CCAPP Seminar: "Unraveling the History of the Milky Way" Yuan-Sen Ting (Harvard)

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September 13, 2016
11:30AM - 12:30PM
Location
4138 PRB

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Add to Calendar 2016-09-13 11:30:00 2016-09-13 12:30:00 CCAPP Seminar: "Unraveling the History of the Milky Way" Yuan-Sen Ting (Harvard) Understanding physical processes responsible for the formation and evolution of galaxies like the Milky Way is a fundamental problem in astrophysics. However, a key challenge is that the properties and orbits of the stars can only be observed at present: in order to understand what happened in the Milky Way at earlier epochs, one must explore “archeological” techniques. One idea, "chemical tagging”, aims to probe the history of the Milky Way via the unique imprint in chemical abundance space of long-disrupted star clusters. I will discuss the opportunities and challenges associated with chemical tagging, including a first constraint on the disrupted cluster mass function in the Milky Way. I will also describe a new set of tools for efficient fitting large quantities of stellar spectra and opportunities for extracting many stellar parameters from low-resolution data. 4138 PRB Center for Cosmology and AstroParticle Physics (CCAPP) ccapp@osu.edu America/New_York public
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Understanding physical processes responsible for the formation and evolution of galaxies like the Milky Way is a fundamental problem in astrophysics. However, a key challenge is that the properties and orbits of the stars can only be observed at present: in order to understand what happened in the Milky Way at earlier epochs, one must explore “archeological” techniques. One idea, "chemical tagging”, aims to probe the history of the Milky Way via the unique imprint in chemical abundance space of long-disrupted star clusters. I will discuss the opportunities and challenges associated with chemical tagging, including a first constraint on the disrupted cluster mass function in the Milky Way. I will also describe a new set of tools for efficient fitting large quantities of stellar spectra and opportunities for extracting many stellar parameters from low-resolution data.

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