Title of the publication
Journal: Astronomy & Astrophysics
1st Author: Helle Bakke
Position: Doctoral research fellow
Co-authors from RoCS:
- Mats Carlsson
- Luc Rouppe van der Voort
- Boris Gudiksen
- Bart De Pontieu
Short summary by the author:
Heating signatures from small-scale magnetic reconnection events in the solar atmosphere have proven to be difficult to detect through observations. Numerical models that reproduce flaring conditions are essential in the understanding of how nanoflares may act as a heating mechanism of the corona. We study the effects of non-thermal electrons in synthetic spectra from 1D hydrodynamic RADYN simulations of nanoflare heated loops to investigate the diagnostic potential of chromospheric emission from small-scale events. The Mg II h and k, Ca II H and K, Ca II 854.2 nm, H-alpha and H-beta chromospheric lines were synthesised from various RADYN models of coronal loops subject to electron beams of nanoflare energies. The contribution function to the line intensity was computed to better understand how the atmospheric response to the non-thermal electrons affects the formation of spectral lines and the detailed shape of their spectral profiles. The spectral line signatures arising from the electron beams highly depend on the density of the loop and the lower cutoff energy of the electrons. Low-energy (5 keV) electrons deposit their energy in the corona and transition region, producing strong plasma flows that cause both redshifts and blueshifts of the chromospheric spectra. Higher-energy (10 and 15 keV) electrons deposit their energy in the lower transition region and chromosphere, resulting in increased emission from local heating. Our results indicate that effects from small-scale events can be observed with ground-based telescopes, expanding the list of possible diagnostics for the presence and properties of nanoflares.
Title of the publication
SunnyNet: A neural network approach to 3D non-LTE radiative transfer
Journal: Astronomy & Astrophysics
1st Author: Bruce A. Chappell
Position: Masterstudent in 2021
Co-authors from RoCS:
- Tiago M.D.Pereira
Short summary by the author:
Computing spectra from 3D simulations of stellar atmospheres when allowing for departures from local thermodynamic equilibrium (non-LTE) is computationally very intensive. Aims. We develop a machine learning based method to speed up 3D non-LTE radiative transfer calculations in optically thick stellar atmospheres. Methods. Making use of a variety of 3D simulations of the solar atmosphere, we trained a convolutional neural network, SunnyNet, to learn the translation from LTE to non-LTE atomic populations. Non-LTE populations computed with an existing 3D code were considered as the true values. The network was then used to predict non-LTE populations for other 3D simulations, and synthetic spectra were computed from its predicted non-LTE populations. We used a six-level model atom of hydrogen and Hα spectra as test cases. Results. SunnyNet gives reasonable predictions for non-LTE populations with a dramatic speedup of about 105 times when running on a single GPU and compared to existing codes. When using different snapshots of the same simulation for training and testing, SunnyNet's predictions are within 20-40% of the true values for most points, which results in average differences of a few percent in Hα spectra. Predicted Hα intensity maps agree very well with existing codes. Most importantly, they show the telltale signs of 3D radiative transfer in the morphology of chromospheric fibrils. The results are not as reliable when the training and testing are done with different families of simulations.
Title of the publication
The Solar ALMA Science Archive (SALSA)
Journal: Solar and Stellar astrophysics
1st Author: Vasco Manuel de Jorge Henriques
Position: Guest researcher
Co-authors from RoCS:
- Shahin Jafarzadeh
- Juan Camilo Guevara Gómez
- Henrik Eklund
- Sven Wedemeyer
- Mikołaj Szydlarski
- Stein Vidar H. Haugan
- Atul Mohan
Short summary by the author:
In December 2016, the Atacama Large Millimeter/submillimeter Array (ALMA) carried out the first regular observations of the Sun. These early observations and the reduction of the respective data posed a challenge due to the novelty and complexity of observing the Sun with ALMA.
The difficulties with producing science-ready time-resolved imaging products in a format familiar and usable by solar physicists based on the measurement sets delivered by ALMA had so far limited the availability of such data. With the development of the Solar ALMA Pipeline (SoAP), it has now become possible to routinely reduce such data sets.
As a result, a growing number of science-ready solar ALMA datasets is now offered in the form of Solar ALMA Science Archive (SALSA).
So far, SALSA contains primarily time series of single-pointing interferometric images at cadences of one or two seconds. The data arrays are provided in FITS format.
We also present the first version of a standardised header format that accommodates future expansions and fits within the scope of other standards including the ALMA Science Archive itself and SOLARNET. The headers also include information designed to aid the reproduction of the imaging products from the raw data. Links to co-observations, if available, with a focus on those of the Interface Region Imaging Spectrograph (IRIS), are also provided. SALSA is accompanied by the Solar ALMA Library of Auxiliary Tools (SALAT) that contains IDL and Python routines for convenient loading and quick-look analysis of SALSA data.