arXiv subjects

Aatiya Ali

Publications and source records attributed to Aatiya Ali.

10 recordsLinked to original sources

arXiv2026

Review of Machine Learning Models for Solar Energetic Particle Prediction

Solar energetic particle (SEP) events have attracted increasing attention due to their significant radiation hazards for aviation, spacecraft electronics, and human missions beyond Earth's magnetosphere. From a scientific perspective, SEP events are intriguing because they arise from a set of physical processes extending from the solar surface and corona through the heliosphere, offering insight into particle acceleration and transport mechanisms that are widely applicable across astrophysics. Therefore, advancing our ability to understand and predict SEP events is essential both for deepening our knowledge of such mechanisms and for safeguarding space technologies and exploration. Traditionally, researchers have modeled SEPs using physics-based simulations and empirical methods. More recently, machine learning (ML) has emerged as a new tool for understanding and predicting SEP events. The purpose of this manuscript is to review the currently available ML models for SEP prediction, identify the datasets used for training, compare their architectures, inputs, and outputs, and, based on these insights, outline good practices and recommendations for future research.

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arXiv2025

Comparative Analysis of 10 - 50 MeV Solar Proton Events at Lagrange Point 1 and the Geostationary Orbit

Solar proton events (SPEs) pose radiation hazards, disrupt technology, and impact operations on Earth and in space, making continuous monitoring essential. We compare 10-50 MeV proton flux measurements from SOHO/EPHIN at Lagrange Point 1 (L1) with those from NOAA/GOES in geostationary orbit (GEO) during Solar Cycle 23 and most of Cycle 24. We identify 83 >=10 pfu SPEs observed at both locations and classify them into S1-S4 categories (comparable to NOAA's solar radiation storm scales). EPHIN detected earlier onsets and longer durations across all categories, along with earlier peaks and ends for S1-S3, while GOES recorded slightly earlier peak and end times for S4. S1 median timing offsets (EPHIN relative to GOES) were -20 +/- 50 min (onsets), -1.00 +/- 1.42 hr (peaks), and -1.08 +/- 2.21 hr (ends), with similar trends for S2-S3 and near-simultaneity for S4 (peaks ~ -0.17 +/- 1.62 hr; ends ~ +0.04 +/- 3.33 hr). Flux comparisons show that EPHIN measurements modestly exceed GOES for S1 (median ratios ~1.11 for peaks and ~1.06 for fluence) and are lower than GOES for stronger events (peaks ~0.97 +/- 0.29, 0.84 +/- 0.21; fluence ~0.84 +/- 0.16, 0.75 +/- 0.16 for S2-S3). The EPHIN-to-GOES peak flux and fluence ratios reach 0.16 +/- 0.03 and 0.29 +/- 0.07, respectively, for S4 events, originating from contamination of lower-energy GOES channels. Correlation analyses show no significant flux dependence on geomagnetic indices, field strength, or spacecraft position, suggesting minimal near-Earth modulation of >=10 MeV proton access at GEO. These results highlight systematic differences in how SPEs manifest at L1 versus GEO and offer practical guidance for forecasting beyond Earth's magnetosphere, supporting mission planning for near-Earth and cislunar exploration, including Artemis.

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arXiv2025

Using ML-based Regression Techniques to Mitigate GOES Energetic Proton Flux Data Contamination and Magnetospheric Effects

Positioned at geostationary orbit (GEO) ~36,000 km above Earth, NOAA's GOES series has recorded real-time energetic proton flux measurements crucial for space weather monitoring for over three decades. Although machine learning models have advanced solar energetic particle (SEP) event prediction using GOES data, the sudden yet sparse nature of SEP events necessitates high-quality proton flux measurements. Previous studies have identified contamination issues in GOES data, when the presence of higher-energy protons can cause parasitic signals in lower-energy GOES channels and lead to artificially elevated fluxes in lower energy ranges (e.g., 10 - 50 MeV). As of now, no universal correction method has been implemented for the publicly available NOAA data. In addition, the effects of Earth's magnetosphere on the 10 - 50 MeV particles are not fully understood yet. This study assesses a reconstruction method using concurrent solar proton event (SPE) measurements from SOHO-EPHIN, which align well with GOES measurements of SPEs across solar cycles 23 and the bulk of cycle 24, but represent the off-magnetospheric environment of the Lagrange 1 point. We train regression models on GOES proton fluxes across multiple energy bins, employing EPHIN fluxes as prediction targets. We expect that similar approaches can allow us to derive non-contaminated flux proxies that preserve valuable data and more accurately capture the characteristics of SPEs, providing a more stable dataset for analyzing SEP behavior and potentially improving SEP event prediction models.

