GSST/CBERS-5 International Symposium on Machine Learning in Heliophysics and Space Weather

Aug 17, 2026, 9:00 AM → Aug 21, 2026, 5:30 PM America/Sao_Paulo

Fernando de Mendonça - LIT (National Institute for Space Research, São José dos Campos, SP, Brazil)

Luis Eduardo Antunes Vieira (INPE), Jose Marchezi (National Institute for Space Research - INPE), Franciele Carlesso (INPE)

The GSST/CBERS-5 International Symposium on Machine Learning in Heliophysics and Space Weather will bring together researchers, engineers, and data scientists working at the interface of artificial intelligence and solar–terrestrial physics. 

This focused symposium aims to explore how Machine Learning (ML) and Computer Vision (CV) techniques can enhance our understanding of heliophysical processes and improve predictive capabilities in space weather and space climate. Discussions will emphasize scientific and technological opportunities within the frameworks of the Galileo Solar Space Telescope (GSST) and the China–Brazil Earth Resources Satellite-5 (CBERS-5) missions, fostering collaborative efforts across Brazilian and international scientific communities.

While the primary emphasis is on heliophysics and space weather, contributions addressing applications of Machine Learning in Astrophysics and Earth Sciences are also strongly encouraged. Particular attention will be given to enabling technologies that underpin next-generation observational capabilities, including advanced instrumentation, high-performance computing, data assimilation frameworks, photonics and optical systems, remote sensing platforms, and autonomous space-based systems. Contributions involving large-scale data analysis, modeling, predictive methodologies, and sensor development are especially welcome.

The event is organized by the National Institute for Space Research (INPE), the University of Vale do Paraíba (UNIVAP), and Mackenzie Presbyterian University, with the support of the Brazilian Society of Space Geophysics and Aeronomy (SBGEA).

The organization of this symposium builds directly on discussions held during the 11th Annual BRICS Astronomy Working Group (BAWG) Workshop*, where ongoing and planned solar and space-weather observation missions were examined, including the Galileo Solar Space Telescope (GSST, Brazil), Aditya-L1 (India), the Chinese Solar Polar Orbit Observatory (China), and CBERS-5 (China/Brazil). These discussions underscored the importance of coordinated international efforts and advanced data-driven approaches for the next generation of heliophysics and space-weather research. 

*The 11th Annual BRICS Astronomy Working Group (BAWG) Workshop was financed, in part, by the São Paulo Research Foundation (FAPESP), Brasil. Process Number #2025/17984-0.

Topics to be Covered

• Computer Vision and Machine Learning applications in heliophysics

• Solar magnetism and magnetic-field inference

• Solar activity: flares, CMEs, and energetic particles

• Solar wind dynamics and heliospheric structures

• Space weather monitoring and forecast

• Physics-Informed Neural Networks

• Research-to-Operations processes augmented by AI and CV

 

Format and Participation

The symposium will feature invited talks, contributed oral presentations, and discussion sessions. Participation from both academia and operational space-weather centers is encouraged to bridge research and applications.

Important Dates

• February 11 2026: Submission of abstract for oral and poster sessions opens/ Call for forecast application opens/ Submission of applications for financial support opens

• March 30 2026 June 17 2026: Deadline for abstract submission / Call for space weather forecast application closes

• April 30 2026 June 27 2026: Notification of abstract acceptance

• March 23 2026 July 05 2026: Deadline for financial support application

• May 29 2026 July 12 2026: Deadline for early registration (participants awarded  financial support)

• August 17–21 2026: Conference     

• December 31 2026: Paper submission deadline

 

Registration & Abstracts

• Registration is open at  https://indico.global/e/gsstAI

• Conference fees: US$ 250

• Submission of applications for financial support opens (The organization will provide limited financial support to cover the conference fees). 

 

Organizing Committee

        LOC

• Luis Eduardo Vieira (Chair;INPE)
• José Marchezi (Chair; INPE)
• Franciele Carlesso (Chair; INPE)
• Alisson Dal Lago (INPE)
• Daniele Medeiros (INPE)
• Hadassa Raquel Peixoto Jácome (INPE)
• Adriany Barbosa (INPE)
• Ivana Yoshie Sumida (INPE)
• Paulo Jauer (INPE)
• Marcos Vinicius Silveira (INPE)
• Letícia Rocha Silva Ferreira (INPE)

 

        SOC

• Luis Eduardo Vieira (Chair;INPE)
• José Marchezi (Chair; INPE)
• Alisson Dal Lago (INPE)
• Joaquim Costa (INPE)
• Ligia Alves da Silva (INPE)
• Laysa Resende (INPE)
• Livia Ribeiro Alves (INPE)
• Jean Carlo Santos (INPE)
• Marlos Rockenbach (INPE)
• Paulo Simões (Mackenzie)
• N.L. Vijaykumar (INPE)
• Alan Prestes (UNIVAP)
• Fernando Luis Guarnieri (INPE)
• Rafael Santos (INPE)

 

Venue and Dates

The event will take place at INPE – National Institute for Space Research, São José dos Campos, Brazil, during the second week of August 2026 (August 17–21, 2026).

Support

Interested in supporting? Contact the organization at solarAI2026@inpe.br 

 

Templates SolarAI 2026 Poster Template.pptx

Mon, August 17

Opening Session: Opening Talks

Invited Talks

1 Selected advances achieved by space- and balloon-borne missions

The Sun is observed from the ground, from the stratosphere and from space. Although there are some observations that can only be done from space, e.g. in wavelengths such as the extreme ultraviolet that are absorbed in the Earth's atmosphere, and others that are currently possible only from the ground, e.g. the highest resolution observations with 4m diameter telescopes. However, there are a range of observations that are of great importance for our understanding of solar activity and space weather that can be carried out from each of the three environments. Perhaps the most fundamental of these are measurements of the Sun’s magnetic field. This talk will highlight advantages of observing the Sun from space and from the stratosphere, and provide some example results. It will also point out some of the challenges to observing the Sun from these environments.

