arXiv subjects

Subhamoy Chatterjee

Publications and source records attributed to Subhamoy Chatterjee.

At least 19 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

A Physics Informed Neural Network For Deriving MHD State Vectors From Global Active Regions Observations

Solar active regions (ARs) do not appear randomly but cluster along longitudinally warped toroidal bands ('toroids') that encode information about magnetic structures in the tachocline, where global-scale organization likely originates. Global MagnetoHydroDynamic Shallow-Water Tachocline (MHD-SWT) models have shown potential to simulate such toroids, matching observations qualitatively. For week-scale early prediction of flare-producing AR emergence, forward-integration of these toroids is necessary. This requires model initialization with a dynamically self-consistent MHD state-vector that includes magnetic, flow fields, and shell-thickness variations. However, synoptic magnetograms provide only geometric shape of toroids, not the state-vector needed to initialize MHD-SWT models. To address this challenging task, we develop PINNBARDS, a novel Physics-Informed Neural Network (PINN)-Based AR Distribution Simulator, that uses observational toroids and MHD-SWT equations to derive initial state-vector. Using Feb-14-2024 SDO/HMI synoptic map, we show that PINN converges to physically consistent, predominantly antisymmetric toroids, matching observed ones. Although surface data provides north and south toroids' central latitudes, and their latitudinal widths, they cannot determine tachocline field strengths, connected to AR emergence. We explore here solutions across a broad parameter range, finding hydrodynamically-dominated structures for weak fields (~2 kG) and overly rigid behavior for strong fields (~100 kG). We obtain best agreement with observations for 20-30 kG toroidal fields, and ~10 degree bandwidth, consistent with low-order longitudinal mode excitation. To our knowledge, PINNBARDS serves as the first method for reconstructing state-vectors for hidden tachocline magnetic structures from surface patterns; potentially leading to weeks ahead prediction of flare-producing AR-emergence.

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arXiv2025

Machine Learning Based Identification of Solar Disk and Plages in Kodaikanal Solar Observatory Historical Suncharts

Kodaikanal Solar Observatory (KoSO) is one of the oldest solar observatories, possessing an archive of multi-wavelength solar observations, including white light, Ca II K, and H-alpha images spanning over a century. In addition to these observations, KoSO has preserved hand-drawn suncharts (1904-2022), on which various solar features such as sunspots, plages, filaments, and prominences are marked on the Stonyhurst grid with distinct colour coding. In this study, we present the first comprehensive result that includes the entire data set from these suncharts using a supervised Machine Learning model called "Convolutional Neural Networks (CNNs)", firstly to identify the solar disks from the charts (1909-2007), secondly to identify the plages, spanning 9 solar cycles (1916-2007). We train the CNN with the manually identified solar disk and plage. We first detect the solar limb and the North-South line in the suncharts, which enables the extraction of disk centre coordinates, radius, and P-angle. Following that, we use a CNN similar architecture to achieve accurate image segmentation for the identification of plages. We compare plage areas derived from the suncharts with those obtained from Ca II K full-disk observations, and find good agreement that demonstrates the potential application of such an ML technique for historical data. The results of this study further demonstrate the potential application of sunchart data to fill the existing data gaps in the KoSO multi-wavelength observations and contribute toward constructing a composite series over the last century.

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arXiv2025

Polar Filaments Capture High Latitude Solar Poloidal Field Interactions and can Foretell the Future Sunspot Cycle Amplitude before Polar Field Precursors

Polar fields at the minimum of a sunspot cycle -- which are a manifestation of the radial component of the Sun's poloidal field -- are deemed to be the best indicator of the strength of the toroidal component, and hence the amplitude of the future sunspot cycle. However, the Sun's polar magnetic fields are difficult to constrain with ground-based or space-based observations from near the plane-of-ecliptic. In this context, polar filaments -- dark, elongated structures that overlie polarity inversion lines -- are known to offer critical insights into solar polar field dynamics. Through investigations of the long-term evolution of polar filament areas and length acquired from the Meudon Observatory and complimentary solar surface flux transport simulations, here we establish the common physical foundation connecting the Babcock-Leighton solar dynamo mechanism of solar polar field reversal and build-up with the origin and evolution of polar filaments. We discover a new relationship connecting the residual filament area of adjacent solar cycles with the amplitude of the next sunspot cycle -- which can serve as a new tool for solar cycle forecasts -- advancing the forecast window to earlier than polar field based precursors. We conclude that polar filament properties encapsulate the physics of interaction of the poloidal magnetic field of the previous and current sunspot cycles, the resultant of which is the net poloidal magnetic field at the end of the current cycle, thus encoding as a precursor the strength of the upcoming solar cycle.

