Using an Open Source Platform that Navteca is deploying for NASA Astrophysics, I was able to complete my degree final thesis at the Universidad complutense de Madrid, this research is an example of why NASA's initiative to Open Science is so important and can have a global impact on science students and researchers.

This project aims to be an exploratory investigation of the structural relationships of remote galaxies at redshift values ranging between 1.5 and 2.5 using the CANDELS and 3D-HST catalogs. To do so, data analysis tools with Python will be employed. Various physical properties of the galaxies will be studied, such as stellar mass, star formation rate and luminosity. In addition, visualization and statistical analysis techniques will be used to identify the most important relationships between these properties and thus gain a better understanding of the formation and evolution of remote galaxies. Data analysis will allow establishing relationships between the studied properties, which will contribute to the understanding of the formation and evolution of galaxies.

Furthermore, the use of machine learning techniques will be implemented to predict a particular physical property as a function other parameters. We demonstrate the feasibility of this approach by training models using a large dataset of galactic physical properties and validating the accuracy of the predicted property against experimental data. Our results could show that machine learning models might provide a promising avenue for predicting desired galactic properties with high accuracy and efficiency, potentially enabling new insights and applications in extragalactic astrophysics and beyond.