Speaker: Sami Solanki (MPAE)

Oral Contributions

10:40 AM Coffee Break

Poster Session

Oral Contributions

2 Studying the Response of the Lower Solar Atmosphere to X-class Flare: 30 THz Thermal Diagnostics and Multi-Instrument Data Integration

We investigate the post-flare amplification of chromospheric 3-minute oscillations within a sunspot umbra following the SOL2024-08-08 X1.3-class flare. Utilizing high-resolution data from the DKIST Visible Broadband Imager at 450 nm and the AR30THz telescope focused on AR13777, we demonstrate the global dominance of 5-minute oscillations and the strict localization of 3-minute modes within the deep umbral photosphere. By spatially cross-correlating the deep photospheric wave field with the mid-infrared chromosphere, we measure a near-zero representative phase lag of 3.2 +/- 1.88s, consistent with the sub-cutoff evanescent nature of the waves, whereby the entire squeezed umbral column oscillates in unison as a phase-locked coherent unit. We then reveal a temporal disconnect (~54 minutes) between the flare’s impulsive phase and the sustained 30 THz wave amplification. We conclude that two sequential processes were required: the impulsive flare provided the initial mechanical perturbation that excited the natural oscillation frequencies of the umbral column, while delayed, gradual thermodynamic density enhancement subsequently altered the local H- and free-free opacity. This opacity tuning physically shifted the 30 THz formation height upward by ~200–250 km into the oscillating umbral chromosphere, allowing the wave power to permanently cross the 95% significance threshold. These results establish the mid-infrared continuum as an essential, scatter-free diagnostic for tracking how mechanical wave energy is processed and dissipated within the dynamic lower solar atmosphere.

Speaker: soumya shree sadangaya

12:30 PM Lunch

Invited Talks

3 From Pixels to Physics: Reconstructing the Solar Atmosphere with Physics-Informed AI

Artificial Intelligence (AI) is rapidly transforming how we analyze and interpret solar observations, enabling new approaches to long-standing challenges in heliophysics. In particular, recent advances in machine learning provide a pathway from data-driven image analysis toward physically consistent models of the solar atmosphere. Rather than treating observations as isolated measurements, these methods enable the reconstruction of continuous, multi-dimensional representations constrained by both observations and underlying physical principles.
In this presentation, I will discuss how Physics-Informed AI can bridge the gap between observations and models of the solar atmosphere. I will highlight recent developments emerging from ongoing, collective efforts across the heliophysics and space weather communities to leverage machine learning for 3D reconstruction, inversion, magnetic field extrapolation, and multi-instrument data integration. Together, these approaches demonstrate how sparse and heterogeneous observations can be transformed into physically meaningful representations of the solar atmosphere. These methods illustrate a broader paradigm shift—from pixel-level analysis to learning physically meaningful representations of the Sun. Finally, I will outline key open challenges and opportunities for physics-informed AI to enable the next generation of data-driven, physically consistent models of the solar atmosphere.

Speaker: Dr Robert Jarolim (High Altitude Observatory, NSF NCAR, USA.)

Oral Contributions

4 CMEAT: A Multimodal Dataset for Predicting CME Earth Impact and Transit Time

Geoeffective coronal mass ejections (CMEs) can disrupt satellites, power grids, and navigation systems, making accurate early warning critical for space weather operations. We present CMEAT, a curated multimodal dataset and fusion framework for predicting CME Earth impact and Sun–Earth transit time. CMEAT pairs CDAW LASCO observations (1996-2025) with ICME arrival labels and upstream L1 context, producing several thousand events, including hundreds of verified impacts. We extract a compact physical feature set and texture/geometric descriptors from LASCO C2/C3 running-difference imagery, and evaluate classical ML baselines alongside fusion strategies. On temporally held-out test sets, our fusion approaches achieve the best F1 score of 88.4% for impact classification and a mean absolute error of 19.04 hours for transit-time prediction, outperforming single-modality baselines.

Speaker: Besma Guesmi (Ubotica Technologies)

5 The new Solar Ultraviolet to Near-infrared Spectrometer (SUNS): searching for white-light flares

Since the first observation of a solar flare in 1859 by Carrington and Hodgson, explaining the origin of the excess visible continuum emission (white-light flares, WLFs) remains a challenge in the understanding of these events. Identifying the radiation mechanism involved is crucial for understanding the transport and deposition of energy in the solar atmosphere. However, spectral data for solar WLFs are relatively rare and insufficient to dispel the ambiguity of their origin: photospheric blackbody radiation or Paschen continuum from hydrogen recombination in the chromosphere. Due to the lack of solar observations with spectral resolution covering the entire visible range, we present a new telescope to meet this demand: the Solar UV-NIR Spectrometer (SUNS), developed and integrated at the Center for Radio Astronomy and Astrophysics Mackenzie (CRAAM), in partnership with the Steiner Institute, with funding from FAPESP and MackPesquisa. We will present its optical, mechanical, and spectral characteristics, observation conditions, future steps, and expected results. SUNS will provide resolved spectra in the visible range, which will allow us to test current visible continuum emission models and move towards a solution to the more than 165-year-old mystery of the origin of WLFs.

