To understand the solar evolution and effects of solar eruptive events, the Sun is permanently observed by multiple satellite missions. The optically-thin emission of the solar plasma and the limited number of viewpoints make it challenging to reconstruct the geometry and structure of the solar atmosphere; however, this information is the missing link to understand the Sun as it is: a three-dimensional, evolving star. We present a method that enables a complete 3D representation of the uppermost solar layer observed in extreme ultraviolet (EUV) light. We use a deep learning approach for 3D scene representation that accounts for radiative transfer, to map the entire solar atmosphere from three simultaneous observations. We demonstrate that our approach provides unprecedented reconstructions of the solar poles, and directly enables height estimates of coronal structures, solar flux ropes, coronal hole profiles, and coronal mass ejections. We validate the approach using model-generated synthetic EUV images, finding that our method accurately captures the 3D geometry even from a limited number of viewpoints. We quantify uncertainties of our model using an ensemble approach that allows us to estimate the model performance in absence of a ground-truth. Our method enables a novel view of our closest star, and is a breakthrough technology for the efficient use of multi-instrument datasets, which paves the way for future cluster missions.

How to cite: Jarolim, R., Tremblay, B., Munoz-Jaramillo, A., Bintsi, K.-M., Jungbluth, A., Santos, M., Mason, J. P., Sundaresan, S., Downs, C., Caplan, R., and Vourlidas, A.: SuNeRF: AI enables 3D reconstruction of the solar EUV corona, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2756, https://doi.org/10.5194/egusphere-egu23-2756, 2023.

Solar eruptive events are complex phenomena, which most often include solar flares, filament eruptions, coronal mass ejections (CMEs), and CME-driven shock waves. CME-driven shocks in the corona and interplanetary space are considered to be the main producer of solar energetic particles (SEPs). A number of fundamental questions remain about how SEPs are produced. Current understanding points to CME-driven shocks and compressions in the solar corona.

A CME kinematics shows three phases - an initial rising phase (weakly accelerated motion), an impulsive phase and a residual propagation phase with constant or decreasing speed.

Despite significant amount of data available from ground-based (COSMO K-Cor, LOFAR) and remote instruments onboard of heliospheric space missions (SDO AIA, SOHO), processing of the data still requires noticeable effort. Most algorithms currently used in solar feature detection and tracking are known for their limited applicability and complexity of their processing chains, while usage of data-driven approaches for tracking of CME-related phenomena is currently limited due to insufficiency of training sets.

Recently (Stepanyuk et.al, J. Space Weather Space Clim. Vol 12, 20(2022)), we have demonstrated the method and the software(https://gitlab.com/iahelio/mosaiics/wavetrack) for smart characterization and tracking of solar eruptive features based on the a-trous wavelet decomposition technique, intensity rankings and a set of filtering techniques. In this work we use Wavetrack to generate training sets for data-driven feature extraction and characterization. We utilize U-Net, a fully convolutional network which training strategy relies on the strong use of data augmentation to use the available annotated samples more efficiently. U-NET can be trained end-to-end from a very limited set of images, while feature engineering allows to improve this approach even further by expanding available training sets.

Here we present pre-trained models and demonstrate data-driven characterization and tracking of solar eruptive features on a set of CME-events.

How to cite: Stepanyuk, O. and Kozarev, K.: Advanced Multi-Instrument and Multi-Wavelength Image Processing and Feature Tracking for Remote CME Characterization with Convolutional Neural Network, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10705, https://doi.org/10.5194/egusphere-egu23-10705, 2023.

