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Found 10 papers, 10 papers with code
Machine learning in solar physics
The application of machine learning in solar physics has the potential to greatly enhance our understanding of the complex processes that take place in the atmosphere of the Sun.
Accelerating Multiframe Blind Deconvolution via Deep Learning
In this method, the image restoration problem is solved with a gradient descent method that is unrolled and accelerated aided by a few small neural networks.
High-precision interpolation of stellar atmospheres with a deep neural network using a 1D convolutional auto encoder for feature extraction
Given the widespread availability of grids of models for stellar atmospheres, it is necessary to recover intermediate atmospheric models by means of accurate techniques that go beyond simple linear interpolation and capture the intricacies of the data.
Accelerating non-LTE synthesis and inversions with graph networks
We find an optimal architecture for the graph network for predicting the departure coefficients of the levels of an atom from the physical conditions of a model atmosphere.
Approximate Bayesian Neural Doppler Imaging
We use amortized neural posterior estimation to produce a model that approximates the high-dimensional posterior distribution for spectroscopic observations of selected spectral ranges sampled at arbitrary rotation phases.
Planet cartography with neural learned regularization
More importantly, if exoplanets are partially cloudy like the Earth is, we show that one can potentially map the distribution of persistent clouds that always occur on the same position on the surface (associated to orography and sea surface temperatures) together with non-persistent clouds that move across the surface.
Learning to do multiframe wavefront sensing unsupervisedly: applications to blind deconvolution
The optimization of this loss function allows an end-to-end training of a machine learning model composed of three neural networks.
Stokes Inversion based on Convolutional Neural Networks
Our aim is to develop a new inversion code based on the application of convolutional neural networks that can quickly provide a three-dimensional cube of thermodynamical and magnetic properties from the interpretation of two-dimensional maps of Stokes profiles.
Enhancing SDO/HMI images using deep learning
We test Enhance against Hinode data that has been degraded to a 28 cm diameter telescope showing very good consistency.
DeepVel: deep learning for the estimation of horizontal velocities at the solar surface
These components are typically estimated using methods based on local correlation tracking.