Speaker: Paulo Simões (Universidade Presbiteriana Mackenzie)

3:40 PM Coffee Break

Poster Session

3:40 PM Coffee Break

6 Adaptive Hybrid Distribution Modeling of Radiation Belt Electrons

The Earth’s radiation belts host electron populations that frequently deviate from thermodynamic equilibrium, exhibiting pronounced suprathermal tails and anisotropies driven by wave–particle interactions. Accurately representing these non-Maxwellian features is essential for understanding acceleration processes and improving predictive capabilities. In this work, we introduce an adaptive hybrid distribution modeling framework for radiation belt electrons. The method automatically identifies the optimal functional representation of the observed electron flux among Maxwellian, kappa, bi-Maxwellian, bi-kappa, and hybrid Maxwellian–kappa distributions. Model selection is performed algorithmically based on goodness-of-fit metrics, enabling a data-driven characterization of the underlying plasma regime. The selected distribution parameters are then used to reconstruct the electron flux density and to provide physically interpretable quantities such as density, characteristic temperature, and suprathermal index. Building upon this representation, we implement a neural network forecasting model that leverages the fitted distribution parameters to predict electron flux variability. This adaptive framework provides a unified, physically grounded approach to modeling and forecasting radiation belt electron populations, capturing both near-equilibrium and strongly non-thermal regimes.

Speaker: Jose Marchezi (National Institute for Space Research - INPE)

Invited Talks

7 Machine learning techniques for remote sensing of the Sun and inner heliosphere

Remote sensing of the Sun and the inner heliosphere remains the primary observational approach for investigating the physical mechanisms underlying the Sun’s short-term variability and its associated space weather phenomena. Given the significant societal and economic implications of space weather, a growing number of current and planned space- and ground-based observatories are dedicated to monitoring the solar atmosphere and the inner heliosphere. Over the past decade, solar physics has increasingly embraced state-of-the-art machine learning (ML) methodologies to address the challenges posed by the increasing volume, complexity, and dimensionality of scientific and operational solar data. In this talk, I present a curated compilation —assisted by artificial intelligence —of established and emerging ML applications, highlighting key trends across four major application domains:

• Data calibration (e.g., image deconvolution, super-resolution, denoising, and spectropolarimetric calibration).
• Feature classification and detection (e.g., of coronal holes, active regions, coronal mass ejections, and flare ribbons).
• Measurement and reconstruction (e.g., Stokes inversions, plasma velocity inference, coronal magnetic field extrapolations, coronal electron density mapping, and three-dimensional reconstruction of CME morphology).
• Forecasting (e.g., solar flares, solar energetic particle events, CME arrival times, sunspot number, and solar irradiance variability).

Speaker: Dr Francisco Iglesias (Grupo de Estudios en Heliofísica de Mendoza (GEHMe), Universidad de Mendoza, Argentina)

Oral Contributions: Heliophysics & Space Weather

Tue, August 18

Invited Talks

8 Solar magnetic field observation for HSOS: from SMFT to SPO

Solar magnetic field observations are mainly divided into two categories: high-resolution local magnetic field measurements and full-disk magnetic field observations. Over the past four decades, Huairou Solar Observing Station (HSOS) has conducted systematic research on the theory and technology of solar magnetic field observations based on these two categories. For high-resolution local magnetic field observations, the 35-cm Solar Magnetic Field Telescope (SFMT) has accumulated one of the longest homogeneous datasets of solar vector magnetic fields in the world, enabling key discoveries such as flare precursor motions and magnetic supergranule properties. As the world’s first mid-infrared solar magnetic field telescope, AIMS has revolutionized solar magnetic measurements by directly detecting Zeeman splitting at Mg I 12.3 μm and achieving a precision better than 10 Gauss. For full-disk magnetic field observations, the full-disk vector magnetograph SFMM has provided high-cadence full-disk solar vector magnetic field data with unprecedented spatial and temporal coverage, significantly improving the accuracy and reliability of solar activity forecasting and space weather research. The Solar Polar-orbit Observatory (SPO) has delivered unprecedented high-resolution observations of the solar polar regions, enabling breakthrough studies of polar magnetic fields, solar wind origin, and greatly advancing the global understanding of the solar dynamo and space weather drivers. This report presents the detailed characteristics and scientific contributions of SFMT, AIMS, SFMM, and SPO.

Speaker: Junfeng Hou

Oral Contributions

9 1-m off-axis mid -infrared solar magnetic field telescope (AIMS)

Magnetic field is the most important observational quantity in contemporary solar physics, as nearly all solar activities are closely associated with the solar magnetic field and its evolution. Currently, measurements of the solar magnetic field are primarily based on the Zeeman effect, where longer wavelengths offer higher detection precision and sensitivity. Therefore, conducting solar magnetic field observations in the mid-infrared band is the most effective approach to improving the accuracy of solar magnetic field measurements. The 1-m off-axis mid -infrared solar magnetic field telescope (AIMS) is the first solar telescope dedicated to mid-infrared solar magnetic field measurement in the world. The first-generation scientific instruments of AIMS are a 12.32 μm Fourier Transform Spectrometer and an 8–10 μm imager. After a decade of development, AIMS began scientific observations in October 2025. This report highlights the latest development progress and observational results of AIMS: AIMS has observed triply-split spectral lines in solar active regions and spectral evolution during solar flares at the 12.32 μm, and has captured multiple instances of radiation enhancement associated with solar flares using its 8–10 μm imager.

