Unsupervised Learning for Stellar Spectra with Deep Normalizing Flows - Jo Ciuca
Explore unsupervised learning techniques for analyzing stellar spectra using deep normalizing flows in this 28-minute conference talk by Jo Ciuca from the Australian National University. Dive into the application of astrostatistics and machine learning tools in galaxy formation and evolution, focusing on the analysis of vast datasets from Integral Field Unit surveys. Discover how data science tools can link observations with theoretical models and detect anomalous galaxies. Learn about the project overview, including the use of synthetic spectra, neural networks, and correlation matrices to find new spectral lines, identify blended lines, and detect unique objects and stars. Gain insights into the potential of these techniques for maximizing the understanding of galaxy formation physics and translating data-driven results into physical understanding.