Dark matter is one of the greatest enduring mysteries of fundamental physics. Despite countless direct and indirect searches for dark matter, still, the only evidence we have for it is through its gravitational effects on astrophysical and cosmological scales. In this talk, I will describe a new astrophysical technique to measure the local dark matter distribution that my collaborators and I have developed. Our technique combines modern machine learning with exquisite data on stellar kinematics from the Gaia Space Telescope and allows us to "see" the local dark matter in 3d for the first time. Along the way, our new technique also produces 3d maps of the local Galactic gravitational potential, acceleration field, and interstellar dust extinction, which are interesting in their own right and provide vital information into the structure and dynamics of the Galaxy.
Biography:
Professor Shih is in the Department of Physics & Astronomy at Rutgers University and a member of the New High Energy Theory Center (NHETC). His primary research focus is theoretical particle physics: understanding the nature of the Universe at the most fundamental level. Currently, he is working on applications of machine learning and artificial intelligence to data from both colliders and astronomy. This will help us better understand long-standing questions in fundamental physics, such as “What is the dark matter?” and “Where is the new physics?”
In the past, he has also worked on a diverse array of topics, from string theory and quantum gravity to model building and phenomenology. (Source)
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