Speaker: Andrew Saydjari
Institution: Harvard University
Title: Mapping the Milky Way in n-Dimensions
Abstract: Interstellar dust is a pervasive observational challenge, but also a vital window for understanding the Galaxy. By mapping the spatial, kinematic, and chemical complexity of dust, we trace the processes that shape star formation, galactic structure, and chemical evolution. Creating these maps requires working at the intersection of statistical methods development and “big-data” astronomy, curating large photometric and spectroscopic surveys. I will describe two such intersectional efforts: 3D-dust mapping from near-infrared photometry and kinematic/chemical mapping with diffuse interstellar bands from APOGEE. Throughout, I will emphasize the power of data-driven statistical-learning techniques for disentangling this precious dust signal from contributions originating from stars or sky.
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