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arXiv2025

Operational and Exploration Requirements and Research Capabilities for SEP Environment Monitoring and Forecasting

Mitigating risks posed by solar energetic particles (SEPs) to operations and exploration in space and Earth's atmosphere motivates the development of advanced, synergistic approaches for monitoring, modeling, and analyzing space weather conditions. The consequences of SEPs and their interactions with the near-Earth space environment are numerous, including elevated radiation levels at aviation altitudes during major events, satellite damage, and health risks to astronauts, resulting in economic impacts and potential hazards for space exploration. This contribution will present a high-level overview of the operational requirements and research capabilities for SEP event environment monitoring and forecasting that were highlighted during a workshop at Georgia State University, held on October 16-19, 2024. Specifically, it summarizes the presented activities concerning the following: (1) Identifying needs for SEP event forecasting and nowcasting, including practical forecast timeframes; (2) Reviewing availability and coverage of the current observational data and identifying tangible data resources for research, operations and the R2O2R loop; (3) Mapping existing forecast capabilities and identifying meaningful modeling advances for research and operations.

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arXiv2024

The Random Hivemind: An Ensemble Deep Learner Application to Solar Energetic Particle Prediction Problem

The application of machine learning and deep learning, including the wide use of non-ensemble, conventional neural networks (CoNN), for predicting various phenomena has become very popular in recent years thanks to the efficiencies and the abilities of these techniques to find relationships in data without human intervention. However, certain CoNN setups may not work on some datasets, especially if the parameters passed to it, including model parameters and hyperparameters, are arguably arbitrary in nature and need to continuously be updated with the need to retrain the model. This concern can be partially alleviated by employing committees of neural networks that are identical in terms of input features and architectures, initialized randomly, and "vote" on the decisions made by the committees as a whole. Yet, it is possible for the committee members to "agree" on identical sets of weights and biases for all nodes and edges. Members of these committees also cannot be expanded to accommodate new features and entire committees must therefore be retrained in order to do so. We propose the Random Hivemind (RH) approach, which helps to alleviate this concern by having multiple neural network estimators make decisions based on random permutations of features and prescribing a method to determine the weight of the decision of each individual estimator. The effectiveness of RH is demonstrated through experimentation in the predictions of hazardous Solar Energetic Particle (SEP) events by comparing it to that of using both CoNNs and the aforementioned setup of committees. Our results demonstrate that RH, while having a comparable or better performance than the CoNN and a Committee-based approach, demonstrates a lesser score spread for the individual experiments, and shows promising results with respect to capturing almost every single flare instance leading to SEPs.

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arXiv2024

Time Series of Magnetic Field Parameters of Merged MDI and HMI Space-Weather Active Region Patches as Potential Tool for Solar Flare Forecasting

Solar flare prediction studies have been recently conducted with the use of Space-Weather MDI (Michelson Doppler Imager onboard Solar and Heliospheric Observatory) Active Region Patches (SMARP) and Space-Weather HMI (Helioseismic and Magnetic Imager onboard Solar Dynamics Observatory) Active Region Patches (SHARP), which are two currently available data products containing magnetic field characteristics of solar active regions. The present work is an effort to combine them into one data product, and perform some initial statistical analyses in order to further expand their application in space weather forecasting. The combined data are derived by filtering, rescaling, and merging the SMARP with SHARP parameters, which can then be spatially reduced to create uniform multivariate time series. The resulting combined MDI-HMI dataset currently spans the period between April 4, 1996, and December 13, 2022, and may be extended to a more recent date. This provides an opportunity to correlate and compare it with other space weather time series, such as the daily solar flare index or the statistical properties of the soft X-ray flux measured by the Geostationary Operational Environmental Satellites (GOES). Time-lagged cross-correlation indicates that a relationship may exist, where some magnetic field properties of active regions lead the flare index in time. Applying the rolling window technique makes it possible to see how this leader-follower dynamic varies with time. Preliminary results indicate that areas of high correlation generally correspond to increased flare activity during the peak solar cycle.

