From 1913d99118daee70396a026388a7bc6fc545cfd4 Mon Sep 17 00:00:00 2001 From: Emanuele Usai Date: Wed, 13 Mar 2024 11:12:16 -0500 Subject: [PATCH] Delete _gsocproposals/2024/proposal_PROSPECTOR.md --- _gsocproposals/2024/proposal_PROSPECTOR.md | 48 ---------------------- 1 file changed, 48 deletions(-) delete mode 100644 _gsocproposals/2024/proposal_PROSPECTOR.md diff --git a/_gsocproposals/2024/proposal_PROSPECTOR.md b/_gsocproposals/2024/proposal_PROSPECTOR.md deleted file mode 100644 index b862c680..00000000 --- a/_gsocproposals/2024/proposal_PROSPECTOR.md +++ /dev/null @@ -1,48 +0,0 @@ ---- -title: Deriving planetary surface composition from orbiting observations from spacecraft -layout: gsoc_proposal -project: Lunar Prospector -year: 2023 -organization: - - Alabama - - Goddard - - JHUAPL ---- - -## Description - -NASA has sent many robotic spacecraft to perform remote-sensing observations of surface composition from orbit. This includes gamma-ray observations of planetary surfaces, which provide position-dependent energy spectra whose shape is due to the sum of components from different elements. We seek to develop a machine learning (ML) approach to isolate the element-dependent contributions to the measurements as a function of surface location. We will use the Lunar Prospector Gamma-Ray Spectrometer dataset to train a model using the moon as the training dataset. The first objective is to identify the best ML model approach and quantify its accuracy as this data, and the Moon, are well understood. We will then use domain adaptation to extend the approach to other planetary objects that are less well known, enabling new discoveries. - - -## Duration - -Total project length: 175/350 hours. - -## Task ideas - * Identify the best ML model to extract element composition information from cumulative gamma-ray spectra using Lunar Prospector measurements of the Moon. - * Use domain adaptation to extend the model to other planetary surfaces. - -## Expected results - * Identification of a suitable model with a proof-of-concept using Lunar Prospector data. - - -## Test -Please use [this link](https://ml4sci.org/assets/GSOC_2023_Evaluation_Test.pdf) to access the test for this project. - -## Requirements -Python and relevant past experience in Machine Learning. - - - - -## Mentors - - * [Patrick Peplowski](mailto:ml4-sci@cern.ch) (JHUAPL) - * [Sergei Gleyzer](mailto:ml4-sci@cern.ch) (University of Alabama) - * [Mauricio Allyon-Unzueta](mailto:ml4-sci@cern.ch) (NASA Goddard) - - -Please **DO NOT** contact mentors directly by email. Instead, please email [ml4-sci@cern.ch](mailto:ml4-sci@cern.ch) with Project Title and **include your CV** and **test results**. The mentors will then get in touch with you. - -## Links - * [Paper 1](https://agupubs.onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2005JE002656)