Coronal mass ejections (CMEs) are one of the most violent solar eruptions, which can burst out large amounts of magnetized plasma with speeds up to thousands of kilometers per second. When it reaches the Earth, a CME can cause geomagnetic storm, affecting aviation safety, satellite operations, communications systems and power facilities. Therefore, fast and accurate prediction of CME arrival time is crucial for avoiding severe damaging effects and reducing economic losses. The initial morphology and kinematics of a CME in the corona can be observed by the coronagraphs equipped on the Solar and Heliospheric Observatory (SOHO), so that the coronagraphs should be useful to predict the CME arrival times. In this study, convolutional neural network (CNN) is used to obtain the features of SOHO/LASCO coronagraph pictures related to the CME transit time, and establish a model capable of predicting the CME arrival time. The influence of different hyperparameters of CNN on the prediction results is studied. Further, we add a physical information constraint of the initial velocities of CME to the basic CNN outputs, and found that smaller prediction errors can be obtained. 

How to cite: Yang, Y., Shen, F., Li, Y., and Lin, R.: Predicting the 1 AU Arrival Time of Coronal Mass Ejections Based on Convolutional Neural Network, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10654, https://doi.org/10.5194/egusphere-egu23-10654, 2023.

Machine learning models trained to reproduce space mission observations are precious resources to fill gaps of missing data in measurement time series or to perform data forecasting within a reasonable uncertainty degree. The latter option is of particular importance for future space missions that will not host instrumentation dedicated to interplanetary medium parameter monitoring. The future LISA mission for low-frequency gravitational wave detection, for instance, will benefit of particle detectors to measure the galactic cosmic-ray integral flux variations and magnetometers that will allow to monitor the passage of large scale magnetic structures through the three LISA spacecraft as part of a diagnostics subsystem. Unfortunately, no instruments dedicated to solar wind speed measurements will be present on board the spacecraft constellation. Moreover, LISA, scheduled to launch in 2035, will trail Earth on the ecliptic at 50 million km distance, far from the orbits of other space missions dedicated to the interplanetary medium monitoring.

Based on precious lessons learned with LISA Pathfinder, the ESA LISA precursor mission, about the correlation between galactic cosmic-ray flux short-term variations and solar wind speed increases, we built a machine learning ensemble model able to reconstruct the solar wind trend only on the basis of contemporaneous and preceding observations of galactic cosmic-ray flux variations. Details about the model creation and performance will be presented, together with a description of the underlying data set, weak predictors and training phase. Advantages and limitations will be discussed, showing that the model performance may be enhanced by providing interplanetary magnetic field intensity observations as additional input data, with the goal of providing the LISA mission with an effective solar wind speed predictive tool.

How to cite: Sabbatini, F. and Grimani, C.: Machine learning ensemble models for solar wind speed prediction, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7941, https://doi.org/10.5194/egusphere-egu23-7941, 2023.

Dynamic interactions between the solar wind and the magnetosphere give rise to dramatic auroral forms that have been instrumental in the ground-based study of magnetospheric dynamics. The general mechanism of aurora types and their large-scale patterns are well-known, but the morphology of small- to meso-scale auroral forms observed in all-sky imagers and their relation to magnetospheric dynamics  and the coupling of the magnetosphere to the upper atmosphere remain in question. Machine learning has the potential to provide answers to these questions, but most existing auroral image data lack the ground-truth labels required for supervised learning and conventional statistical analyses. To mitigate this issue, we propose a novel self-supervised semi-supervised algorithm to automatically label the THEMIS all-sky image database. Specifically, we adapt the self-supervised Simple framework for Contrastive Learning of Representations (SimCLR) algorithm to learn latent representations of THEMIS all-sky images. These representations are finetuned using a small set of manually labeled data from the Oslo Aurora THEMIS (OATH) dataset, after which semi-supervised classification is used to train a classifier, beginning by training on the manually labeled OATH dataset and gradually incorporating the classifier’s most confident predictions on unlabeled data into the training dataset as ground-truth. We demonstrate that (a) classifiers fit to the learned representations of the manually labeled images achieve state–of–the–art performance, improving the classification accuracy by almost 10% over the current benchmark on labeled data; and (b) our model’s learned representations naturally cluster into more clusters than manually assigned categories, suggesting that existing categorizations are coarse and may obscure important connections between auroral types and their drivers. Finally, we introduce AuroraClick, a citizen science project with the goal of manually annotating a large representative sample of THEMIS all-sky images for the validation of our current models and the training of future models.  

