Presentation #241.38 in the session Evolution of Galaxies — iPoster Session.
The currently accepted model of galaxy evolution defines two regimes of galaxies: star-forming and quiescent. However, using a novel technique called IMF fitting to estimate the temperatures of galaxies, we actually find two distinct regimes of star-forming galaxies, raising the total number of regimes of galaxies to three. This is because gas temperatures in star-forming galaxies exhibit two distinct behaviors at lower masses in the initial stages of growth and at higher masses in the quiescent stages. We motivate the technique of dimensionality reduction, an unsupervised machine learning technique used to discover and visualize patterns in a dataset by reducing its number of dimensions, or “features.” We will show that by applying dimensionality reduction to a dataset of galaxies, we can provide evidence for an updated model of galaxy evolution. We chose to study the roughly one million galaxies in the COSMOS dataset, each of which has flux measurements in twenty-seven photometric bands, making COSMOS a twenty-seven-dimensional dataset. First, we preprocessed the data by removing galaxies with incomplete or erroneous measurements and normalized the photometric measurements to a single photometric band. We then applied the t-SNE dimensionality reduction algorithm to the preprocessed dataset, determining appropriate values for t-SNE’s parameters. Observing the resulting two-dimensional maps, we found that t-SNE clustered the galaxies together by regime and that the three clusters representing the three regimes were ordered sequentially on the map. We conclude that the t-SNE maps illustrate and provide evidence for our three-regime model of galaxy evolution, where a hot, star-forming galaxy cools into the Star-Forming Main Sequence, which then transitions into a quiescent galaxy when its blue, high-mass stars die out and star formation ceases.