Speaker: Yuliang Shen (naoc)

10:40 AM Coffee Break

Poster Session

Oral Contributions

10 Interpretable Machine Learning for Solar Active Region Detection via 2D Circular Kernel Time-Series Transformation and Entropy Features

Solar active region detection from high-cadence EUV imagery is important for data-driven space weather monitoring, yet robust pixel-level characterization remains challenging because active and non-active bright structures can overlap in intensity and morphology. We present an interpretable machine-learning framework for solar active region detection in 193 Å images from the Atmospheric Imaging Assembly onboard NASA’s Solar Dynamics Observatory. The method converts the circular neighborhood around each sampled pixel into a one-dimensional data series using a 2D circular kernel transformation, enabling compact feature extraction from local image structure.
We investigate two feature-selection strategies: (i) statistical and entropy descriptors of the transformed series—median, 95th percentile, distribution entropy, and fuzzy entropy—and (ii) direct use of the transformed series values as input features. A support vector classifier with an RBF kernel is trained to assign each local region to one of three classes: quiet/no active region, non-eruptive bright zone surrounding an active region, and flaring active region. Using repeated stratified 10-fold cross-validation, the proposed framework yields classification accuracies of 0.900 with entropy features and 0.914 with statistical features, while the combined feature set reaches 0.940. Among individual descriptors, fuzzy entropy provides the best performance (A_KF = 0.895), outperforming distribution entropy (0.738), the 95th percentile (0.873), and the median (0.840).
The resulting classifications preserve a realistic spatial distribution of solar activity and support the definition of a generalized solar activity indicator derived from the fraction of flaring-region pixels. The proposed approach is scalable, interpretable, and suitable for automated event detection pipelines in heliophysics and space weather applications.

Speaker: Dr OLUDEHINWA Irewola Aaron (Department of Physics, Federal University of Agriculture, Abeokuta, Nigeria.)

12:30 PM Lunch

Invited Talks

11 Computer Vision with small-scale magnetic elements in the photosphere

Magnetic Bright Points (MBPs) are small-scale magnetic features, ubiquitous across the solar surface. They are short-lived, highly dynamic features which can transfer energy between layers of the solar. Furthermore, as they are small intensity enhancements, they appear brighter than the quiescent environment and can contribute to Total Solar Irradiance. With the advent of new facilities such as the Daniel K. Inouye Solar Telescope, MBPs are sampled at unprecedented resolutions. Here we report on a novel deep learning computer vision techniques developed to isolate and track these features in high resolution data. The results of these techniques are compared to more traditional computer vision methods.

Speaker: Dr Peter Keys (Astrophysics Research Centre, Queen’s University Belfast (QUB), U.K.)

Oral Contributions

12 Multidirectional Muon Detector for Solar-Terrestrial Physics

One important topic within Heliophysics is the modulation of <100GeV cosmic rays due to solar activity. Since 2001, a prototype of a multidirectional muon detector is in operation at the Southern Space Observatory, in Sao Martinho da Serra (SMS), Brazil. It is part of the Global Muon Detector Network (GMDN), composed by detectors in Nagoya (Japan), Kingston (Australia), Sao Martinho da Serra (Brazil) and Kuwait City (Kuwait). The SMS detector went through an upgrade in 2025 and it now operates with a collecting area of 36 m2. Since then, the SMS instrument was able to observe a few Forbush Decreases (FD) resulting from solar Coronal Mass Ejections (CMEs) and their interplanetary counterparts (ICMEs). We will show some preliminary ground cosmic ray directional observations of these recent FDs/CMEs (atmospheric pressure and temperature corrected observations). The SMS detector was a donation from Shinshu University (Japan) to the National Institute for Space Research (Brazil), in 2001 and 2005. The recent upgrade was funded by the Japan Society for the Promotion of Science (JSPS-Japan), the National Institute for Space Research (Brazil), the Brazilian Space Agency, and the Brazilian CAPES funding agency. Additional funding for data analysis is being requested to FAPESP funding agency.

Speaker: Alisson Dal Lago (INPE - National Institute for Space Research)

13 Modeling Neurocognitive Damage from Cosmic and Solar Radiation

Deep space missions beyond Low Earth Orbit expose crews to significant doses of galactic cosmic radiation (GCR), composed of high-energy protons and high-atomic-number, high-energy (HZE) ions including ⁵⁶Fe, ²⁸Si, ⁴⁸Ti, ¹⁶O, and ⁴He. Unlike the protection offered by Earth's magnetosphere, GCR cannot be attenuated by available spacecraft materials, and its cumulative effects on the central nervous system (CNS) represent one of the most critical risks for crewed missions to the Moon and Mars. Data from the RAD instrument aboard the Curiosity rover indicate that a six-month transit to Mars exposes crews to approximately 60% of the recommended career limit. Solar minimum periods further intensify GCR exposure during long-duration missions.
Experimental rodent studies at NASA's Space Radiation Laboratory (NSRL) demonstrate that exposure to mission-relevant doses (≤15 cGy) produces persistent neurocognitive deficits, including impairment of spatial memory, executive function, and attentional shifting. These effects are ion-type- and dose-dependent: ⁵⁶Fe at 1–10 cGy produces impairment in the simple discrimination (SD) and compound discrimination (CD) stages of the Attentional Set-Shifting Test (ATSET), while ⁴He and ²⁸Si produce distinct cognitive profiles in different brain regions, suggesting cerebral circuits are differentially affected by radiation field composition.
This work synthesizes recent advances in applying supervised machine learning (ML), including support vector machines (SVM), Gaussian naïve Bayes (GNB), and artificial neural networks (ANN), for predicting individual-level cognitive impairment in rodents subjected to GCR. Using pre-irradiation behavioral scores as multidimensional input features, classifiers demonstrate better than chance predictive capability, surpassing conventional population level statistical analyses. ML models based on U-Net architectures calculate physical dose distributions of heavy ions with deviations under 2% from Monte Carlo simulations in milliseconds, highlighting ML potential for accurate modeling of HZE effects in physical planning and biological prediction. Neurobiological mechanisms, including microglial activation, reduced hippocampal neurogenesis, loss of dendritic spines, and disruption of synaptic plasticity, establish the biological plausibility of individual susceptibility to GCR. Genetic modifiers such as ATM heterozygosity and ApoE isoforms further modulate this sensitivity, reinforcing the need for personalized pre-mission risk stratification. ML tools trained with ground-based GCR data can screen astronaut candidates and predict cognitive impairment before departure, supporting permissible exposure limits and countermeasures integrated into space weather monitoring.