How to cite: Johnson, J., Ozturk, D., Connor, H., Hampton, D., Blandin, M., and Keesee, A.: Automatic Classification of THEMIS All-Sky Images via Self-Supervised Semi-Supervised Learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2897, https://doi.org/10.5194/egusphere-egu23-2897, 2023.

As is known, neural networks can universally approximate any complex functions. This ground truth naturally makes it a suitable candidate for solution representation of complex partial differential equation (PDE) governed. For planetary magnetic field modelling problem, spherical harmonic functions are most used as standard modelling method. Spherical harmonic method requires globally nearly uniformly distributed observations. Meanwhile this method has quite limited ability for conducting regional field modelling. Instead, neural networks have great potential to deal with global or regional modelling problems. In this work, we thoroughly investigate the representative ability of neural networks for magnetic field modelling problem at global and regional scale, and concentrate on a specific neural network, that is physics-informed neural networks (PINNs) for implementation. PINNs makes it easier to incorporate different kinds of informed physics within a uniform optimization framework. Through synthetic model tests and partial mathematical proof, we showcase the importance of employing natural boundary condition, Laplace equation constraint and Poisson equation constraint at suitable collocation points for a reasonable and accurate magnetic field representation and introduce the detailed scheme for implementation. Finally, we use newly released Juno mission measurements, and present a global PINNs model for Jupiter's magnetic field, and a regional PINNs model for Great Blue Spot (GBS) region. Comparison with spherical harmonic model has been conducted to evaluate the correctness and flexibility of PINNs models.

How to cite: Chen, L., Livermore, P., Wu, L., de Ridder, S., and Zhang, C.: Modelling Jupiter's global and regional magnetic fields using physics-informed neural networks, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8430, https://doi.org/10.5194/egusphere-egu23-8430, 2023.

The BepiColombo mission will arrive at Mercury in 2025. It consists of two spacecraft, which both have a magnetometer on board. One of the science objectives of these instruments is to study the structure of Mercury’s magnetosphere and its dynamical interaction with the solar wind. To study this statistically, a large dataset of observations of the magnetopause (the magnetosphere’s outer boundary) is needed. However, identifying such magnetopause crossings in magnetic field data requires visual inspection by humans with expert knowledge and as such is a very time consuming process. We therefore design an algorithm to automatically detect the Hermean magnetopause in magnetometer time series data, making use of a convolutional neural network.

Since no BepiColombo data (in orbit) is available yet, we train the network on MESSENGER magnetometer data. However, we formulate the problem and design the architecture of the network in such a way that the algorithm should be easily transferable to BepiColombo magnetometer data, avoiding the possible impact of any instrumental particularities or orbital biases.

The goal is to have a neural network which is directly applicable to BepiColombo magnetometer data, as soon as the observations start and without any further training, thereby eliminating the necessity of manually creating a new dataset of BepiColombo magnetopause crossings.

How to cite: Maes, L., Fraenz, M., and Heyner, D.: Detecting the magnetopause of Mercury by neural network — using MESSENGER data to train for BepiColombo., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15160, https://doi.org/10.5194/egusphere-egu23-15160, 2023.

Aiming to unravel the astrobiology of Mars, the Perseverance mission came with a lot of unknowns. With the surface level knowledge that we have already known, The High Resolution Imaging Science Experiment (HiRISE) can already determine the observation or experimental sites through the images generated from the orbiter. Although the resolution is high, with the power of a 1-meter-size object determinator, we can always expect so much more from the ground-level observation.

The Mars Perseverance rover is equipped with a pair of Mastcam-Z set cameras that are equipped in a manner to simulate the human eye for depth determination in image processing. The instruments can process stereo colour images of the ground level. These images can be used to make detailed maps of the Mars surface scenery at ground level with high precision.