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arXiv2025

A Machine Learning-Ready Data Processing Tool for Near Real-Time Forecasting

Space weather forecasting is critical for mitigating radiation risks in space exploration and protecting Earth-based technologies from geomagnetic disturbances. This paper presents the development of a Machine Learning (ML)- ready data processing tool for Near Real-Time (NRT) space weather forecasting. By merging data from diverse NRT sources such as solar imagery, magnetic field measurements, and energetic particle fluxes, the tool addresses key gaps in current space weather prediction capabilities. The tool processes and structures the data for machine learning models, focusing on time-series forecasting and event detection for extreme solar events. It provides users with a framework to download, process, and label data for ML applications, streamlining the workflow for improved NRT space weather forecasting and scientific research.

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arXiv2025

Deep Generative model that uses physical quantities to generate and retrieve solar magnetic active regions

Deep generative models have shown immense potential in generating unseen data that has properties of real data. These models learn complex data-generating distributions starting from a smaller set of latent dimensions. However, generative models have encountered great skepticism in scientific domains due to the disconnection between generative latent vectors and scientifically relevant quantities. In this study, we integrate three types of machine learning models to generate solar magnetic patches in a physically interpretable manner and use those as a query to find matching patches in real observations. We use the magnetic field measurements from Space-weather HMI Active Region Patches (SHARPs) to train a Generative Adversarial Network (GAN). We connect the physical properties of GAN-generated images with their latent vectors to train Support Vector Machines (SVMs) that do mapping between physical and latent spaces. These produce directions in the GAN latent space along which known physical parameters of the SHARPs change. We train a self-supervised learner (SSL) to make queries with generated images and find matches from real data. We find that the GAN-SVM combination enables users to produce high-quality patches that change smoothly only with a prescribed physical quantity, making generative models physically interpretable. We also show that GAN outputs can be used to retrieve real data that shares the same physical properties as the generated query. This elevates Generative Artificial Intelligence (AI) from a means-to-produce artificial data to a novel tool for scientific data interrogation, supporting its applicability beyond the domain of heliophysics.

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arXiv2025

Exploring the variation in the dynamic rotation profile of the hotter solar atmosphere using mutliwavelength data

The global rotational profile of the solar atmosphere and its variation at different layers, although crucial for a comprehensive understanding of the dynamics of the solar magnetic field, has been a subject to contradictory results throughout the past century. In this study, we thereby unify the results for different parts of the multi-thermal Solar atmosphere by utilizing 13 years of data in 7 wavelength channels of the Atmospheric Imaging Assembly (AIA) atop the Solar Dynamic Observatory (SDO). Using the method of image correlation, we find that the solar atmosphere exhibits a rotational profile that is up to 4.18% and 1.92% faster at the equator and comparatively less differential than that of the photosphere, as derived from Doppler measurements and sunspots, respectively and exhibits variation at different respective heights. Additionally, we find results suggestive of the role played by the rooting of different magnetic field structures on a comparison with helioseismology data.

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arXiv2025

The Solar Ultraviolet Imaging Telescope on board Aditya-L1

The Solar Ultraviolet Imaging Telescope (SUIT) is an instrument on the Aditya-L1 mission of the Indian Space Research Organization (ISRO) launched on September 02, 2023. SUIT continuously provides, near-simultaneous full-disk and region-of-interest images of the Sun, slicing through the photosphere and chromosphere and covering a field of view up to 1.5 solar radii. For this purpose, SUIT uses 11 filters tuned at different wavelengths in the 200{--}400~nm range, including the Mg~{\sc ii} h~and~k and Ca~{\sc ii}~H spectral lines. The observations made by SUIT help us understand the magnetic coupling of the lower and middle solar atmosphere. In addition, for the first time, it allows the measurements of spatially resolved solar broad-band radiation in the near and mid ultraviolet, which will help constrain the variability of the solar ultraviolet irradiance in a wavelength range that is central for the chemistry of the Earth's atmosphere. This paper discusses the details of the instrument and data products.

