arXiv subjects

Madhulika Guhathakurta

Publications and source records attributed to Madhulika Guhathakurta.

7 recordsLinked to original sources

arXiv2026

The Solar Dynamics Observatory in the Living With a Star Era: From Solar Observations to Predictive Heliophysics

The Solar Dynamics Observatory (SDO), launched in 2010 as part of NASA's Living With a Star (LWS) program, represents a methodological transition in heliophysics: from identifying discrete solar events to characterizing the continuously evolving state of the solar atmosphere as the upstream boundary of a coupled Sun-heliosphere-geospace system. By providing co-registered, high-cadence, full-disk measurements via the Helioseismic and Magnetic Imager (HMI), Atmospheric Imaging Assembly (AIA), and Extreme Ultraviolet Variability Experiment (EVE), SDO has enabled the solar atmosphere to be treated as a dynamical system evolving in time. This capability has direct consequences for space-weather specification, ionospheric and thermospheric modeling, heliospheric transport, and the radiation environment relevant to human exploration beyond low Earth orbit. The mission's open-data policy has produced a uniform, continuously calibrated archive contributing to more than 8,400 peer-reviewed publications, enabling statistical, model-driven, and machine-learning approaches. The Space-weather HMI Active Region Patches (SHARPs) are used directly in operational flare probability forecasting and serve as the standard training dataset for machine-learning prediction systems. This perspective traces SDO's scientific lineage, infrastructure role, and the emerging applications of its archive to data-driven approaches and foundation models in the service of predictive heliophysics.

astro-ph.SR↗

arXiv2026

Connecting the Dots: A Machine Learning Ready Dataset for Ionospheric Forecasting Models

Operational forecasting of the ionosphere remains a critical space weather challenge due to sparse observations, complex coupling across geospatial layers, and a growing need for timely, accurate predictions that support Global Navigation Satellite System (GNSS), communications, aviation safety, as well as satellite operations. As part of the 2025 NASA Heliolab, we present a curated, open-access dataset that integrates diverse ionospheric and heliospheric measurements into a coherent, machine learning-ready structure, designed specifically to support next-generation forecasting models and address gaps in current operational frameworks. Our workflow integrates a large selection of data sources comprising Solar Dynamic Observatory data, solar irradiance indices (F10.7), solar wind parameters (velocity and interplanetary magnetic field), geomagnetic activity indices (Kp, AE, SYM-H), and NASA JPL's Global Ionospheric Maps of Total Electron Content (GIM-TEC). We also implement geospatially sparse data such as the TEC derived from the World-Wide GNSS Receiver Network and crowdsourced Android smartphone measurements. This novel heterogeneous dataset is temporally and spatially aligned into a single, modular data structure that supports both physical and data-driven modeling. Leveraging this dataset, we train and benchmark several spatiotemporal machine learning architectures for forecasting vertical TEC under both quiet and geomagnetically active conditions. This work presents an extensive dataset and modeling pipeline that enables exploration of not only ionospheric dynamics but also broader Sun-Earth interactions, supporting both scientific inquiry and operational forecasting efforts.

cs.LG↗

arXiv2026

Polarimeter to Unify the Corona and Heliosphere (PUNCH)

The Polarimeter to Unify the Corona and Heliosphere (PUNCH) mission is a NASA Small Explorer to determine the cross-scale processes that unify the solar corona and heliosphere. PUNCH has two science objectives: (1) understand how coronal structures become the ambient solar wind, and (2) understand the dynamic evolution of transient structures, such as coronal mass ejections, in the young solar wind. To address these objectives, PUNCH uses a constellation of four small spacecraft in Sun-synchronous low Earth orbit, to collect linearly polarized images of the K corona and young solar wind. The four spacecraft each carry one visible-light imager in a 1+3 configuration: a single Narrow Field Imager solar coronagraph captures images of the outer corona at all position angles, and at solar elongations from 1.5 degrees (6 R$_\odot$) to 8 degrees (32 R$_\odot$); and three separate Wide Field Imager heliospheric imagers together capture views of the entire inner solar system, at solar elongations from 3 degrees (12 R$_\odot$) to 45 degrees (180 R$_\odot$) from the Sun. PUNCH images include linear-polarization data, to enable inferring the three-dimensional structure of visible features without stereoscopy. The instruments are matched in wavelength passband, support overlapping instantaneous fields of view, and are operated synchronously, to act as a single ``virtual instrument'' with a 90 degree wide field of view, centered on the Sun. PUNCH launched in March of 2025 and began science operations in June of 2025. PUNCH has an open data policy with no proprietary period, and PUNCH Science Team Meetings are open to all.

