Generating Photospheric Vector Magnetograms of Solar Active Regions for SOHO/MDI Using SDO/HMI and BBSO Data with Deep Learning

[1]  W. Hsu,et al.  Inferring Line-of-sight Velocities and Doppler Widths from Stokes Profiles of GST/NIRIS Using Stacked Deep Neural Networks , 2022, The Astrophysical Journal.

[2]  S. Hanasoge,et al.  Deep-learning Reconstruction of Sunspot Vector Magnetic Fields for Forecasting Solar Storms , 2022, The Astrophysical Journal.

[3]  B. Ommer,et al.  High-Resolution Image Synthesis with Latent Diffusion Models , 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Fan Zhang,et al.  ST-MFNet: A Spatio-Temporal Multi-Flow Network for Frame Interpolation , 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Zhen-yu Jin,et al.  A deep learning method to estimate magnetic fields in solar active regions from photospheric continuum images , 2021, Astronomy & Astrophysics.

[6]  Chang Liu,et al.  Tracing Hα Fibrils through Bayesian Deep Learning , 2021, The Astrophysical Journal Supplement Series.

[7]  Jiajia Liu,et al.  Reliability of AI-generated magnetograms from only EUV images , 2021, Nature Astronomy.

[8]  Chang Liu,et al.  DeepSun: machine-learning-as-a-service for solar flare prediction , 2020, Research in Astronomy and Astrophysics.

[9]  Marlon Núñez,et al.  Identifying Flux Rope Signatures Using a Deep Neural Network , 2020, Solar Physics.

[10]  Haodi Jiang,et al.  Identifying and Tracking Solar Magnetic Flux Elements with Deep Learning , 2020, The Astrophysical Journal Supplement Series.

[11]  Jing Liu,et al.  Scene Segmentation With Dual Relation-Aware Attention Network , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[12]  Hao Liu,et al.  Inferring Vector Magnetic Fields from Stokes Profiles of GST/NIRIS Using a Convolutional Neural Network , 2020, The Astrophysical Journal.

[13]  G. A. Gary,et al.  Forecasting Solar Cycle 25 Using Deep Neural Networks , 2020, Solar Physics.

[14]  Vladlen Koltun,et al.  Exploring Self-Attention for Image Recognition , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  C. Rudin,et al.  PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Hao Liu,et al.  Predicting Coronal Mass Ejections Using SDO/HMI Vector Magnetic Data Products and Recurrent Neural Networks , 2020, The Astrophysical Journal.

[17]  A. Hero,et al.  Predicting Solar Flares with Machine Learning: Investigating Solar Cycle Dependence , 2019, The Astrophysical Journal.

[18]  Hui Xiong,et al.  A Comprehensive Survey on Transfer Learning , 2019, Proceedings of the IEEE.

[19]  Chang Liu,et al.  Predicting Solar Flares Using a Long Short-term Memory Network , 2019, The Astrophysical Journal.

[20]  Sung-Ho Bae,et al.  Solar farside magnetograms from deep learning analysis of STEREO/EUVI data , 2019, Nature Astronomy.

[21]  Alfred O. Hero,et al.  Identifying Solar Flare Precursors Using Time Series of SDO/HMI Images and SHARP Parameters , 2019, Space Weather.

[22]  Thomas Brox,et al.  U-Net: deep learning for cell counting, detection, and morphometry , 2018, Nature Methods.

[23]  Jun Fu,et al.  Dual Attention Network for Scene Segmentation , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Xiaogang Wang,et al.  Context Encoding for Semantic Segmentation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[25]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[26]  Haimin Wang,et al.  Predicting Solar Flares Using SDO/HMI Vector Magnetic Data Products and the Random Forest Algorithm , 2017, 1706.02422.

[27]  Philipp Hennig,et al.  Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients , 2017, ICML.

[28]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[29]  M. Aschwanden,et al.  GLOBAL ENERGETICS OF SOLAR FLARES. II. THERMAL ENERGIES , 2015, 1502.05941.

