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Machine Learning Application in Supersymmetry
10/31
2017
- Title Machine Learning Application in Supersymmetry
- Speaker
- Date
- Venue
Abstract
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CAS Key Laboratory of Theoretical Physics | |
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Institute of Theoretical Physics | |
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Chinese Academy of Sciences | |
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Lunch Seminar | |
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Title 题目 |
Machine Learning Application in Supersymmetry |
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Speaker 报告人 |
杨金民 |
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Affiliation 所在单位 |
ITP |
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Date 日期 |
2017年10月31日（周 二）中午12：00 |
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Venue 地点 |
Conference Hall 322, ITP/理论物理所322报告厅
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Abstract 摘要 |
Will talk about a Machine Learning approach for a fast and reliable exploration of high dimensional parameter space by using machine learning models to evaluate the quality of random parameter sets. As a proof-of-concept, this approach is applied to several benchmark models including a supersymmetry scenario. The finding is that such an approach can significantly reduce the computational cost and ensure the discovery of all survived regions. |
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Contact Person 所内联系人 |
杨刚 |