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arXiv2023

Predicting Solar Proton Events of Solar Cycles 22-24 using GOES Proton & soft X-Ray flux features

Solar Energetic Particle (SEP) events and their major subclass, Solar Proton Events (SPEs), can have unfavorable consequences on numerous aspects of life and technology, making them one of the most harmful effects of solar activity. Garnering knowledge preceding such events by studying operational data flows is essential for their forecasting. Considering only Solar Cycle (SC) 24 in our previous study, Sadykov et al. 2021, we found that it may be sufficient to utilize only proton and soft X-ray (SXR) parameters for SPE forecasts. Here, we report a catalog recording $\geq$ 10 MeV $\geq$ 10 particle flux unit SPEs with their properties, spanning SCs 22-24, using NOAA's Geostationary Operational Environmental Satellite flux data. We report an additional catalog of daily proton and SXR flux statistics for this period, employing it to test the application of machine learning (ML) on the prediction of SPEs using a Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGBoost). We explore the effects of training models with data from one and two SCs, evaluating how transferable a model can be across different time periods. XGBoost proved to be more accurate than SVMs for almost every test considered, while outperforming operational SWPC NOAA predictions and a persistence forecast. Interestingly, training done with SC 24 produces weaker TSS and HSS2, even when paired with SC 22 or SC 23, indicating transferability issues. This work contributes towards validating forecasts using long-spanning data -- an understudied area in SEP research that should be considered to verify the cross-cycle robustness of ML-driven forecasts.

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arXiv2023

Statistical Study of the Correlation between Solar Energetic Particles and Properties of Active Regions

The flux of energetic particles originating from the Sun fluctuates during the solar cycles. It depends on the number and properties of Active Regions (ARs) present in a single day and associated solar activities, such as solar flares and coronal mass ejections (CMEs). Observational records of the Space Weather Prediction Center (SWPC NOAA) enable the creation of time-indexed databases containing information about ARs and particle flux enhancements, most widely known as Solar Energetic Particle events (SEPs). In this work, we utilize the data available for Solar Cycles 21-24, and the initial phase of Cycle 25 to perform a statistical analysis of the correlation between SEPs and properties of ARs inferred from the McIntosh and Hale classifications. We find that the complexity of the magnetic field, longitudinal location, area, and penumbra type of the largest sunspot of ARs are most correlated with the production of SEPs. It is found that most SEPs ($\approx$60\%, or 108 out of 181 considered events) were generated from an AR classified with the 'k' McIntosh subclass as the second component, and these ARs are more likely to produce SEPs if they fall in a Hale class containing $δ$ component. The resulting database containing information about SEP events and ARs is publicly available and can be used for the development of Machine Learning (ML) models to predict the occurrence of SEPs.

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arXiv2022

A spectroscopic survey of infrared 1-4μm spectra in regions of prominent solar coronal emission lines of Fe XIII, Si X, and Si IX

The infrared solar spectrum contains a wealth of physical data about the Sun and is being explored using modern detectors and technology with new ground-based solar telescopes. One such instrument will be the ground-based Cryogenic Near-IR Spectro-Polarimeter of the Daniel K. Inouye Solar Telescope that will be capable of sensitive imaging of the faint infrared solar coronal spectra with full Stokes I, Q, U, and V polarization states. Highly ionized magnetic dipole emission lines have been observed in galaxies and the solar corona. Quantifying the accuracy of spectral inversion procedures requires a precise spectroscopic calibration of observations. A careful interpretation of the spectra around prominent magnetic dipole lines is essential for deriving physical parameters, and particularly, for quantifying the off-limb solar coronal observations from DKIST. In this work, we aim to provide an analysis of the spectral regions around the infrared coronal emission lines of Fe XIII 1074.68 nm, Fe XIII 1079.79 nm, Si X 1430.10 nm, and Si IX 3934.34 nm, aligning with the goal of identifying solar photospheric and telluric lines that will help facilitate production of reliable inversions and data products from four sets of solar coronal observations. The outputs will be integrated in the processing pipeline to produce Level-2 science-ready data that will be made available to DKIST observers.

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arXiv2021

Prediction of Solar Proton Events with Machine Learning: Comparison with Operational Forecasts and "All-Clear" Perspectives

Solar Energetic Particle events (SEPs) are among the most dangerous transient phenomena of solar activity. As hazardous radiation, SEPs may affect the health of astronauts in outer space and adversely impact current and future space exploration. In this paper, we consider the problem of daily prediction of Solar Proton Events (SPEs) based on the characteristics of the magnetic fields in solar Active Regions (ARs), preceding soft X-ray and proton fluxes, and statistics of solar radio bursts. The machine learning (ML) algorithm uses an artificial neural network of custom architecture designed for whole-Sun input. The predictions of the ML model are compared with the SWPC NOAA operational forecasts of SPEs. Our preliminary results indicate that 1) for the AR-based predictions, it is necessary to take into account ARs at the western limb and on the far side of the Sun; 2) characteristics of the preceding proton flux represent the most valuable input for prediction; 3) daily median characteristics of ARs and the counts of type II, III, and IV radio bursts may be excluded from the forecast without performance loss; and 4) ML-based forecasts outperform SWPC NOAA forecasts in situations in which missing SPE events is very undesirable. The introduced approach indicates the possibility of developing robust "all-clear" SPE forecasts by employing machine learning methods.

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