Research

In my undergraduate thesis, I studied the SFMS across cosmic time. I used data from the JWST and HST in four observational fields. I performed the SED fitting procedure to extract physical properties of over 45 thousands star-forming galaxies. I found that SFMS normalization increases with redshift, while the slope has a valley at redshift $z~4-5$.

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In my graduate thesis, I continued to study physical processes that driven the SFMS evolution. I took a sample of 15% star-forming galaxies from my undergraduate work. To study the physical processes, I perfomed the spatially resolved SED fitting. Then, I analyze the galactic radial profiles at $2<z<8$. It found that the SFMS evolution may be driven by the inside-out growth theory.

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For my college assignment, I developed a machine learning model to classify galaxy morphology. I used a Convolutional Neural Network for the machine learning model. To train and test the model, I used images of 21785 galaxies from the Galaxy10 SDSS dataset. The dataset was divided into 10 morphological classes. To prevent overfitting, the data was augmented by rotating (±90◦), resizing, and flipping. This machine model learning achieved up to 74% accuracy.