Analyzing multi-featured time series data is critical for space missions making efficient event detection, potentially onboard, essential for automatic analysis. However, limited onboard computational resources and data downlink constraints necessitate robust methods for identifying regions of interest in real time. This work presents an adaptive outlier detection algorithm based on the reconstruction error of Principal Component Analysis (PCA) for feature reduction, designed explicitly for space mission applications. The algorithm adapts dynamically to evolving data distributions by using Incremental PCA, enabling deployment without a predefined model for all possible conditions. A pre-scaling process normalizes each feature's magnitude while preserving relative variance within feature types. We demonstrate the algorithm's effectiveness in detecting space plasma events, such as distinct space environments, dayside and nightside transients phenomena, and transition layers through NASA's MMS mission observations. Additionally, we apply the method to NASA's THEMIS data, successfully identifying a dayside transient using onboard-available measurements.

Geomagnetic storms are large-scale disturbances of the Earth's magnetosphere driven by solar wind interactions, posing significant risks to space-based and ground-based infrastructure. The Disturbance Storm Time (Dst) index quantifies geomagnetic storm intensity by measuring global magnetic field variations. This study applies symbolic regression to derive data-driven equations describing the temporal evolution of the Dst index. We use historical data from the NASA OMNIweb database, including solar wind density, bulk velocity, convective electric field, dynamic pressure, and magnetic pressure. The PySR framework, an evolutionary algorithm-based symbolic regression library, is used to identify mathematical expressions linking dDst/dt to key solar wind. The resulting models include a hierarchy of complexity levels and enable a comparison with well-established empirical models such as the Burton-McPherron-Russell and O'Brien-McPherron models. The best-performing symbolic regression models demonstrate superior accuracy in most cases, particularly during moderate geomagnetic storms, while maintaining physical interpretability. Performance evaluation on historical storm events includes the 2003 Halloween Storm, the 2015 St. Patrick's Day Storm, and a 2017 moderate storm. The results provide interpretable, closed-form expressions that capture nonlinear dependencies and thresholding effects in Dst evolution.

The accurate forecasting of geomagnetic activity is important. In this work, we present a novel multimodal Transformer based framework for predicting the 3 days and 5 days planetary Kp index by integrating heterogeneous data sources, including satellite measurements, solar images, and KP time series. A key innovation is the incorporation of the Wasserstein distance into the transformer and the loss function to align the probability distributions across modalities. Comparative experiments with the NOAA model demonstrate performance, accurately capturing both the quiet and storm phases of geomagnetic activity. This study underscores the potential of integrating machine learning techniques with traditional models for improved real time forecasting.

Artificial Intelligence (AI) has the potential to revolutionize space exploration by delegating several spacecraft decisions to an onboard AI instead of relying on ground control and predefined procedures. It is likely that there will be an AI/ML Processing Unit onboard the spacecraft running an inference engine. The neural-network will have pre-installed parameters that can be updated onboard by uploading, by telecommands, parameters obtained by training on the ground. However, satellite uplinks have limited bandwidth and transmissions can be costly. Furthermore, a mission operating with a suboptimal neural network will miss out on valuable scientific data. Smaller networks can thereby decrease the uplink cost, while increasing the value of the scientific data that is downloaded. In this work, we evaluate and discuss the use of reduced-precision and bare-minimum neural networks to reduce the time for upload. As an example of an AI use case, we focus on the NASA's Magnetosperic MultiScale (MMS) mission. We show how an AI onboard could be used in the Earth's magnetosphere to classify data to selectively downlink higher value data or to recognize a region-of-interest to trigger a burst-mode, collecting data at a high-rate. Using a simple filtering scheme and algorithm, we show how the start and end of a region-of-interest can be detected in on a stream of classifications. To provide the classifications, we use an established Convolutional Neural Network (CNN) trained to an accuracy >94%. We also show how the network can be reduced to a single linear layer and trained to the same accuracy as the established CNN. Thereby, reducing the overall size of the model by up to 98.9%. We further show how each network can be reduced by up to 75% of its original size, by using lower-precision formats to represent the network parameters, with a change in accuracy of less than 0.6 percentage points.

Geomagnetic storms (GS) occur when solar winds disrupt Earth's magnetosphere. GS can cause severe damages to satellites, power grids, and communication infrastructures. Estimate of direct economic impacts of a large scale GS exceeds $40 billion a day in the US. Early prediction is critical in preventing and minimizing the hazards. However, current methods either predict several hours ahead but fail to identify all types of GS, or make predictions within short time, e.g., one hour ahead of the occurrence. This work aims to predict all types of geomagnetic storms reliably and as early as possible using big data and machine learning algorithms. By fusing big data collected from multiple ground stations in the world on different aspects of solar measurements and using Random Forests regression with feature selection and downsampling on minor geomagnetic storm instances (which carry majority of the data), we are able to achieve an accuracy of 82.55% on data collected in 2021 when making early predictions three hours in advance. Given that important predictive features such as historic Kp indices are measured every 3 hours and their importance decay quickly with the amount of time in advance, an early prediction of 3 hours ahead of time is believed to be close to the practical limit.

If there is extraterrestrial life in our solar system, experts have long thought it could be hiding beneath Mars' surface, in Venus' clouds or in the icy oceans of Jupiter and Saturn's moons. NASA scientists say Uranus' moons Titania and Oberon may also have oceans warm enough to support life, suggesting that we should look there too in our hunt for aliens close to home. They made their discovery after re-analysing data from Voyager 2's close flybys of Uranus in the 1980s, as well as using computer modelling to look for signs of water on five of the planet's largest icy moons. It is the first piece of research to establish how the interior makeup and structure has evolved on Ariel, Umbriel, Titania, Oberon, and Miranda. Distant ice worlds: NASA scientists say Uranus' moons Titania and Oberon may have oceans warm enough to support life.

In the Earth's magnetosphere, there are fewer than a dozen dedicated probes beyond low-Earth orbit making in-situ observations at any given time. As a result, we poorly understand its global structure and evolution, the mechanisms of its main activity processes, magnetic storms, and substorms. New Artificial Intelligence (AI) methods, including machine learning, data mining, and data assimilation, as well as new AI-enabled missions will need to be developed to meet this Sparse Data challenge.

May 7, 2018 --A streak of rocket fire pierced the foggy predawn skies of southern California Saturday, as NASA sent off its latest Mars mission. The InSight mission is set to rack up a series of "firsts." It will also be the first time CubeSats will deploy in deep space. And, if the mission is successful, it will be the first time that scientists gather direct data on the interior of another planet and detect quakes on another planet. Despite all these firsts, the mission marks the 45th time humans have sent robotic envoys to uncover Mars's secrets (although only about half of those missions are considered a success).

There have been 26 significant'space weather' events affecting Earth over the last 50 years. These solar events can severely disrupt the Earth's magnetosphere (the boundary between the Earth's magnetic field and the solar wind), and pose a direct threat to electrical infrastructure - knocking out technologies that we rely on every single day, like GPS signals, electrical grids, computers and satellites. To put it lightly, if a major event were to happen tomorrow, it's likely to cost at least $2 trillion in damages in the first year alone. So, we're all doomed - right? Luckily for us, NASA has founded a Solar Space Team, which sits within the Frontier Development Lab (FDL).