• Corpus ID: 207870549

Probabilistic Super-Resolution of Solar Magnetograms: Generating Many Explanations and Measuring Uncertainties

@article{Gitiaux2019ProbabilisticSO,
  title={Probabilistic Super-Resolution of Solar Magnetograms: Generating Many Explanations and Measuring Uncertainties},
  author={Xavier Gitiaux and Shane A. Maloney and Anna Jungbluth and Carl Shneider and Paul J. Wright and Atilim Gunecs Baydin and Michel Deudon and Yarin Gal and Freddie Kalaitzis and Andr{\'e}s Mu{\~n}oz-Jaramillo},
  journal={ArXiv},
  year={2019},
  volume={abs/1911.01486}
}
Machine learning techniques have been successfully applied to super-resolution tasks on natural images where visually pleasing results are sufficient. However in many scientific domains this is not adequate and estimations of errors and uncertainties are crucial. To address this issue we propose a Bayesian framework that decomposes uncertainties into epistemic and aleatoric uncertainties. We test the validity of our approach by super-resolving images of the Sun's magnetic field and by… 

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References

SHOWING 1-10 OF 12 REFERENCES

Enhancing SDO/HMI images using deep learning

The aim is to develop a new method to enhance HMI data, simultaneously deconvolving and super-resolving images and magnetograms, based on two deep fully convolutional neural networks trained on synthetic data obtained from simulations of the emergence of solar active regions.

Magnetohydrodynamic modeling of the solar corona during Whole Sun Month

The Whole Sun Month campaign (August 10 to September 8, 1996) brought together a wide range of space-based and ground-based observations of the Sun and the interplanetary medium during solar minimum.

The Helioseismic and Magnetic Imager (HMI) Investigation for the Solar Dynamics Observatory (SDO)

The Helioseismic and Magnetic Imager (HMI) instrument and investigation as a part of the NASA Solar Dynamics Observatory (SDO) is designed to study convection-zone dynamics and the solar dynamo, the

Deep Learning for Single Image Super-Resolution: A Brief Review

This survey reviews representative deep learning-based SISR methods and group them into two categories according to their contributions to two essential aspects of S ISR: The exploration of efficient neural network architectures for SISS and the development of effective optimization objectives for deep SISr learning.

Space Weather Modeling Framework: A new tool for the space science community

[1] The Space Weather Modeling Framework (SWMF) provides a high-performance flexible framework for physics-based space weather simulations, as well as for various space physics applications. The SWMF

SunPy—Python for solar physics

Though still in active development, SunPy already provides important functionality for solar data analysis, and future releases will build upon and integrate with current work in the Astropy project and the rest of the scientific python community, to bring greater functionality to SunPy users.

Automatic differentiation in PyTorch

An automatic differentiation module of PyTorch is described — a library designed to enable rapid research on machine learning models that focuses on differentiation of purely imperative programs, with a focus on extensibility and low overhead.

Data Structures for Statistical Computing in Python

P pandas is a new library which aims to facilitate working with data sets common to finance, statistics, and other related fields and to provide a set of fundamental building blocks for implementing statistical models.

The NumPy Array: A Structure for Efficient Numerical Computation

This effort shows, NumPy performance can be improved through three techniques: vectorizing calculations, avoiding copying data in memory, and minimizing operation counts.