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arXiv2024

Exploring the dynamic rotational profile of the hotter solar atmosphere: A multi-wavelength approach using SDO/AIA data

Understanding the global rotational profile of the solar atmosphere and its variation is fundamental to uncovering a comprehensive understanding of the dynamics of the solar magnetic field and the extent of coupling between different layers of the Sun. In this study, we employ the method of image correlation to analyze the extensive dataset provided by the Atmospheric Imaging Assembly of the Solar Dynamic Observatory in different wavelength channels. We find a significant increase in the equatorial rotational rate ($A$) and a decrease in absolute latitudinal gradient ($|B|$) at all temperatures representative of the solar atmosphere, implying an equatorial rotation up to $4.18\%$ and $1.92\%$ faster and less differential when compared to the rotation rates for the underlying photosphere derived from Doppler measurement and sunspots respectively. In addition, we also find a significant increase in equatorial rotation rate ($A$) and a decrease in differential nature ($|B|$ decreases) at different layers of the solar atmosphere. We also explore a possible connection from the solar interior to the atmosphere and interestingly found that $A$ at $r=0.94\,\mathrm{R}_{\odot}, 0.965\,\mathrm{R}_{\odot}$ show an excellent match with 171 Angstrom, 304 Angstrom and 1600 Angstrom, respectively. Furthermore, we observe a positive correlation between the rotational parameters measured from 1600 Angstrom, 131 Angstrom, 193 Angstrom and 211 Angstrom with the yearly averaged sunspot number, suggesting a potential dependence of the solar rotation on the appearance of magnetic structures related to the solar cycle or the presence of cycle dependence of solar rotation in the solar atmosphere.

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arXiv2023

Differential Rotation of the Solar Chromosphere: A Century-long Perspective from Kodaikanal Solar Observatory Ca II K Data

Chromospheric differential rotation is a key component in comprehending the atmospheric coupling between the chromosphere and the photosphere at different phases of the solar cycle. In this study, we therefore utilize the newly calibrated multidecadal Ca II K spectroheliograms (1907-2007) from the Kodaikanal Solar Observatory (KoSO) to investigate the differential rotation of the solar chromosphere using the technique of image cross-correlation. Our analysis yields the chromospheric differential rotation rate $\Omega (\theta) = (14.61\pm 0.04 - 2.18\pm 0.37\sin^2{\theta} - 1.10 \pm 0.61\sin^4{\theta})^\circ{\rm /day}$. These results suggest the chromospheric plages exhibit an equatorial rotation rate 1.59% faster than the photosphere when compared with the differential rotation rate measured using sunspots and also a smaller latitudinal gradient compared to the same. To compare our results to those from other observatories, we have applied our method on a small sample of Ca II K data from Rome, Meudon, and Mt. Wilson observatories, which support our findings from KoSO data. Additionally, we have not found any significant north-south asymmetry or any systematic variation in chromospheric differential rotation over the last century.

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arXiv2023

Differential Rotation of the Solar Chromosphere using multidecadal Ca II K Spectroheliograms

The study of the differential rotation in the chromosphere of the Sun is of significant importance as it provides valuable insights into the rotational behaviour of the solar atmosphere at higher altitudes and the coupling mechanism between the various layers of the solar atmosphere. In this work, we employed the image correlation technique, explicitly focusing on plages, intending to estimate the chromospheric differential rotation. For this purpose, we have utilized Ca II K spectroheliograms (1907-2007) from the Kodaikanal Solar Observatory (KoSO), recently calibrated with a better technique to ensure accuracy. Our analysis indicates that plages in the chromosphere exhibit faster rotation and a smaller latitudinal gradient when compared to the rotation rate obtained through sunspot tracking. Furthermore, we investigate the temporal analysis of the chromospheric differential rotation parameters across various solar cycles.

