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Machine Learning Application in Supersymmetry

Source Date of Publication:Oct 31,2017
10/31 2017
  • Title Machine Learning Application in Supersymmetry
  • Speaker
  • Date
  • Venue
  • Abstract

    CAS Key Laboratory of Theoretical Physics

    Institute of Theoretical Physics

    Chinese Academy of Sciences

    Lunch Seminar

    Title

    题目

    Machine Learning Application in Supersymmetry

    Speaker

    报告人

    杨金民

    Affiliation

    所在单位

    ITP

    Date

    日期

    2017年10月31日(周 二)中午12:00

    Venue

    地点

    Conference Hall 322, ITP/理论物理所322报告厅

     

    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.

    Contact Person

    所内联系人

    杨刚