Speakers: Alexandre da Silva Santos (Federal University of Maranhão), Roger Pinheiro Presoti (Universidade Federal do Maranhão (UFMA))

14 Automatic Characterization and Three-dimensional Reconstruction of Coronal Mass Ejections Using Deep Learning Techniques

The study of space weather critically depends on the three-dimensional (3D) morphological and kinematic characterization of coronal mass ejections (CMEs). This process can be done via generic 3D point position estimation (using e.g., tie-pointing plus triangulation, differential emission measure tomography, polarization ratio or neural radiation fields techniques) or model-based 3D reconstructions. The former is typically limited by scarce spatial coverage and/or limited temporal resolution, critical for extended and dynamic events such as CMEs. Model-based approaches are usually done via a manual fit of a parametric geometric model to the CME outer shell observed in coronographic images, which is time-consuming and prone to subjective biases. To address these limitations, we present a deep learning framework structured into three fundamental stages. First, to mitigate the lack of pixel-level labeled observational data, we develop a methodology for generating large-scale synthetic training datasets; this is achieved by combining real coronal backgrounds with synthetic CME brightness images produced by applying ray-tracing techniques to a density distribution defined by the Graduated Cylindrical Shell (GCS) geometric model. Second, using these synthetic data, a Mask R-CNN convolutional network was trained in a supervised manner for the instance segmentation of the CME outer envelope in differential coronographic images. Its outcome demonstrates a high capacity to discriminate the CME from other radially moving structures without requiring explicit kinematic information. Finally the 3D reconstruction is addressed using a deep convolutional network architecture, designed to automatically infer the six parameters of the GCS model from input coronagraph images acquired from three different perspectives. Altogether, the implementation of these neural networks represents a significant advance toward the automated detection and 3D morphological characterization of CMEs, reducing human intervention and accelerating computational analysis.

Speaker: Mariano Sanchez Toledo (Grupo de Estudios en Heliofísica de Mendoza, Universidad de Mendoza, Mendoza, Argentina)

3:40 PM Coffee Break

Poster Session

Invited Talks

15 Individual and Collective Dynamics of Vortical Structures in the Solar Atmosphere

In this presentation, we discuss recent advances in understanding vortical plasma motions within the solar atmosphere. Using high-resolution magnetoconvection simulations from the Bifrost, MURaM, and R2D2 codes, together with observational data (SST and Sunrise), and state-of-the-art techniques for identifying plasma flows and transport barriers (machine learning, forward and backward finite-time Lyapunov exponents, FTLE), we show that vortex structures play a fundamental role in energy transport across different layers of the solar atmosphere and support wave propagation. Analogously to the application of FTLE in hydrodynamic flows, the ridges of forward and backward FTLE fields can indicate the locations of energy sources in the simulated solar atmosphere. In particular, we focus on three distinct classes of vortices, i.e. kinetic, magnetic, and energy vortices, highlighting their physical properties, formation mechanisms, and contributions to plasma dynamics. We also describe the network of Poynting flux inferred from the FTLE fields at different heights, connecting the photospheric and chromospheric layers. Beyond individual vortical structures, we explore their collective behaviour within (intra-connected) and between (inter-connected) communities of vortical motions, examining how these structures interact across a wide range of spatial and temporal scales. We demonstrate that their coupled dynamics can enhance energy transport to the higher layers of the solar atmosphere and beyond.

Speaker: Dr Viktor Fedun (School of Electrical and Electronic Engineering, The University of Sheffield, UK.)

Oral Contributions

Wed, August 19

Invited Talks

16 Wave theories and modelling in the lower solar atmosphere

In this talk I will review recent progress and the remaining challenges in both modelling and analysing observed magnetohydrodynamic (MHD) waves in the lower solar atmosphere. To model waves in sunspot umbrae, we have had to go beyond the standard cylindrical flux tube model due to the irregular shapes of the MHD waveguides. For sunspots the umbra/penumbra boundary shape is reasonably static over the time scales of oscillations of interest. This makes solving the eigenvalue problem for MHD wave modes in sunspots tractable because of the assumption of time independent boundary shape conditions. However, the same cannot be said about pores that have highly dynamic boundary shape changes. I will highlight recent progress that has been made in this very challenging area of research.

Speaker: Dr Gary Verth (School Mathematical and Physical Sciences, University of Sheffield, UK.)