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arXiv2023

MEMPSEP III. A machine learning-oriented multivariate data set for forecasting the Occurrence and Properties of Solar Energetic Particle Events using a Multivariate Ensemble Approach

We introduce a new multivariate data set that utilizes multiple spacecraft collecting in-situ and remote sensing heliospheric measurements shown to be linked to physical processes responsible for generating solar energetic particles (SEPs). Using the Geostationary Operational Environmental Satellites (GOES) flare event list from Solar Cycle (SC) 23 and part of SC 24 (1998-2013), we identify 252 solar events (flares) that produce SEPs and 17,542 events that do not. For each identified event, we acquire the local plasma properties at 1 au, such as energetic proton and electron data, upstream solar wind conditions, and the interplanetary magnetic field vector quantities using various instruments onboard GOES and the Advanced Composition Explorer (ACE) spacecraft. We also collect remote sensing data from instruments onboard the Solar Dynamic Observatory (SDO), Solar and Heliospheric Observatory (SoHO), and the Wind solar radio instrument WAVES. The data set is designed to allow for variations of the inputs and feature sets for machine learning (ML) in heliophysics and has a specific purpose for forecasting the occurrence of SEP events and their subsequent properties. This paper describes a dataset created from multiple publicly available observation sources that is validated, cleaned, and carefully curated for our machine-learning pipeline. The dataset has been used to drive the newly-developed Multivariate Ensemble of Models for Probabilistic Forecast of Solar Energetic Particles (MEMPSEP; see MEMPSEP I (Chatterjee et al., 2023) and MEMPSEP II (Dayeh et al., 2023) for associated papers).

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arXiv2023

MEMPSEP I : Forecasting the Probability of Solar Energetic Particle Event Occurrence using a Multivariate Ensemble of Convolutional Neural Networks

The Sun continuously affects the interplanetary environment through a host of interconnected and dynamic physical processes. Solar flares, Coronal Mass Ejections (CMEs), and Solar Energetic Particles (SEPs) are among the key drivers of space weather in the near-Earth environment and beyond. While some CMEs and flares are associated with intense SEPs, some show little to no SEP association. To date, robust long-term (hours-days) forecasting of SEP occurrence and associated properties (e.g., onset, peak intensities) does not effectively exist and the search for such development continues. Through an Operations-2-Research support, we developed a self-contained model that utilizes a comprehensive dataset and provides a probabilistic forecast for SEP event occurrence and its properties. The model is named Multivariate Ensemble of Models for Probabilistic Forecast of Solar Energetic Particles (MEMPSEP). MEMPSEP workhorse is an ensemble of Convolutional Neural Networks that ingests a comprehensive dataset (MEMPSEP III - (Moreland et al., 2023)) of full-disc magnetogram-sequences and in-situ data from different sources to forecast the occurrence (MEMPSEP I - this work) and properties (MEMPSEP II - Dayeh et al. (2023)) of a SEP event. This work focuses on estimating true SEP occurrence probabilities achieving a 2.5% improvement in reliability and a Brier score of 0.14. The outcome provides flexibility for the end-users to determine their own acceptable level of risk, rather than imposing a detection threshold that optimizes an arbitrary binary classification metric. Furthermore, the model-ensemble, trained to utilize the large class-imbalance between events and non-events, provides a clear measure of uncertainty in our forecast

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arXiv2023

MEMPSEP II. -- Forecasting the Properties of Solar Energetic Particle Events using a Multivariate Ensemble Approach

Solar Energetic Particles (SEPs) form a critical component of Space Weather. The complex, intertwined dynamics of SEP sources, acceleration, and transport make their forecasting very challenging. Yet, information about SEP arrival and their properties (e.g., peak flux) is crucial for space exploration on many fronts. We have recently introduced a novel probabilistic ensemble model called the Multivariate Ensemble of Models for Probabilistic Forecast of Solar Energetic Particles (MEMPSEP). Its primary aim is to forecast the occurrence and physical properties of SEPs. The occurrence forecasting, thoroughly discussed in a preceding paper (Chatterjee et al., 2023), is complemented by the work presented here, which focuses on forecasting the physical properties of SEPs. The MEMPSEP model relies on an ensemble of Convolutional Neural Networks, which leverage a multi-variate dataset comprising full-disc magnetogram sequences and numerous derived and in-situ data from various sources. Skill scores demonstrate that MEMPSEP exhibits improved predictions on SEP properties for the test set data with SEP occurrence probability above 50%, compared to those with a probability below 50%. Results present a promising approach to address the challenging task of forecasting SEP physical properties, thus improving our forecasting capabilities and advancing our understanding of the dominant parameters and processes that govern SEP production.