astro-ph.SR↗

arXiv2025

IonCast: A Deep Learning Framework for Forecasting Ionospheric Dynamics

The ionosphere is a critical component of near-Earth space, shaping GNSS accuracy, high-frequency communications, and aviation operations. For these reasons, accurate forecasting and modeling of ionospheric variability has become increasingly relevant. To address this gap, we present IonCast, a suite of deep learning models that include a GraphCast-inspired model tailored for ionospheric dynamics. IonCast leverages spatiotemporal learning to forecast global Total Electron Content (TEC), integrating diverse physical drivers and observational datasets. Validating on held-out storm-time and quiet conditions highlights improved skill compared to persistence. By unifying heterogeneous data with scalable graph-based spatiotemporal learning, IonCast demonstrates how machine learning can augment physical understanding of ionospheric variability and advance operational space weather resilience.

cs.LG↗

arXiv2025

Forecasting the Ionosphere from Sparse GNSS Data with Temporal-Fusion Transformers

The ionosphere critically influences Global Navigation Satellite Systems (GNSS), satellite communications, and Low Earth Orbit (LEO) operations, yet accurate prediction of its variability remains challenging due to nonlinear couplings between solar, geomagnetic, and thermospheric drivers. Total Electron Content (TEC), a key ionospheric parameter, is derived from GNSS observations, but its reliable forecasting is limited by the sparse nature of global measurements and the limited accuracy of empirical models, especially during strong space weather conditions. In this work, we present a machine learning framework for ionospheric TEC forecasting that leverages Temporal Fusion Transformers (TFT) to predict sparse ionosphere data. Our approach accommodates heterogeneous input sources, including solar irradiance, geomagnetic indices, and GNSS-derived vertical TEC, and applies preprocessing and temporal alignment strategies. Experiments spanning 2010-2025 demonstrate that the model achieves robust predictions up to 24 hours ahead, with root mean square errors as low as 3.33 TECU. Results highlight that solar EUV irradiance provides the strongest predictive signals. Beyond forecasting accuracy, the framework offers interpretability through attention-based analysis, supporting both operational applications and scientific discovery. To encourage reproducibility and community-driven development, we release the full implementation as the open-source toolkit \texttt{ionopy}.

cs.LG↗

arXiv2025

Surya: Foundation Model for Heliophysics

Heliophysics is central to understanding and forecasting space weather events and solar activity. Despite decades of high-resolution observations from the Solar Dynamics Observatory (SDO), most models remain task-specific and constrained by scarce labeled data, limiting their capacity to generalize across solar phenomena. We introduce Surya, a 366M parameter foundation model for heliophysics designed to learn general-purpose solar representations from multi-instrument SDO observations, including eight Atmospheric Imaging Assembly (AIA) channels and five Helioseismic and Magnetic Imager (HMI) products. Surya employs a spatiotemporal transformer architecture with spectral gating and long--short range attention, pretrained on high-resolution solar image forecasting tasks and further optimized through autoregressive rollout tuning. Zero-shot evaluations demonstrate its ability to forecast solar dynamics and flare events, while downstream fine-tuning with parameter-efficient Low-Rank Adaptation (LoRA) shows strong performance on solar wind forecasting, active region segmentation, solar flare forecasting, and EUV spectra. Surya is the first foundation model in heliophysics that uses time advancement as a pretext task on full-resolution SDO data. Its novel architecture and performance suggest that the model is able to learn the underlying physics behind solar evolution.

astro-ph.SR↗

arXiv2021

Radiation Data Portal: Integration of Radiation Measurements at the Aviation Altitudes and Solar-Terrestrial Environment Observations

The impact of radiation dramatically increases at high altitudes in the Earth's atmosphere and in space. Therefore, monitoring and access to radiation environment measurements are critical for estimating the radiation exposure risks of aircraft and spacecraft crews and the impact of space weather disturbances on electronics. Addressing these needs requires convenient access to multi-source radiation environment data and enhancement of visualization and search capabilities. The Radiation Data Portal represents an interactive web-based application for search and visualization of in-flight radiation measurements. The Portal enhances the exploration capabilities of various properties of the radiation environment and provides measurements of dose rates along with information on space weather-related conditions. The Radiation Data Portal back-end is a MySQL relational database that contains the radiation measurements obtained from the Automated Radiation Measurements for Aerospace Safety (ARMAS) device and the soft X-ray and proton flux measurements from the Geostationary Operational Environmental Satellite (GOES). The implemented Application Programming Interface (API) and Python routines allow a user to retrieve the database records without interaction with the web interface. As a use case of the Radiation Data Portal, we examine ARMAS measurements during an enhancement of the Solar Proton (SP) fluxes, known as Solar Proton Events (SPEs), and compare them to measurements during SP-quiet periods.

physics.space-ph↗