This paper introduces a high resolution, machine learning-ready heliophysics dataset derived from NASA's Solar Dynamics Observatory (SDO), specifically designed to advance machine learning (ML) applications in solar physics and space weather forecasting. The dataset includes processed imagery from the Atmospheric Imaging Assembly (AIA) and Helioseismic and Magnetic Imager (HMI), spanning a solar cycle from May 2010 to July 2024. To ensure suitability for ML tasks, the data has been preprocessed, including correction of spacecraft roll angles, orbital adjustments, exposure normalization, and degradation compensation. We also provide auxiliary application benchmark datasets complementing the core SDO dataset. These provide benchmark applications for central heliophysics and space weather tasks such as active region segmentation, active region emergence forecasting, coronal field extrapolation, solar flare prediction, solar EUV spectra prediction, and solar wind speed estimation. By establishing a unified, standardized data collection, this dataset aims to facilitate benchmarking, enhance reproducibility, and accelerate the development of AI-driven models for critical space weather prediction tasks, bridging gaps between solar physics, machine learning, and operational forecasting.

In the era of space exploration, coronal holes on the sun play a significant role due to their impact on satellites and aircraft through their open magnetic fields and increased solar wind emissions. This study employs computer vision techniques to detect coronal hole regions and estimate their sizes using imagery from the Solar Dynamics Observatory (SDO). Additionally, we utilize deep learning methods, specifically Long Short-Term Memory (LSTM) networks, to analyze trends in the area of coronal holes and predict their areas across various solar regions over a span of seven days. By examining time series data, we aim to identify patterns in coronal hole behavior and understand their potential effects on space weather. This research enhances our ability to anticipate and prepare for space weather events that could affect Earth's technological systems.

In the era of space exploration, the implications of space weather have become increasingly evident. Central to this is the phenomenon of coronal holes, which can significantly influence the functioning of satellites and aircraft. These coronal holes, present on the sun, are distinguished by their open magnetic field lines and comparatively cooler temperatures, leading to the emission of solar winds at heightened rates. To anticipate the effects of these coronal holes on Earth, our study harnesses computer vision to pinpoint the coronal hole regions and estimate their dimensions using imagery from the Solar Dynamics Observatory (SDO). Further, we deploy deep learning methodologies, specifically the Long Short-Term Memory (LSTM) approach, to analyze the trends in the data related to the area of the coronal holes and predict their dimensions across various solar regions over a span of seven days. By evaluating the time series data concerning the area of the coronal holes, our research seeks to uncover patterns in the behavior of coronal holes and comprehend their potential influence on space weather occurrences. This investigation marks a pivotal stride towards bolstering our capacity to anticipate and brace for space weather events that could have ramifications for Earth and its technological apparatuses.

NASA's Solar Dynamics Observatory (SDO) mission collects large data volumes of the Sun's daily activity. Data compression is crucial for space missions to reduce data storage and video bandwidth requirements by eliminating redundancies in the data. In this paper, we present a novel neural Transformer-based video compression approach specifically designed for the SDO images. Our primary objective is to efficiently exploit the temporal and spatial redundancies inherent in solar images to obtain a high compression ratio. Our proposed architecture benefits from a novel Transformer block called Fused Local-aware Window (FLaWin), which incorporates window-based self-attention modules and an efficient fused local-aware feed-forward (FLaFF) network. This architectural design allows us to simultaneously capture short-range and long-range information while facilitating the extraction of rich and diverse contextual representations. Moreover, this design choice results in reduced computational complexity. Experimental results demonstrate the significant contribution of the FLaWin Transformer block to the compression performance, outperforming conventional hand-engineered video codecs such as H.264 and H.265 in terms of rate-distortion trade-off.

NASA shared a video taken by its Solar Dynamic Observatory showing dark patches on the sun, giving the illusion of a smile. NASA said Thursday that a new computer model that combines artificial intelligence and agency satellite data could help prepare for dangerous space weather. The model, called DAGGER (Deep Learning Geomagnetic Perturbation), uses the technical tool to analyze spacecraft measurements of the solar wind and forecast where an impending solar storm will strike on Earth – with 30 minutes of advance warning. An international team of researchers at the Frontier Development Lab said the model can produce predictions in less than a second, with predictions updating every minute. The lab is a partnership that includes NASA, the U.S. Geological Survey and the Department of Energy.

The sun may be the most powerful source of energy in the Milky Way, but NASA researchers are using artificial intelligence to get a better view of the giant ball of gas. The US space agency is using machine learning on solar telescopes, including its Solar Dynamics Observatory (SDO), launched in 2010, and its Atmospheric Imagery Assembly (AIA), imaging instrument that looks constantly at the sun. This allows the agency to snap incredible pictures of the celestial giant, while limiting the effects of solar particles and'intense sunlight,' which begins to degrade lenses and sensors over time. The sun goes through an 11-year cycle where it goes from very active to less active. It is tracked by sunspots and it is currently going through a quiet phase.

Neural networks have helped scientists monitor the Sun's extreme ultraviolet outbursts after an instrument on NASA's Solar Dynamic Observatory suffered an electrical malfunction, making it difficult for scientists to monitor a portion of extreme ultraviolet energy (EUV) being spewed by our star. EUV rays ejected from solar flares are particularly worrisome. The surge of highly energetic particles bombarding Earth can cause radio communication blackouts, knock satellites out of place, and disturb GPS signals. Space agencies around the world keep a close eye on the Sun's activity in an attempt to study and predict these outbursts. NASA's SDO is just one of the many spacecrafts currently orbiting our planet's star.

On Wednesday 10 April, the first image ever taken of a black hole was released. The picture, which shows a black hole surrounded by a hazy red and yellow circle, provides an unprecedented peek at one of the most mysterious entities in the universe. One of the scientists involved in the development of the picture is Dr Katie Bouman. We'll tell you what's true. You can form your own view.

The black hole that starred in the first ever photo to be taken of its kind has been given a name. The now famous swirling void will be known as Powehi, a Hawaiian word which has been bestowed by a language professor. And the name's meaning, chosen by University of Hawaii-Hilo Hawaiian Professor Larry Kimura, is as fittingly dramatic as the picture and work that produced it. We'll tell you what's true. You can form your own view.

Scientist Katherine Bouman has become one of the world's most popular people for helping create the first ever picture of a black hole. The researcher was one of a team made up of a huge number of experts who produced the image, which shows the blazing red and yellow of the event horizon that surrounds the first black hole ever to be seen. And one image in particular of Dr Bouman doing part of that work – using an algorithm she wrote to generate the image that made headlines around the world – has served as a reminder of the vast amount of expertise that has gone into creating such an achievement. We'll tell you what's true. You can form your own view.