Is this project an undergraduate, graduate, or faculty project?
Undergraduate
Project Type
group
Campus
Daytona Beach
Authors' Class Standing
Chintan Thakrar: Graduated Marie Bals: Graduate Student Jose Gachancipa Parga: Graduated Kshitija Deshpande: Faculty
Lead Presenter's Name
Chintan Thakrar
Lead Presenter's College
DB College of Arts and Sciences
Faculty Mentor Name
Kshitija Deshpande
Abstract
We are making use of the vast amount of publicly available GNSS data to develop a data driven supervised machine learning model to categorize to a predefined set of characteristic high-latitude ionospheric irregularity nightside regions. The goal of this model is to predict whether the scintillating instance of high-frequency data was recorded in the auroral oval vs. the polar cap with a high level of confidence. The source regions were determined using the SUSSI instruments onboard the DMSP satellites. The model is trained and tested with events extracted by thresholding the low-rate data from receivers from both characteristic nightside regions. As input parameters of the model serve the Power Spectral Densities (PSD) of the events since they provide context about the size and velocities of the irregularities. The model is expected to identify potential distinct characteristics of each predefined source region and in a second step to predict the region based on a given phase or amplitude time series. This will allow us to further understand of the characteristics of ionospheric irregularities and their sources.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
Yes, Spark Grant
Categorizing Characteristic Regions of Nightside High-Latitude Ionospheric Irregularities Using a Machine Learning Approach.
We are making use of the vast amount of publicly available GNSS data to develop a data driven supervised machine learning model to categorize to a predefined set of characteristic high-latitude ionospheric irregularity nightside regions. The goal of this model is to predict whether the scintillating instance of high-frequency data was recorded in the auroral oval vs. the polar cap with a high level of confidence. The source regions were determined using the SUSSI instruments onboard the DMSP satellites. The model is trained and tested with events extracted by thresholding the low-rate data from receivers from both characteristic nightside regions. As input parameters of the model serve the Power Spectral Densities (PSD) of the events since they provide context about the size and velocities of the irregularities. The model is expected to identify potential distinct characteristics of each predefined source region and in a second step to predict the region based on a given phase or amplitude time series. This will allow us to further understand of the characteristics of ionospheric irregularities and their sources.