Oral Contributions

10:40 AM Coffee Break

Poster Session

Oral Contributions

12:30 PM Lunch

Invited Talks

17 The impact of machine learning, AI, and novel analysis tools to better understand the plasma dynamics of the Sun

Solar atmospheric plasmas host highly dynamical magnetohydrodynamic (MHD) processes, including reconnection, shocks, turbulence, and waves, that couple the photosphere, chromosphere, and corona and ultimately seed variability throughout the heliosphere. This talk connects these drivers to their signatures in the solar wind and at Earth’s ionosphere, emphasizing the need for consistent “Sun-to-geospace” diagnostics. I will highlight how modern collaborative efforts are tackling long-standing obstacles in identifying and quantifying MHD wave modes in structured magnetic features, including the role of radiative transfer, partial ionisation, and multi-height spectropolarimetry. I will then demonstrate how modern machine learning is accelerating plasma inference through use of neural classifiers for Stokes-profile morphologies, transformer-based stratified inversions, and the emerging promise of Physics-Informed Neural Networks (PINNs) to connect inversions directly to governing MHD physics. Remaining challenges include the domain shift between simulations and observations, and the robust coupling of wave energetics to atmospheric heating. Finally, I will outline how next-generation instrumentation, such as integral field spectropolarimeters and next-generation space missions such as the Galileo Solar Space Telescope, will deliver the cadence, sensitivity, and coverage needed to close these gaps.

Speaker: David B. Jess

Oral Contributions

18 Solarfall: A Multi-Stage Machine Learning Architecture for Solar Flare Forecasting

Forecasting solar flares is essential for mitigating space weather risks that threaten technological infrastructures. This research investigated the most relevant attributes for predicting solar flares by comparing the predictive performance of models based on two different predictive philosophies: an "Effect-to-Effect" approach based on X-ray inertia, and a "Cause-to-Effect" approach focused on magnetic topology of active regions. Additionally, an arbiter Meta Model was proposed to integrate both datasets. To address the high dimensionality and class imbalance characteristic of astrophysical data, an hierarchical cascade architecture named Solarfall was developed. This architecture employs four sequential XGBoost-based specialist classifiers to separate a solar flare signal from systemic noise.
Specifically, Solarfall divides the detection problem into four specialized stages. The initial stages prioritize maximizing recall by separating potential alerts from solar calm and filtering out low-impact noise. Subsequent stages act as mechanisms, progressively isolating medium flares from severe threats, and ultimately distinguishing Class X events from Class M storms. To prevent the propagation of cascading errors and overcome exposure bias, the intermediate and final classifiers underwent conditioned training. They were trained on the residual distribution—containing both true signals and false positives—that survived previous cuts, allowing the algorithm to learn the mathematical signatures of predecessor errors and protect the final decision's rigor.
The study used data spanning from 2010 to 2024, employing a chronological split to test the models on Solar Cycle 25. Results indicated that the multi-stage cascade has potential to reduce noise, filtering the initial 158,906 samples down to 26,428 (17%) at the third stage and to 4,440 (3%) at the fourth and last stage, marking a 97% reduction. However, the X-ray approach proved inadequate for forecasting extreme Class X events, yielding a negligible recall of 0.03 at the final stage (capturing 3% of extreme flares). Furthermore, evaluating the integration of x-ray flux and magnetic topology over the test set refuted the hypothesis of a superior combined model. The unification triggered an effect where X-ray flux acted as a confounding variable that degraded the system's accuracy and corrupted predictions.
Conversely, the architecture relying on magnetograms achieving a robust Precision-Recall Area Under the Curve of 0.462 and a precision of 0.48 for events Classes M and X. The research concludes that physical preconditions of an active region like current density and magnetic free energy, constitute the most relevant indicators for solar flare forecasting, outperforming generalized macroscopic approaches.

Speaker: Eduardo Ferraz de Campos (Federal Institute of São Paulo)

3:40 PM Coffee Break

Poster Session

Invited Talks

19 The Data-Driven Evolution of Heliophysics: Bridging Physical Mechanisms and Machine Learning in the Inner Magnetosphere

Heliophysics was born as a data-driven scientific field. From the discovery of the radiation belts and the confirmation of the existence of the solar wind, with the beginning of the space age, and until the latest discoveries in solar, magnetospheric, and ionospheric dynamics and coupling mechanisms, our field has always relied on the use of abundant in-situ and remote measurements to confirm physical processes and theoretical ideas, and to discover new phenomena. Thus, with every leap in technology, the field has greatly advanced. In recent years, the abundant data generated by ground and space-based and ground instruments, coupled with the increasing availability of such data in both long-term repositories and near-real time, has created both challenges and opportunities for new science and predictive modelling. In particular, the explosion of Data Science, Machine Learning Methods, and Artificial Intelligence algorithms to handle large datasets has been crucial to enable new scientific discoveries and develop low-cost data-driven models for Space Weather applications. While we are still in the beginning of the data-driven era of Space Weather, significant advancements have occurred in the past years, in part, due to the lower cost of running models in real time compared to the more traditional physics-based methods that usually require large data centers. This talk discusses the pivotal role of Machine Learning (ML) and Artificial Intelligence in navigating this data-rich era. I will present an overview of the latest scientific results in solar wind-magnetosphere coupling, with a focus on radiation belt processes. In particular, I will discuss recent results in MeV electron acceleration and losses during geomagnetically active times, as well as the effects of solar-wind driven perturbations in the inner-magnetosphere dynamics. The scientific results will then be put in the context of the recent efforts that have been made in using this physical knowledge and data availability for creating better predictive models in the Earth's inner magnetosphere.

Speaker: Victor A. Pinto

Oral Contributions

20 Application of Machine Learning Techniques for Instability Prediction and Risk Management Support in Mining Tailings Pile

The stability of tailings storage structures represents one of the major challenges in contemporary geotechnical engineering, particularly considering the environmental, social, and economic impacts associated with geotechnical failures. The increasing adoption of filtered tailings systems and dry stacking methods has expanded the use of tailings piles as an alternative to conventional disposal techniques. Despite the advantages associated with these structures, factors related to hydraulic behavior, infiltration processes, moisture variations, slope stability, and environmental conditions make their analysis a complex problem.

