RUI: Pion Lagrangian for Large Scale Structure in Cosmology
RUI: Pion Lagrangian for Large Scale Structure in Cosmology
批准号:
2210475
负责人:
Bartholomew Horn
金额:
$13.5万
依托单位:
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2022
资助国家:
美国
项目状态:
未结题
起止时间:
2022-08-15 至 2025-07-31
中文摘要
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英文摘要
This RUI award funds the research activities of Professor Bart Horn at Manhattan College.Surveys of large-scale structure in the Universe are poised to transform the field of cosmology over the next five to ten years. By measuring the distribution of galaxies and matter and their evolution over time, physicists hope to measure parameters of primordial cosmology and to probe the age, composition, and early history of the Universe to unprecedented levels of precision. As part of his research, Professor Horn will develop some of the new computational tools that will be required to analyze these large data sets. In addition to supporting the progress of scientific research, this work will further the national interest by helping to introduce a new generation of researchers to the field. This project will also create multiple opportunities for undergraduate students to participate in cutting-edge research. Moreover, as an additional broader impact, this program will also support outreach to local high schools and other venues in New York City.More specifically, Professor Horn will apply effective field theory techniques from high-energy theoretical physics to modeling the growth and evolution of large-scale structure in the Universe, using an effective pion Lagrangian description for the gravitational collapse of matter in order to connect theoretical inputs and experimental observables well into the nonlinear regime. This description helps keep the symmetries manifest, similar to the role symmetries have played in field theories of nuclear and particle physics, and this description may suggest new ways of connecting the inputs of primordial physics to experimental observables of large-scale structure. These methods will be compared against experimental data and N-body simulations, which are computationally expensive despite their accuracy, and it is expected that the additional efficiency obtained will help make the field more accessible to a new generation of researchers.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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