Solar Physics

    Physics of the Sun

    The Sun constitutes a physics laboratory with complex interactions between its electrically conducting plasma and a strong magnetic field, in conditions that cannot be reproduced in our laboratories.

    A new paradigm has been emerging that involves a connected view of the solar atmosphere linked by the magnetic field from the solar interior to the outer corona, as well as detailed local helioseismology in sunspots, loops and other magnetic features. The solar physics group of the IAC holds a leadership position in the investigation of the Sun in the framework of this global paradigm as exemplified by its role in major projects like the European Solar Telescope, the Solar Orbiter mission of ESA or the NASA-JAXA-IAC Chromospheric Ly-alpha Spectropolarimeter 1 & 2, and the leadership of the European Network SOLARNET.

    The IAC's expertise in the development of polarimetric instrumentation (TIP & LPSP, Sunrise, GRIS, EST, and Solar Orbiter), in the development and application of diagnostic techniques for magnetized plasmas, and in 3D numerical radiation-MHD modelling has established the team as one of the most competitive and scientifically prepared in the world.

     

    The major objective is to understand how the magnetic fields emerge from the solar interior through the surface and rise to the upper atmosphere, leaving in the meantime its imprint of complex interaction and releasing part of its energy to the medium.

    Specific goals 2020-2023:
    • To produce realistic one-, two- and three-dimensional models of key magnetic, dynamic and radiative processes in the solar atmosphere and convection zone using massively parallel computer facilities, in order to understand the physics underlying the solar structures and processes through suitable theoretical models.
    • To carry out forward modelling from numerical simulations to bridge the gap between observation and theory, taking into account all the physical mechanisms that produce polarization in solar spectral lines.
    • To develop novel diagnostic methods and inversion codes. Together with Bayesian inference tools, we will make a significant step forward on the quality of the information extracted from observations.
    • To support space projects (e.g., CLASP, Solar Orbiter, Sunrise3) via new developments in observations and theory, including the modeling of the CLASP2 ultraviolet spectropolarimetric observations in order to study the magnetism of the upper solar chromosphere.
    • To expand our understanding of the physics of the Sun by building a bridge between the knowledge gathered from solar observations and modeling, and the diversity of stars.

    For previous specific goals visit: 2016-2019 IAC-SO website

    Coordinator
    Research Lines Scientific Representative of the Severo Ochoa Programme at the IAC
    Research Lines Scientific Representative of the Severo Ochoa Programme at the IAC
    Staff
    Member
    Member
    Member

    Photospheric magnetism:

    • Identified the presence of spiral wavefronts in sunspots, which start from the darkest area of the spot, called the umbra, and extend to the outermost regions of the penumbra. The observed waves have been interpreted as a manifestation of magneto-acoustic waves, which propagate from the solar interior to the upper atmospheric layers following the direction of the magnetic field lines (Felipe et al. 2019, A&A).
    • Development of novel methods to investigate the small-scale magnetic activity of the quiet Sun photosphere via the Hanle and Zeeman effects (del Pino Alemán et al. 2018; Shchukina & Trujillo Bueno 2019, A&A).
    • First detection of small-scale photospheric loops in the solar poles (Pastor Yabar et al. 2018, A&A).
    • Development of a new method based on the Hanle effect to detect dipolar global magnetic fields in the Sun and solar-type stars (Vieu et al. 2017, MNRAS).
    • Khomenko et al. (2017, A&A) carried out numerical simulations in which the Bierman battery effect can act as a seed for generating the observed magnetic fields in the solar photosphere.
    • Using artificial intelligence techniques, a neural network has been developed capable of automatically measuring the horizontal movement of plasma in the solar photosphere with unprecedented resolution. The neural network is capable of detecting very small vortices in the solar atmosphere, only a few hundred kilometers in diameter, and which can last less than a minute (Asensio Ramos et al. 2017, A&A).
    • The origin of solar spicules was discovered by combining simulations and images taken with NASA's IRIS spectrograph and the Swedish Solar Telescope at the Roque de los Muchachos Observatory (Martínez-Sykora et al. 2017, Science).
    • A study of the latest advances, both theoretical and observational, in the waves that propagate in the Sun's magnetic fields (published in the journal Living Reviews in Solar Physics).

    Physical processes in the chromosphere, transition region and corona:

    • Numerical simulations of the emergence of cool surges showing they are mediated by two wedge-like shocks (Nóbrega-Siverio et al. 2016, ApJ).
    • Arregui et al. (2019, A&A) applied Bayesian seismology to determine magnetic fields in coronal structures.
    • The polarization of the ultraviolet radiation from the Sun has been measured for the first time with the CLASP (Chromospheric Lyman-Alpha SpectroPolarimeter) instrument. CLASP is an international experiment, which emerged as a result of theoretical research carried out at the IAC, and whose importance lies in the fact that it opens up a new window for exploring the magnetic field and geometry of the plasma in the transition region between the chromosphere and the Sun's corona. Following theoretical predictions by the IAC solar physics group, the CLASP suborbital space experiment discovered scattering polarization in the hydrogen Lyman-alpha line (Kano, Trujillo Bueno, Winebarger et al. 2017, ApJ Letters), opening up a new diagnostic window for investigating the chromosphere-corona transition region (Trujillo Bueno et al. 2017; SSR).
    • A radiative transfer investigation on the magnetic sensitivity of the solar resonance line from Mg II k to 2795.5 Å has been carried out, which indicates that this line is especially suitable for probing the chromosphere in quiet and active regions of the Sun (Alsina Ballester et al. 2016, ApJ Letters).
    • Previous (Belluzzi & Trujillo Bueno 2012; ApJ Letters) and new (Alsina Ballester et al. 2016; ApJ Letters) theoretical investigations led to the development of CLASP2, which in 2019 observed the polarization across the Mg II resonance lines providing unprecedented information on the magnetic fields in the upper solar chromosphere.

    Magnetic field in chromospheric and transition region structures:

    • Development of a new general inversion code to deal with very strong lines observed with the SST and IRIS (de la Cruz Rodríguez et al. 2016, ApJL).
    • Martínez González et al. (2016, ApJ) showed that prominence legs harbor vertical helical fields with slow temporal variations.
    • Khomenko et al. (2016, ApJ) detected drifts between ionized and neutral species in solar prominences, pointing to the presence of multi-fluid effects in the solar chromosphere.
    • Grant et al. (2018, Nature Physics) provide the first observational evidence of Alfvén waves heating chromospheric plasma in a sunspot umbra.
    • 3D radiative transfer modeling of unprecedented Lyman-alpha spectropolarimetric observations obtained with CLASP provided constraints on the magnetic field and geometrical complexity of the corrugated layer that delineates the chromosphere-corona transition region (Trujillo Bueno et al. 2018, ApJ Letters).

    Inversion codes and deep learning:

    • Del Toro Iniesta & Ruiz Cobo (2016, LRSP) reviewed the field of inversion of the radiative transfer equation where IAC is at the forefront of research.
    • Asensio Ramos & Díaz Baso (2019, A&A) developed convolutional neural networks for a very fast inversion of Stokes profiles.
    • Díaz Baso & Asensio Ramos (2018, A&A) produced a method to enhance SDO/HMI images using deep learning.

    Scientific Outputs 2012 - 2015

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