# Deep Computer Vision for Solar Physics Big Data: Opportunities and Challenges

arXiv: https://arxiv.org/abs/2409.04850 · HTML: https://mlsanity.com/paper/2409.04850/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 43 references that are in the map.
- First posted on arXiv 7 Sep 2024.

## Builds on (most cited first, 17 of 17)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [Galactica: A Large Language Model for Science](https://mlsanity.com/paper/2211.09085/) (2022)
- [A Machine Learning Dataset Prepared From the NASA Solar Dynamics Observatory Mission](https://mlsanity.com/paper/1903.04538/) (2019)
- [Towards Coupling Full-disk and Active Region-based Flare Prediction for Operational Space Weather Forecasting](https://mlsanity.com/paper/2209.07406/) (2022)
- [Explaining Full-disk Deep Learning Model for Solar Flare Prediction using Attribution Methods](https://mlsanity.com/paper/2307.15878/) (2023)
- [Enhancing SDO/HMI images using deep learning](https://mlsanity.com/paper/1706.02933/) (2017)
- [Machine learning in solar physics](https://mlsanity.com/paper/2306.15308/) (2023)
- [Stokes Inversion based on Convolutional Neural Networks](https://mlsanity.com/paper/1904.03714/) (2019)
- [Identifying and Tracking Solar Magnetic Flux Elements with Deep Learning](https://mlsanity.com/paper/2008.12080/) (2020)
- [Inferring Vector Magnetic Fields from Stokes Profiles of GST/NIRIS Using a Convolutional Neural Network](https://mlsanity.com/paper/2005.03945/) (2020)
- [Explainable Deep Learning-based Solar Flare Prediction with post hoc Attention for Operational Forecasting](https://mlsanity.com/paper/2308.02682/) (2023)
- [Fast and Accurate Emulation of the SDO/HMI Stokes Inversion with Uncertainty Quantification](https://mlsanity.com/paper/2103.17273/) (2021)
- [Tracing Halpha Fibrils through Bayesian Deep Learning](https://mlsanity.com/paper/2107.07886/) (2021)
- [Inferring Line-of-Sight Velocities and Doppler Widths from Stokes Profiles of GST/NIRIS Using Stacked Deep Neural Networks](https://mlsanity.com/paper/2210.04122/) (2022)
- [SynthIA: A Synthetic Inversion Approximation for the Stokes Vector Fusing SDO and Hinode into a Virtual Observatory](https://mlsanity.com/paper/2108.12421/) (2021)
- [Super-Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using SDO/HMI Data and an Attention-Aided Convolutional Neural Network](https://mlsanity.com/paper/2403.18302/) (2024)

## Built on by (most cited first, 2 of 2)

- [Physics-informed Attention-enhanced Fourier Neural Operator for Solar Magnetic Field Extrapolations](https://mlsanity.com/paper/2510.05351/) (2025)
- [Cross-Modal Solar Image Synthesis: Adapting the Surya Foundation Model from He I 10830 AA to EUV Translation and Coronal Hole Segmentation](https://mlsanity.com/paper/2610.04553/) (2026)

## Abstract (arXiv)

With recent missions such as advanced space-based observatories like the Solar Dynamics Observatory (SDO) and Parker Solar Probe, and ground-based telescopes like the Daniel K. Inouye Solar Telescope (DKIST), the volume, velocity, and variety of data have made solar physics enter a transformative era as solar physics big data (SPBD). With the recent advancement of deep computer vision, there are new opportunities in SPBD for tackling problems that were previously unsolvable. However, there are new challenges arising due to the inherent characteristics of SPBD and deep computer vision models. This vision paper presents an overview of the different types of SPBD, explores new opportunities in applying deep computer vision to SPBD, highlights the unique challenges, and outlines several potential future research directions.