[30]  Jian Sun,et al.  Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[31]  Monica G. Bobra,et al.  SOLAR FLARE PREDICTION USING SDO/HMI VECTOR MAGNETIC FIELD DATA WITH A MACHINE-LEARNING ALGORITHM , 2014, 1411.1405.

[32]  M. Aschwanden,et al.  GLOBAL ENERGETICS OF SOLAR FLARES. I. MAGNETIC ENERGIES , 2014, 1410.8013.

[33]  J. T. Hoeksema,et al.  The Helioseismic and Magnetic Imager (HMI) Vector Magnetic Field Pipeline: SHARPs – Space-Weather HMI Active Region Patches , 2014, 1404.1879.

[34]  M. Turmon,et al.  The Helioseismic and Magnetic Imager (HMI) Vector Magnetic Field Pipeline: Overview and Performance , 2014, Solar Physics.

[35]  Jesper Schou,et al.  The Helioseismic and Magnetic Imager (HMI) Vector Magnetic Field Pipeline: Overview and Performance , 2014, 1404.1881.

[36]  Yang Liu,et al.  EVOLUTION OF MAGNETIC FIELD AND ENERGY IN A MAJOR ERUPTIVE ACTIVE REGION BASED ON SDO/HMI OBSERVATION , 2011, 1201.3404.

[37]  T. Wiegelmann.  Optimization code with weighting function for the reconstruction of coronal magnetic fields , 2008, 0802.0124.

[38]  C. J. Schrijver,et al.  Can We Improve the Preprocessing of Photospheric Vector Magnetograms by the Inclusion of Chromospheric Observations? , 2008, 0801.2707.

[39]  C. J. Schrijver,et al.  Nonlinear Force-free Field Modeling of a Solar Active Region around the Time of a Major Flare and Coronal Mass Ejection , 2007, 0712.0023.

[40]  J. Owens,et al.  The Solar Optical Telescope for the Hinode Mission: An Overview , 2007, 0711.1715.

[41]  T. Kosugi,et al.  The Hinode (Solar-B) Mission: An Overview , 2007 .

[42]  S. Lewis,et al.  Regression analysis , 2007, Practical Neurology.

[43]  T. Sakurai,et al.  Preprocessing of Vector Magnetograph Data for a Nonlinear Force-Free Magnetic Field Reconstruction , 2006, astro-ph/0612641.

[44]  T. Metcalf,et al.  Magnetic Free Energy in NOAA Active Region 10486 on 2003 October 29 , 2005 .

[45]  Stephen L. Keil,et al.  Innovative Telescopes and Instrumentation for Solar Astrophysics , 2003 .

[46]  Mark S. Giampapa,et al.  SOLIS: an innovative suite of synoptic instruments , 2003, SPIE Astronomical Telescopes + Instrumentation.

[47]  Michael S. Wheatland,et al.  An Optimization Approach to Reconstructing Force-free Fields , 1997 .

[48]  R. Canfield,et al.  The imaging vector magnetograph at Haleakala , 1996 .

[49]  Alan M. Title,et al.  The solar oscillations investigation - Michelson Doppler Imager. , 1992 .

[50]  D. Mickey.  The Haleakala Stokes polarimeter , 1985 .

[51]  J. T. Hoeksema,et al.  The Helioseismic and Magnetic Imager (HMI) Investigation for the Solar Dynamics Observatory (SDO) , 2012 .

[52]  Haimin Wang,et al.  The new global high-resolution H alpha network: First observations and first results , 2000 .

[53]  H. Zirin,et al.  Synoptic Hα Full-Disk Observations of the Sun from Big Bear Solar Observatory – I. Instrumentation, Image Processing, Data Products, and First Results , 1999 .

[54]  A. Yamaguchi,et al.  Solar Flare Telescope and 10-cm New Coronagraph , 1991 .

[55]  K. Pearson.  VII. Note on regression and inheritance in the case of two parents , 1895, Proceedings of the Royal Society of London.

[56]  F. Galton.  Regression Towards Mediocrity in Hereditary Stature. , 1886 .