaciones que recopila los artículos del IAC publicados en revistas científicas. Por favor, pulsa la flecha del menú para ver todas las opciones de búsqueda y de ordenación de resultados; autor, revista, año, etc..

Además ofrece acceso al  repositorio de preprints del IAC: https://research.iac.es/preprints/l

  • BUSQUEDA DE EVIDENCIAS DE ACELERACION DE HADRONES EN REMANENTES DE SUPERNOVA
    Desde el espacio exterior llegan constantemente a la Tierra gran cantidad de particulas cargadas, la mayoria protones, aunque algunas veces tambien llegan nucleos atomicos que pueden ser tan masivos como el hierro. El espectro de estas particulas abarca un rango muy amplio de energia y sigue una ley de potencias. Este espectro plantea el problema
    Teresa Costado Dios

    Fecha de publicación:

    10
    2009
  • Atomic Data Assessment with PyNeb
    PyNeb is a Python package widely used to model emission lines in gaseous nebulae. We take advantage of its object-oriented architecture, class methods, and historical atomic database to structure a practical environment for atomic data assessment. Our aim is to reduce the uncertainties in parameter space (line-ratio diagnostics, electron density
    Morisset, Christophe et al.

    Fecha de publicación:

    10
    2020
    Número de citas
    21
  • Crater depth-to-diameter ratios on asteroid 162173 Ryugu d/D of craters on Ryugu
    The near-Earth asteroid 162173 Ryugu, the target of the Hayabusa2 mission, is noted to be a spinning top-shaped rubble-pile. Craters are among the most prominent surface features on Ryugu. Their shapes, particularly their depth-to-diameter ratio (d/D), can provide an important proxy for probing both the internal structure and surface processes of
    Noguchi, Rina et al.

    Fecha de publicación:

    1
    2021
    Número de citas
    7
  • Detecting outliers in astronomical images with deep generative networks
    With the advent of future big-data surveys, automated tools for unsupervised discovery are becoming ever more necessary. In this work, we explore the ability of deep generative networks for detecting outliers in astronomical imaging data sets. The main advantage of such generative models is that they are able to learn complex representations
    Margalef-Bentabol, Berta et al.

    Fecha de publicación:

    6
    2020
    Número de citas
    38
  • Magnetism Science with the Square Kilometre Array
    The Square Kilometre Array (SKA) will answer fundamental questions about the origin, evolution, properties, and influence of magnetic fields throughout the Universe. Magnetic fields can illuminate and influence phenomena as diverse as star formation, galactic dynamics, fast radio bursts, active galactic nuclei, large-scale structure, and Dark
    Bracco, Andrea et al.

    Fecha de publicación:

    7
    2020
    Número de citas
    58
  • Stellar masses of giant clumps in CANDELS and simulated galaxies using machine learning
    A significant fraction of high redshift star-forming disc galaxies are known to host giant clumps, whose nature and role in galaxy evolution are yet to be understood. In this work, we first present a new method based on neural networks to detect clumps in galaxy images. We use this method to detect clumps in the rest-frame optical and UV images of
    Huertas-Company, Marc et al.

    Fecha de publicación:

    9
    2020
    Número de citas
    33