Document Type
Poster
Publication Title
Northrop Grumman Engineering & Science Student Design Showcase
Abstract
The current growing number of discovered exoplanets via the transit method demands a need to have a quick and accurate way of performing atmospheric retrieval. • This project investigates the accuracy of neural network (NN) methods of atmosphere retrieval, involving long short-term memory (LSTM) NNs and graph neural networks (GNN) and compares it to preexisting convolutional neural network (CNN) and Bayesian retrieval methods.
Advisor
Howard Chen
Publication Date
4-25-2025
Recommended Citation
Wainscott, Timothy, "Atmospheric Retrieval using Neural Network Methods" (2025). Aerospace, Physics, and Space Science Student Publications. 62.
https://repository.fit.edu/apss_student/62
COinS