Sung Hak Lim, Mihoko Nojiri (JHEP, 2018) Amit - PowerPoint PPT Presentation

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Sung Hak Lim, Mihoko Nojiri (JHEP, 2018) Amit Chakraborty, Sung Hak Lim, Mihoko Nojiri ( arXiv 1904.02092 ) 1 LHC

解釈可能な機械学習ジェット構造と Sung Hak Lim, Mihoko Nojiri (JHEP, 2018) Amit Chakraborty, Sung Hak Lim, Mihoko Nojiri ( arXiv 1904.02092 ) 1
馬鹿でかい統計今後の LHC 統計は今後も増える（発見の物理から精密測定の物理へ）標準模型のプロセスのずれを探す。より厳しいカットをかけて、面白いイベントを探す過去の成功体験（ LEP) vs QCD 特有の難しさ * システマティクスへの理解が重要 hadronization, ** 解析のスピードアップの必要 PDF, parton shower modeling, … high pT objects (Events in Tail) soft object, mono something Physics outputs or something unknown effective operator, top partner dark matter.. 2
の不定性を実データを使って迂回（理解しないで済ます）あるいはソフトはプロセスの理解の促進機械学習 (ML) への期待機械学習を使ってイベントの再構成の効率化、高速化ジェットやイベントの特性を学習させて、 top W H などのを取り出す目的ルミノシティに見合ったアウトプット QCD 3
Jets と機械学習 Jet physics : QCD の中ではソフトな物理を含むにも変わらず比較的成功している (Theoretical understanding + computation) 理論と現象論パートンシャワーー＞ハドロン実験データとの比較 theory and computational developments Jet reconstruction algorithm ( kT, CA, antikT) IRC safe アルゴリズム numerical progress ( fastjet N^3-> Nlog N) Jet substructure (mass drop), Hi,,s W, Top reconstruction (BSM search) minimal Validation Analysis (leaving optimization to algorithm) → 機械学習 4

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