scholarly journals A Machine Learning Approach to Correcting Atmospheric Seeing in Solar Flare Observations

2020 ◽  
Author(s):  
John A Armstrong ◽  
Lyndsay Fletcher
Keyword(s):  
Solar Flare ◽  
Transfer Learning ◽  
Solar Physics ◽  
Speckle Interferometry ◽  
Similar Data ◽  
Solar Observations ◽  
Phase Diversity ◽  
Machine Learning Approach ◽  
Processing Techniques ◽  
Atmospheric Seeing

Abstract Current post-processing techniques for the correction of atmospheric seeing in solar observations – such as Speckle interferometry and Phase Diversity methods – have limitations when it comes to their reconstructive capabilities of solar flare observations. This, combined with the sporadic nature of flares meaning observers cannot wait until seeing conditions are optimal before taking measurements, means that many ground-based solar flare observations are marred with bad seeing. To combat this, we propose a method for dedicated flare seeing correction based on training a deep neural network to learn to correct artificial seeing from flare observations taken during good seeing conditions. This model uses transfer learning, a novel technique in solar physics, to help learn these corrections. Transfer learning is when another network already trained on similar data is used to influence the learning of the new network. Once trained, the model has been applied to two flare datasets: one from AR12157 on 2014/09/06 and one from AR12673 on 2017/09/06. The results show good corrections to images with bad seeing with a relative error assigned to the estimate based on the performance of the model. Further discussion takes place of improvements to the robustness of the error on these estimates.

scholarly journals Synoptic solar observations of the Solar Flare Telescope focusing on space weather

2020 ◽  
Vol 10 ◽  
pp. 41 ◽  
Author(s):  
Yoichiro Hanaoka ◽  
Takashi Sakurai ◽  
Ken’ichi Otsuji ◽  
Isao Suzuki ◽  
Satoshi Morita
Keyword(s):  
Magnetic Field ◽  
Solar Flare ◽  
Space Weather ◽  
Solar Observation ◽  
The Sun ◽  
Solar Observations ◽  
Solar Filaments ◽  
Field Information ◽  
Activity Cycles ◽  
Synoptic Observations

The solar group at the National Astronomical Observatory of Japan is conducting synoptic solar observation with the Solar Flare Telescope. While it is a part of a long-term solar monitoring, contributing to the study of solar dynamo governing solar activity cycles, it is also an attempt at contributing to space weather research. The observations include imaging with filters for Hα, Ca K, G-band, and continuum, and spectropolarimetry at the wavelength bands including the He I 1083.0 nm/Si I 1082.7 nm and the Fe I 1564.8 nm lines. Data for the brightness, Doppler signal, and magnetic field information of the photosphere and the chromosphere are obtained. In addition to monitoring dynamic phenomena like flares and filament eruptions, we can track the evolution of the magnetic fields that drive them on the basis of these data. Furthermore, the magnetic field in solar filaments, which develops into a part of the interplanetary magnetic cloud after their eruption and occasionally hits the Earth, can be inferred in its pre-eruption configuration. Such observations beyond mere classical monitoring of the Sun will hereafter become crucially important from the viewpoint of the prediction of space weather phenomena. The current synoptic observations with the Solar Flare Telescope is considered to be a pioneering one for future synoptic observations of the Sun with advanced instruments.

scholarly journals New Observational Aspects

1990 ◽  
Vol 138 ◽  
pp. 469-487
Author(s):  
Oddbj⊘rn Engvold
Keyword(s):  
High Resolution ◽  
Canary Islands ◽  
Solar Physics ◽  
High Resolution Imaging ◽  
The Sun ◽  
Image Stabilization ◽  
Structure And Dynamics ◽  
Solar Observations ◽  
Methods And Techniques ◽  
Resolution Imaging

The requirements and conditions for high resolution imaging and polarimetry of the Sun are reviewed. Various methods and techniques are discussed for image stabilization and sharpening in solar observations. The new solar facilities in the Canary Islands in particular are frequently reaching diffraction limited resolution and yield new insight in the structure and dynamics of the solar atmosphere. Future ground based telescopes like THEMIS and LEST, as well as planned solar missions in space will trigger a next advance in solar physics.

scholarly journals Infrared Speckle Interferometry: A Sensitive Technique for Physical Measurements of Unseen Companions to Nearby Stars

1986 ◽  
Vol 109 ◽  
pp. 309-319
Author(s):  
D. W. McCarthy
Keyword(s):  
Main Sequence ◽  
Position Angle ◽  
Speckle Interferometry ◽  
Resolving Power ◽  
Detector Arrays ◽  
Physical Measurements ◽  
Nearby Stars ◽  
Absolute Magnitudes ◽  
Large Telescopes ◽  
Atmospheric Seeing

Infrared speckle interferometry combines the full resolving power of large telescopes with high photometric sensitivity over the wavelength range 2.2 to 12 microns. Despite improved atmospheric seeing at these wavelengths, seeing fluctuations limit measurement precision. Astrometric companions have been detected with angular separations ≥0.1 arcsec and magnitude differences ≤3.7 mag. Results illustrate seeing limitations and show how the usual position angle ambiguity can be overcome. These measurements yield masses and absolute magnitudes for calibrating the lower main sequence. In some cases, orbital motion is detected. A method of “shift-and-add” enables detection of substellar (0.04 to 0.08 M⊙) companions. Future improvements involving detector arrays and seeing monitors are discussed.

