Related papers: Stokes inversion techniques with neural networks: …
The presence of the magnetic field is critical to transport energy through the solar atmosphere. The new generation of telescopes will provide new insight into how the magnetic field arrives into the chromosphere and its role in the energy…
Atmospheric models used for weather and climate prediction are traditionally formulated in a deterministic manner. In other words, given a particular state of the resolved scale variables, the most likely forcing from the sub-grid scale…
In a previous work, we reported on the discovery of supersonic magnetic upflows on granular cells in data from the {\sc Sunrise}/IMaX instrument. In the present work we investigate the physical origin of these events employing data of the…
Helioseismology and solar modelling have enjoyed a golden era thanks to decades-long surveys from ground-based networks such as for example GONG, BiSON, IRIS and the SOHO and SDO space missions which have provided high-quality helioseismic…
Magnetic Doppler imaging is currently the most powerful method of interpreting high-resolution spectropolarimetric observations of stars. This technique has revealed the presence of unexpected small-scale magnetic fields on the surfaces of…
The representation of nonlinear sub-grid processes, especially clouds, has been a major source of uncertainty in climate models for decades. Cloud-resolving models better represent many of these processes and can now be run globally but…
Modern weather forecast models perform uncertainty quantification using ensemble prediction systems, which collect nonparametric statistics based on multiple perturbed simulations. To provide accurate estimation, dozens of such…
Stratospheric aerosols play an important role in the earth system and can affect the climate on timescales of months to years. However, estimating the characteristics of partially observed aerosol injections, such as those from volcanic…
The Sun's polar magnetic fields are directly related to solar cycle variability. The strength of the polar fields at the start (minimum) of a cycle determine the subsequent amplitude of that cycle. In addition, the polar field reversals at…
The polarization of the Mg II h & k resonance lines is the result of the joint action of scattering processes and the magnetic field induced Hanle, Zeeman, and magneto-optical effects, thus holding significant potential for the diagnostic…
We present a new approach for correcting instrumental polarization by modeling the non-depolarizing effects of a complex series of optical elements to determine physically realizable Mueller matrices. Provided that the Mueller matrix of the…
Inferences of the magnetic field in the solar atmosphere by means of spectropolarimetric inversions (i.e., Stokes inversion codes) yield magnetic fields that are non-solenoidal($\nabla\cdot{\bf B} \ne 0$). Because of this, results obtained…
A central area of research in nonlinear science is the study of instabilities that drive the emergence of extreme events. Unfortunately, experimental techniques for measuring such phenomena often provide only partial characterization. For…
The solar wind, accelerated within the solar corona, sculpts the heliosphere and continuously interacts with planetary atmospheres. On Earth, high-speed solar-wind streams may lead to severe disruption of satellite operations and power…
The Sun is the most studied of all stars. It is a reference for all other observed stars and a laboratory of fundamental physics helping us understand processes occuring in conditions irreproducible on Earth. However, our understanding of…
With the plentiful information available in the Gaia BP/RP spectra, there is significant scope for applying discriminative models to extract stellar atmospheric parameters and abundances. We describe an approach to leverage an `Uncertain…
Using neural networks as a prediction method, we attempt to demonstrate that forecasting of the Sun's sunspot time series can be extended to the spatial-temporal case. We employ this machine learning methodology to forecast not only in time…
We have developed an inversion procedure designed for high-resolution solar spectro-polarimeters, such as Hinode/SP or DKIST/ViSP. The procedure is based on artificial neural networks trained with profiles generated from random atmospheric…
Long-term high-cadence measurements of stellar spectral variability are fundamental to better understand stellar atmospheric properties and stellar magnetism. These, in turn, are fundamental for the detectability of exoplanets as well as…
Translational invariance induced by pooling operations is an inherent property of convolutional neural networks, which facilitates numerous computer vision tasks such as classification. Yet to leverage rotational invariant tasks,…