5 papers
CORONA-Fields: Leveraging Foundation Models for Classification of Solar Wind Phenomena
Daniela Martin, Jinsu Hong, Connor O'Brien +4
Space weather at Earth, driven by the solar activity, poses growing risks to satellites around our planet as well as to critical ground-based technological infrastructure. Major sp…
Towards Reliable Sea Ice Drift Estimation in the Arctic Deep Learning Optical Flow on RADARSAT-2
Daniela Martin, Joseph Gallego
Accurate estimation of sea ice drift is critical for Arctic navigation, climate research, and operational forecasting. While optical flow, a computer vision technique for estimatin…
Uncovering Solar Wind Phenomena with iSAX, HDBSCAN, Human-in-the-loop and PSP Observations
Valmir P Moraes Filho, Daniela Martin, Jasmine R. Kobayashi +4
The solar wind is a dynamic plasma outflow that shapes heliospheric conditions and drives space weather. Identifying its large-scale phenomena is crucial, yet the increasing volume…
Scalable Machine Learning Analysis of Parker Solar Probe Solar Wind Data
Daniela Martin, Connor O'Brien, Valmir P Moraes Filho +4
We present a scalable machine learning framework for analyzing Parker Solar Probe (PSP) solar wind data using distributed processing and the quantum-inspired Kernel Density Matrice…
CIPHER: Scalable Time Series Analysis for Physical Sciences with Application to Solar Wind Phenomena
Jasmine R. Kobayashi, Daniela Martin, Valmir P Moraes Filho +12
Labeling or classifying time series is a persistent challenge in the physical sciences, where expert annotations are scarce, costly, and often inconsistent. Yet robust labeling is…