The Solaris Solar Polar Mission

2020 ◽  
Author(s):  
Donald M. Hassler ◽  
Jeff Newmark ◽  
Sarah Gibson ◽  
Louise Harra ◽  
Thierry Appourchaux ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Solar Physics ◽  
Polar View ◽  
Ecliptic Plane ◽  
Vantage Point ◽  
Mission Design ◽  
Future Research ◽  
The Sun ◽  
Gravity Assist ◽  
Solar Observations

<p>The solar poles are one of the last unexplored regions of the solar system. Although Ulysses flew over the poles in the 1990s, it did not have remote sensing instruments onboard to probe the Sun’s polar magnetic field or surface/sub-surface flows.</p><p>We will discuss Solaris, a proposed Solar Polar MIDEX mission to revolutionize our understanding of the Sun by addressing fundamental questions that can only be answered from a polar vantage point. Solaris uses a Jupiter gravity assist to escape the ecliptic plane and fly over both poles of the Sun to >75 deg. inclination, obtaining the first high-latitude, multi-month-long, continuous remote-sensing solar observations. Solaris will address key outstanding, breakthrough problems in solar physics and fill holes in our scientific understanding that will not be addressed by current missions.</p><p>With focused science and a simple, elegant mission design, Solaris will also provide enabling observations for space weather research (e.g. polar view of CMEs), and stimulate future research through new unanticipated discoveries.</p>

Automatic Solar Flare Tracking Using Image-Processing Techniques

Solar Physics ◽  
2004 ◽  
Vol 222 (1) ◽  
pp. 137-149 ◽  
Author(s):  
Ming Qu ◽  
Frank Shih ◽  
Ju Jing ◽  
Haimin Wang
Keyword(s):  
Image Processing ◽  
Solar Flare ◽  
Image Processing Techniques ◽  
Processing Techniques

scholarly journals Fine-grained Solar Flare Forecasting Based on the Hybrid Convolutional Neural Networks*

2021 ◽  
Vol 922 (2) ◽  
pp. 232
Author(s):  
Zheng Deng ◽  
Feng Wang ◽  
Hui Deng ◽  
Lei. Tan ◽  
Linhua Deng ◽  
...  
Keyword(s):  
Solar Cycle ◽  
Solar Flare ◽  
Solar Physics ◽  
Forecast Model ◽  
Research Field ◽  
Generative Adversarial Networks ◽  
Solar Dynamics Observatory ◽  
Solar Dynamics ◽  
The Stability ◽  
Class Flare

Abstract Improving the performance of solar flare forecasting is a hot topic in the solar physics research field. Deep learning has been considered a promising approach to perform solar flare forecasting in recent years. We first used the generative adversarial networks (GAN) technique augmenting sample data to balance samples with different flare classes. We then proposed a hybrid convolutional neural network (CNN) model (M) for forecasting flare eruption in a solar cycle. Based on this model, we further investigated the effects of the rising and declining phases for flare forecasting. Two CNN models, i.e., M rp and M dp, were presented to forecast solar flare eruptions in the rising phase and declining phase of solar cycle 24, respectively. A series of testing results proved the following. (1) Sample balance is critical for the stability of the CNN model. The augmented data generated by GAN effectively improved the stability of the forecast model. (2) For C-class, M-class, and X-class flare forecasting using Solar Dynamics Observatory line-of-sight magnetograms, the means of the true skill statistics (TSS) scores of M are 0.646, 0.653, and 0.762, which improved by 20.1%, 22.3%, and 38.0% compared with previous studies. (3) It is valuable to separately model the flare forecasts in the rising and declining phases of a solar cycle. Compared with model M, the means of the TSS scores for No-flare, C-class, M-class, and X-class flare forecasting of the M rp improved by 5.9%, 9.4%, 17.9%, and 13.1%, and those of the M dp improved by 1.5%, 2.6%, 11.5%, and 12.2%.

scholarly journals transferGWAS: GWAS of images using deep transfer learning

2021 ◽  
Author(s):  
Matthias Kirchler ◽  
Stefan Konigorski ◽  
Matthias Norden ◽  
Christian Meltendorf ◽  
Marius Kloft ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Medical Images ◽  
Genome Wide Association ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Similar Data ◽  
Fundus Images ◽  
Genome Wide ◽  
Retinal Fundus Images ◽  
Retinal Fundus

Medical images can provide rich information about diseases and their biology. However, investigating their association with genetic variation requires non-standard methods. We propose transferGWAS, a novel approach to perform genome-wide association studies directly on full medical images. First, we learn semantically meaningful representations of the images based on a transfer learning task, during which a deep neural network is trained on independent but similar data. Then, we perform genetic association tests with these representations. We validate the type I error rates and power of transferGWAS in simulation studies of synthetic images. Then we apply transferGWAS in a genome-wide association study of retinal fundus images from the UK Biobank. This first-of-a-kind GWAS of full imaging data yielded 60 genomic regions associated with retinal fundus images, of which 7 are novel candidate loci for eye-related traits and diseases.

scholarly journals A Hybrid Supervised/Unsupervised Machine Learning Approach to Solar Flare Prediction

2018 ◽  
Vol 853 (1) ◽  
pp. 90 ◽  
Author(s):  
Federico Benvenuto ◽  
Michele Piana ◽  
Cristina Campi ◽  
Anna Maria Massone
Keyword(s):  
Machine Learning ◽  
Solar Flare ◽  
Learning Approach ◽  
Unsupervised Machine Learning ◽  
Machine Learning Approach
Sign in / Sign up

Export Citation Format

Share Document