CHESS: Coronal Hole Extraction with Semantic Segmentation
This project aims at expanding the training of two Convolutional Neural Networks (CNNs) that we have already developed to obtain a more efficient, more accurate, and least-biased…
Machine learning for X-ray astronomical spectroscopy
We use Simulation Based Inference to construct 34000 artificial spectra that are representative of observed Active Galactic Nuclei X-ray spectra with NASA's NuSTAR X-ray telescope…
Using MML code generation to create new high-energy astrophysics science software
We plan to train an LLM on previous large code bases for high-energy astrophysics science pipeline and analysis software. HEASARC is under a mandate to work on next-generation sc…
Diffusion Modeling of the Solar Corona
Diffusion models such as DeepMind’s GenCast have demonstrated powerful performance in terrestrial weather forecasting, achieving results on-par and surpassing leading medium-range…
Data-Mining Similar Scenarios for Uncertainty Quantification of Solar Wind Predictions at L1
Accurate Uncertainty Quantification (UQ) for space weather forecasts is an ever-important supplementary variable to enable accurate risk response. Modeling uncertainty is itself a…
Flarenet
Developing a CNN model to identify stellar flares in the 20-second cadence TESS data product.
Uniting Physics and Machine Learning for Enhanced Heliophysics Insights
The Solar Neutron TRACking (SONTRAC) instrument is designed to detect incident solar neutrons in an energy range that fills a key gap in understanding flare ion acceleration. SONT…
Cloud translator for large-scale models
Simulates frequency of occurrence of cloud types as seen from a space-based imager from radiative fluxes.
AI/ML for Anomaly Detection and Health Monitoring for the NSN
The project develops ML/AI methods/tools to enhance the operation and sustainment of GSFC Space Network (ACCESS-managed) assets. We analyze telemetry from TDRS and STPSat-6 to ass…
Solar Neutron Tracking Spectrometer (SONTRAC)
To develop new instrumentation that will be capable of measuring winds in planetary atmospheres.
Text-to-Spaceship
Text-to-Spaceship is a NASA-led initiative exploring how artificial intelligence can transform mission development. The approach transforms traditional slow, linear development in…
Creating U-Net + LSTM Hybrid Architecture for Image Series Analysis
This work focuses on creating U-Net + long short-term memory Hybrid analysis architectures for image series analysis on solar image data.
Roman Space Telecope WFI Pupil alignment verification
Using Machine Learning (ML) to determine the pupil alignment of the Wide Field Instrument of the Roman Space Telescope during the spacecraft testing in thermal vacuum conditions.…
A hybrid machine learning approach for calibration and regionalization of LSM soil and vegetation parameters
This work explores the combined use of machine learning and traditional model calibration methods to develop a high resolution (1 km) soil and vegetation parameter dataset for Nor…
Mapping anthropogenic water cycle impacts in a future climate: A global digital twin for scenario-driven exploration
This project develops an emergent constraint emulator for future changes in water storage estimates based on the available historical record of GRACE and GRACE-FO measurements.
Nominal Forecast Sub System for the NASA Coastal Zone Digital Twin
Development of ML based emulator for enabling what-now, what-next, and what-if analysis for flood and water quality indicators in the Chesapeake Bay, trained using Land Informatio…
AI assisted Image recognition for EEE parts kitting and auditing
Due to the limits in personnel capacity, warehouse inventory contract (TRAX), and the limits in the Goddard Material Management System (MMS), EEE parts kitting and auditing is lab…
Development of a next-generation snow and ice product (SNIP) for operational applications
This project aims to develop interpretable AI/ML models to improve global snow depth retrieval from AMSR2 brightness temperature observations.
Simultaneous emulation and downscaling of modeled soil state variables with machine learning
We propose a lightweight, computationally efficient machine learning (ML) model capable of emulating the LIS-based soil moisture and soil temperature and downscaling them from a n…
Hydrology Copilot
We are building a multi-agent AI copilot that lets users explore and apply the new 1-km, hourly North American Land Data Assimilation System (NLDAS) version 3 dataset through natu…
Developing high-resolution multidecadal satellite remote sensing-based snow lifecycle reanalysis products over the Northern Hemisphere
A deep learning model is being trained using a subset of meteorological forcings and remote sensing observations of snow cover to reconstruct seasonal snow water content globally.…
Distributed Spacecraft Autonomy
Distributed Spacecraft Autonomy (DSA) is a project developed by the National Aeronautics and Space Administration that enables distributed spacecraft systems through the developme…
GLOBE Program - Image processing
Processing photo images submitted to the GLOBE Program through the GLOBE Observer app. These photos relate to submissions of Clouds, Mosquito Habitat Mapper, Trees, and Land Cover…
ML-Driven Wide Field Instrument (WFI) Image Calibration
This machine learning-driven project aims to expedite the image calibration process for Roman Wide Field Instrument (WFI) data by developing an automated calibration system. Lever…