https://solarsystem.video/videos/watch/809e8039-c120-4654-afd5-7349ca02d13a Presented by Poshan Belbase (The Catholic University of America and NASA Goddard Space Flight Center) ML4PSP Seminar August 2026 Mars possesses a hybrid magnetosphere with a localized crustal magnetic field strong enough to stand off the solar wind. This creates a unique interaction with the solar wind, which is actively being explored by the Mars Atmosphere and Volatile Evolution (MAVEN) spacecraft. The boundary between the solar wind and Mars' induced magnetosphere can give rise to the Kelvin–Helmholtz (KH) instability, which may further drive the loss of atmospheric plasma—contributing, over time, to the gradual erosion of the planet's atmosphere. This study aims to identify sparse KH instabilities using machine learning tools to better understand and analyze the Martian atmospheric loss rate attributed to these instabilities, thereby exploring the potential habitability of Mars. In this project, we developed an advanced recurrent neural network (RNN) using the Sequential flow.The Sequential flow is initially used to form the basic RNN, and subsequently, the Model class is utilized to manage multiple inputs.The model was initially trained on labeled magnetic field data to identify specific regions in Mars' atmosphere like dynamic induced magnetospheric (IM) boundary between the magneto sheath and the induced magnetosphere. Additionally, it aims to identify KH instabilities occurring in the dynamic IM boundary. The model achieved an accuracy of approximately 77% in detecting the KH regions. Further on, we plan to address the scarcity of confirmed KH events at Mars by adopting a transfer learning framework: a detection model will be pre-trained on the comparatively well-sampled KH vortices observed at Earth's magnetopause, and the learned representations will then be fine-tuned and applied to identify analogous KH signatures in the Martian induced magnetosphere. Tue, 29 Sep 2026 22:03:03 GMT https://validator.w3.org/feed/docs/rss2.html PeerTube - https://solarsystem.video https://solarsystem.video/client/assets/images/icons/icon-1500x1500.png https://solarsystem.video/videos/watch/809e8039-c120-4654-afd5-7349ca02d13a All rights reserved, unless otherwise specified in the terms specified at https://solarsystem.video/about and potential licenses granted by each content's rightholder.