At the same time, advances associated with Industry 4.0 have enabled the application of Artificial Intelligence, Machine Learning, remote sensing, and real-time geotechnical monitoring techniques for identifying patterns related to structural stability. However, most existing studies remain focused on tailings dams, while only a limited number of investigations have specifically addressed tailings piles through the integrated use of multiple data sources.

In this context, the present study proposes an integrated approach based on Machine Learning techniques to identify instability precursor patterns in tailings piles using geotechnical, climatic, instrumental, and geospatial data. Models such as Random Forest, XGBoost, and Long Short-Term Memory (LSTM) networks will be evaluated to identify complex and temporal relationships within the datasets. The expected results aim to contribute to the development of methodologies for preventive monitoring, risk mitigation, and the advancement of scientific knowledge regarding the application of Artificial Intelligence in the mining ind

Speaker: Dr Matheus Alves de Barros (Topotech - Soluções em Topografia e Georreferenciamento)

21 A CNN Classifier for Hidden Solar Flares in Historical UV Spectroscopic Plates From Skylab SO82B

The NRL SO82B spectrograph on board of Skylab captured more than 6,000 far-ultraviolet photographic exposures of the Sun between 1973 and 1974. UV flare spectra are still rare with only a few reported during the mission. This work presents a supervised binary image classifier based on a ResNet-18 convolutional neural network (CNN) to identify uncatalogued flares in the SO82B data. The CNN was trained with transfer learning from ImageNet weights to distinguish flare from non-flare SO82B plate images. Training on 1,281 labelled frames (321 flares and 960 non-flares) and evaluating on a held-out test set of 641 independent examples (161 flares and 480 non-flares), the model achieved a ROC-AUC of 0.88 and an area under the precision-recall curve (AUPRC) of 0.75, confirming adequate discrimination capability. At the standard threshold of p > 0.50, the model flagged 400 candidate frames, achieving a held-out flare recall of 72.7% and a false-positive rate of 9.8% on held-out non-flares. Further analysis revealed a strip-level systematic: after plate-level suppression the 253 candidates (p > 0.70) reduce to 136 unique plates, and after strip-level suppression to all 3 film strips. This collapse suggests the model partially responds to strip-level properties, such as emulsion batch or scanner calibration artefacts, rather than exclusively to frame-level flare morphology, which it was expected. To isolate genuine events from this systematic baseline, a within-strip outlier analysis was applied, retaining only plates whose maximum frame score exceeds the strip median by more than 2σ. This conservative approach has led to identification of 68 plates of statistically significant, of which 20, it was from strip 1B (May 30 – Jun 17, 1973) and 48 from strip 2B (Aug 09 – Sep 12, 1973). Cross-referencing the 68 outlier candidates against the NOAA/NGDC flare catalogue for 1973 reveals that 5 candidate plates have capture times that fall within the duration of a catalogued optical flare event, and with 10 more within 30min of a catalogued flare; the strongest temporal match is on 1973 June 15, where four consecutive SO82B plates were acquired during a class 1F optical flare at active region AR 2382. Gradient-weighted class activation maps confirm that the network attends to the chromospheric emission core of the UV spectrum rather than plate artefacts or edges. The resulting candidate list provides a prioritised target list for spectral follow-up and for extending the SO82B flare database.

Speakers: Tiago Mendes Ferrer (@Mackenzie), Paulo Simões (Universidade Presbiteriana Mackenzie)

Thu, August 20

Invited Talks

22 Coupling Between Complex Solar Wind Structures and Outer Radiation Belt Dynamics

The solar photosphere produces various solar structures, such as Coronal Mass Ejections (CMEs) and High-Speed Solar Wind Streams (HSSs), which can propagate toward Earth and interact in the interplanetary medium, forming complex solar wind structures. In this study, we focus on events composed of two identical solar wind structures, specifically those formed by two CMEs or by two HSSs. While the coupling between a single ICME or HSS and the Earth’s magnetosphere, as well as its impact on electron flux variability in the outer radiation belt, is relatively well understood, the effects of complex solar wind structures remain insufficiently explored, particularly regarding outer radiation belt dynamics. This work uses the solar wind parameters measured at L1 Lagrangian point to identify the main characteristics of each complex solar wind structure, especially in terms of velocity components and the energy transport, which exhibit markedly different behaviors. In the inner magnetosphere, in situ measurements of high-energy electron flux and magnetospheric waves, such as ultra-low frequency (ULF) and whistler-mode chorus waves, together with calculations of radial and pitch angle diffusion coefficients, indicate that these processes respond differently during electron flux enhancements driven by each type of complex structure. We explicitly compare how the different solar wind drivers modulate energy transfer, wave activity, and diffusion processes, thereby governing the dynamic response of the outer radiation belt. These results demonstrate that the coupling between complex solar wind structures and outer radiation belt dynamics depends on the specific type of solar structures composing the event, as their intrinsic properties differ substantially. Furthermore, our findings suggest that empirical models used to calculate radial and pitch angle diffusion coefficient may require refinement to account for the distinct characteristics of different complex solar wind drivers.