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arXiv2023

Probabilistic solar flare forecasting using historical magnetogram data

Solar flare forecasting research using machine learning (ML) has focused on high resolution magnetogram data from the SDO/HMI era covering Solar Cycle 24 and the start of Solar Cycle 25, with some efforts looking back to SOHO/MDI for data from Solar Cycle 23. In this paper, we consider over 4 solar cycles of daily historical magnetogram data from multiple instruments. This is the first attempt to take advantage of this historical data for ML-based flare forecasting. We apply a convolutional neural network (CNN) to extract features from full-disk magnetograms together with a logistic regression model to incorporate scalar features based on magnetograms and flaring history. We use an ensemble approach to generate calibrated probabilistic forecasts of M-class or larger flares in the next 24 hours. Overall, we find that including historical data improves forecasting skill and reliability. We show that single frame magnetograms do not contain significantly more relevant information than can be summarized in a small number of scalar features, and that flaring history has greater predictive power than our CNN-extracted features. This indicates the importance of including temporal information in flare forecasting models.

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arXiv2023

Efficient labeling of solar flux evolution videos by a deep learning model

Machine learning (ML) is becoming a critical tool for interrogation of large complex data. Labeling, defined as the process of adding meaningful annotations, is a crucial step of supervised ML. However, labeling datasets is time consuming. Here we show that convolutional neural networks (CNNs), trained on crudely labeled astronomical videos, can be leveraged to improve the quality of data labeling and reduce the need for human intervention. We use videos of the solar magnetic field, crudely labeled into two classes: emergence or non-emergence of bipolar magnetic regions (BMRs), based on their first detection on the solar disk. We train CNNs using crude labels, manually verify, correct labeling vs. CNN disagreements, and repeat this process until convergence. Traditionally, flux emergence labelling is done manually. We find that a high-quality labeled dataset, derived through this iterative process, reduces the necessary manual verification by 50%. Furthermore, by gradually masking the videos and looking for maximum change in CNN inference, we locate BMR emergence time without retraining the CNN. This demonstrates the versatility of CNNs for simplifying the challenging task of labeling complex dynamic events.

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arXiv2023

Homogenising SoHO/EIT and SDO/AIA 171\AA$~$ Images: A Deep Learning Approach

Extreme Ultraviolet images of the Sun are becoming an integral part of space weather prediction tasks. However, having different surveys requires the development of instrument-specific prediction algorithms. As an alternative, it is possible to combine multiple surveys to create a homogeneous dataset. In this study, we utilize the temporal overlap of SoHO/EIT and SDO/AIA 171~\AA ~surveys to train an ensemble of deep learning models for creating a single homogeneous survey of EUV images for 2 solar cycles. Prior applications of deep learning have focused on validating the homogeneity of the output while overlooking the systematic estimation of uncertainty. We use an approach called `Approximate Bayesian Ensembling' to generate an ensemble of models whose uncertainty mimics that of a fully Bayesian neural network at a fraction of the cost. We find that ensemble uncertainty goes down as the training set size increases. Additionally, we show that the model ensemble adds immense value to the prediction by showing higher uncertainty in test data that are not well represented in the training data.

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arXiv2022

Detection of Solar Filaments using Suncharts from Kodaikanal Solar Observatory Archive Employing a Clustering Approach

With over 100 years of solar observations, the Kodaikanal Solar Observatory (KoSO) is a one-of-a-kind solar data repository in the world. Among its many data catalogues, the `suncharts' at KoSO are of particular interest. These Suncharts (1904-2020) are coloured drawings of different solar features, such as sunspots, plages, filaments, and prominences, made on papers with a Stonyhurst latitude-longitude grid etched on them. In this paper, we analyze this unique data by first digitizing each suncharts using an industry-standard scanner and saving those digital images in high-resolution `.tif' format. We then examine the Cycle~19 and Cycle~20 data (two of the strongest cycles of the last century) with the aim of detecting filaments. To this end, we employed `k-means clustering' method and obtained different filament parameters such as position, tilt angle, length, and area. Our results show that filament length (and area) increases with latitude and the pole-ward migration is clearly dominated by a particular tilt sign. Lastly, we cross-verified our findings with results from KoSO digitized photographic plate database for the overlapping time period and obtained a good agreement between them. This work, acting as a proof-of-the-concept, will kick-start new efforts to effectively use the entire hand-drawn series of multi-feature, full-disk solar data and enable researchers to extract new sciences, such as the generation of pseudo magnetograms for the last 100 years.

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