The history of a galaxy is encapsulated in the history of its gas cycling between the visible body of the galaxy and its harder-to-detect diffuse circumgalactic medium (CGM). We are researching methods to study the history of the CGM by developing computational and machine learning tools to use quasar absorption lines to determine the gas composition, temperature and density.
The project focuses on applying ML dimensionality reduction techniques for the dataset of simulated quasar absorption spectra.
Python, previous experience in Machine Learning.
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