Building and analyzing these images can take days to process on Earth manually. But if we utilise machine learning tools and onsite computation, it might save a lot of time for the mission. The current model used in the Mars Perseverance is the AutoNav Mark 4 with a lot of tasks, including spacecraft positioning, in-flight orbit determination, target tracking, and ephemeris calculations. All those might be computationally expensive to process. Therefore, the aim of this research is to develop a simple algorithm to do object and slope determinations to feed into an autonomous path determination process. The data fed into the algorithm are panoramic images captured by the MastCam-Z mounted on Mars Perseverance.

How to cite: Swida, O. B., Foing, B., and Vleugels, C.: Mars Perseverance Panoramic Image for Self-Determination Mission Algorithm, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16941, https://doi.org/10.5194/egusphere-egu23-16941, 2023.

We present the analysis of a 2.06 Ga apatite crystal obtained from an ultramafic phoscorite rock from the Phalaborwa Complex (Limpopo Province, South Africa) [1]. A space-prototype laser ablation ionisation mass spectrometer (LIMS) [2,3] was used to study the chemical composition of the sample. Mass spectra were recorded from a sample area of 0.6x0.6 mm², with a spatial resolution of 30 μm and sub-micrometre depth resolution.

Apatite is a calcium phosphate mineral expressed by the chemical stoichiometric formula [Ca₅(PO₄)₃(F, Cl, OH)]. The halogen site, occupied by F, Cl, and OH, corresponds to an isomorpous series with fluor-, chlor- and hydroxyl-apatite end members, respectively. Apatite, being an accessory mineral in igneous and other rocks, commonly contains a range of other elements that do not fit well into the major rock forming minerals, such as rare earth elements (REE). These are suitable targets for investigating physical and chemical conditions in igneous rocks and the volatile evolution of magmas.

The analysis of the spectra recorded with our LIMS system for the abundances of the elements of interest at each location were performed in two steps. First, the abundances of each element across the sampled area were compiled in element maps. And second, an unsupervised machine learning algorithm based on clustering and network analysis was applied to the data set of analysed mass spectra to separate it into groups of distinct chemical composition. Subsequently, a more detailed analysis was conducted on each of the recovered groups to assign the corresponding mineral. In addition to the group of spectra belonging to apatite, which was assigned to fluorapatite, other minerals were identified, amongst others olivine. This method yields an unsupervised approach to identify different mineralogical entities present within a sample. This network analysis method was previously applied to a 1.88 Ga Gunflint sample (Ontario, Canada) to separate spectra recorded from the host (chert) from spectra containing signatures of organic matter from fossilized microbes [4].

Given that the data were recorded using a miniature mass spectrometer designed for space flight, this analysis demonstrates the analytical capabilities of our LIMS system that could be achieved in-situ on other planetary bodies in our Solar System, for example on the Moon or on Mars. The current performance of this miniature LIMS instrument to study the chemical composition of apatite is sufficiently high to measure volatiles (H, F, Cl) and nearly all relevant mineral and partially trace elements (Na, C, Mg, Si, S, K, Mn, Fe, Sr, Ba), including REE (La, Ce, Pr, Sm) which allows for a systematic quantitative analysis of their distribution.

[1] Tulej, M. et al., 2022, https://doi.org/10.3390/universe8080410.

[2] Riedo, A. et al., 2012, https://doi.org/10.1002/jms.3104.

[3] Tulej, M. et al., 2021, https://doi.org/10.3390/app11062562.

[4] Lukmanov, R.A. et al., 2022, https://doi.org/10.3389/frspt.2022.718943

How to cite: Gruchola, S., Tulej, M., Keresztes Schmidt, P., Lukmanov, R., Riedo, A., and Wurz, P.: Composition Analysis of an Apatite Crystal using a Space-Prototype Mass Spectrometric Instrument and Machine Learning for Unsupervised Mineralogical Phase Detection, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11898, https://doi.org/10.5194/egusphere-egu23-11898, 2023.