Speaker: Dr Ligia Alves da Silva (Instituto Nacional de Pesquisas Espaciais - INPE)

Oral Contributions

23 GNSS-TEC Characterization of the 17 March 2015 Geomagnetic Storm over Morocco as a Benchmark for Data-Driven Space Weather Monitoring

We investigated the ionospheric response to the 17 March 2015 geomagnetic storm, the most severe event of solar cycle 24 (SYM-H = -233 nT), using GPS-derived total electron content (TEC) observations from the RABT station in Morocco (34°N, 7°W). Vertical TEC (VTEC) was derived from dual-frequency GPS measurements using differential code bias corrections and a 30° elevation cutoff. The results reveal a strong storm-time enhancement, with ΔVTEC reaching +45.78 TECU, corresponding to a 158% increase above the quiet-day baseline of 28.92 TECU. Lagged correlation analysis shows the strongest relationship with Kp at approximately 3 hours delay (r ≈ 0.53), indicating structured ionospheric variability linked to geomagnetic forcing. The temporal evolution is consistent with rapid electrodynamic effects during the main phase and composition-related changes during the recovery phase. This study highlights the usefulness of GNSS-TEC time series for regional space weather monitoring and provides a physically interpretable benchmark for future machine-learning approaches to ionospheric disturbance detection and prediction in the still understudied North African sector.

Speaker: Nouhaïla Bouhadi

10:40 AM Coffee Break

Poster Session

Oral Contributions

24 EoFTCNets: Efficient Solar Flare Nowcasting using 3D Temporal Convolutional Networks (3DTCN)

In the evolving landscape of 21st-century space science, forecasting space weather events such as solar flares and Coronal Mass Ejections (CMEs) are crucial yet challenging. Solar flares are intense bursts of radiation caused by the release of magnetic energy in active regions and are often accompanied by CMEs. These events can significantly impact Earth’s space environment, causing disruptions in radio communication, satellite operations, and power grids. Monitoring the temporal evolution of active regions and providing early warnings of solar flares is essential to mitigate these risks. Deep learning techniques have demonstrated significant success in detecting and predicting time-dependent events. By leveraging spatial data through convolution operations with temporal correlations, we introduce 3D Temporal Convolutional Networks(3DTCNs) to efficiently analyze active region patches over time, leveraging spatial and temporal correlations. Additionally, we introduce separate predictor modules based on flare classification to enhance the performance of our EoFTC-Nets (Eye-on-Flare Temporal Convolutional Networks) nowcasting system. Our results demonstrate that the proposed architecture matches or outperforms state-of-the-art approaches in the literature, achieving an accuracy exceeding 96% for a 24-hour forecasting window. Furthermore, the model is computationally efficient, consuming approximately 1.2 watts on Intel Movidius Myriad X, making it well-suited for onboard deployment and real-time space weather monitoring.

Speaker: Besma Guesmi (Ubotica Technologies)

12:30 PM Lunch

Invited Talks

25 The Hainan Ionogram Dataset: Supporting AI-Enabled Spread-F Monitoring, Prediction, and Space Weather Operations

Equatorial and low-latitude Spread-F (SF) can severely affect radio communication and navigation systems, making timely identification and prediction important for space weather monitoring and operations. Although artificial intelligence (AI) has shown strong potential for automating ionogram analysis, broader progress has been limited by the lack of large-scale, well-annotated, and publicly available datasets. Traditional manual ionogram scaling and interpretation are labor-intensive and subject to inconsistencies, especially for long-term observations and borderline cases.

Here we present a long-term ionogram dataset from the Hainan station (19.5°N, 109.1°E), covering 2002–2016 and spanning an entire solar cycle. The archive includes more than 517,000 expert-labeled raw ionograms. Building on this long-term observational foundation, we established a standardized human-in-the-loop active learning workflow to improve labeling consistency and scalability. This process led to a curated public dataset of 150,000 high-quality images, balanced across five classes—frequency SF, range SF, mixed SF, strong range SF, and non-SF—with 30,000 samples per class. The released dataset provides a practical benchmark resource for AI-based ionogram analysis and supports the development of scalable methods for future large-volume observations.

We further illustrate the value of this dataset through three published AI studies based on the Hainan observations: (1) real-time automated detection and subtype classification of SF using image-based deep learning; (2) nowcasting of ionogram sequence evolution using a spatio-temporal ConvGRU framework; and (3) physically constrained generative modeling of quiet and disturbed ionospheric features using IonoGAN. Together, these studies demonstrate that the Hainan dataset can support diverse downstream tasks, from automated event identification to short-term prediction, and can serve as a data foundation for AI-enabled space weather research and operations.

Speaker: Zheng Wang (National Space Science Center, CAS)

Oral Contributions

26 The Alfvén Critical Surface via Parker Solar Probe: Current Findings and Future Directions

This work aims to identify and characterize the critical Alfvén surface, which marks the boundary of the solar corona. Within this surface, the solar plasma is in a sub-Alfvénic regime, whereas beyond it, the solar wind becomes super-Alfvénic. The study presents theoretical foundations on the solar structure, plasma characteristics and Alfvén waves in the solar corona. The analysis was performed using data from the Parker Solar Probe from the eighth, ninth and tenth solar flybys. The parameters evaluated to determine the behavior of the plasma in the study region were: the magnetic field, its radial component, the particle density, the Alfvén velocity, the radial velocity, the plasma beta, the ratio between the kinetic energy, the magnetic energy and the Mach number. The results indicate that, during the eighth encounter, it was possible to identify the moment of the spacecraft’s entry into and exit from the solar corona, whereas during the ninth and tenth encounters only the entry was identified. Future research perspectives include investigating the energy balance (kinetic, magnetic and internal), along with performing solar wind simulations, incorporating the spacecraft trajectory during intervals in which it is within the sub-alfvénic plasma regime.

Speakers: Caroline Botelho (Instituto Nacional de Pesquisas Espaciais), PAULO RICARDO JAUER (Instituto Nacional de Pesquisas Espaciais INPE)

3:40 PM Coffee Break

Poster Session

Invited Talks

Oral Contributions