介紹 TrackML 挑戰 (Introduction to TrackML Kaggle challenge)
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This document introduces the Kaggle TrackML challenge, which aims to develop new machine learning solutions for particle tracking in high energy physics experiments. It provides background on the speaker's work with the CMS experiment at CERN, particle physics concepts like the standard model, and challenges of digitally reconstructing particle tracks from detector data. Instructions for installing the necessary Python packages like Jupyter and SciPy to get started with machine learning for particle tracking are also included.
介紹 TrackML 挑戰 (Introduction to TrackML Kaggle challenge)
- 1. 介紹介紹 TrackMLTrackML 的的 KaggleKaggle 挑戰挑戰 An Introduction toAn Introduction to Kaggle TrackML ChallengeKaggle TrackML Challenge Yuan CHAO ( 趙元 ) (National Taiwan University, Taipei, Taiwan) PyCon2018 2018/06/01-2
- 3. 研究員 高能物理 使用 OSS 做研究 ...
- 4. 研究員 高能物理 使用 Python 做研究 ...
- 5. 研究員 高能物理 使用 Python 做研究 ... Member of CMS Experiment
- 9. 尋找希格斯粒子 Atlas Higgs ML Challenge https://www.kaggle.com/c/higgs-boson $13,000 & 876 teams
- 10. 淺嚐味物理 Search for charged lepton flavour violation https://www.kaggle.com/c/flavours-of-physics Search for new physics on lepton-flavour violation $15,000 & 673 teams
- 11. 機器學習粒子追跡 TrackML Challenge https://www.kaggle.com/c/trackml-particle -identification To develop new machine-learning solutions for the next generation of particle detectors $25,000 & Nvidia v100 GPU and more...
- 13. 13 標準模型標準模型 Standard ModelStandard Model ~10-18 m宇宙的尺度 http://htwins.net/scale2/~10-1 m 膠子 光子 W/Z 子 重力子 強作用力 電磁力 弱作用力 重力 夸 克 輕 子 奈米 =10-9 m
- 14. Machine Learning is nothing new in HEP People in Tevatron, B-factories, LEP and LHC experiments more or less use MVA in their studies! (LL, LD → BDT, ANN, .. → DL?)
- 17. 17 A Beginner's GuideA Beginner's Guide https://www.kaggle.com/pranav84/a-beginner-s-guide- to-cern-s-trackml-challenge/notebook
- 18. 18 Related URLsRelated URLs LHC computing grid (LCG) and CERN overview video: https://cds.cern.ch/record/2020780 CMS Introduction video: https://www.youtube.com/watch?v=S99d9BQmGB0 "Higgs ML" Kaggle Challenge https://www.kaggle.com/c/higgs-boson “Flavour of physics” Kaggle Challenge https://www.kaggle.com/c/flavours-of-physics "TrackML" Kaggle Challenge https://www.kaggle.com/c/trackml-particle-identific ation 宇宙的尺度 http://htwins.net/scale2/ Atlas Experiment http://atlas.cern/ LHCb Experiment http://lhcb-public.web.cern.ch/lhcb-public/
- 19. 以上 Thank YOU! 謝謝 Mercie de Votre Attention
- 20. 20 Installing Jupyter & SciPyInstalling Jupyter & SciPy Setup a virtual environment (you need python installed before hands) Using pip: $ pip3 install virtualenv You can also use easy_install or apt-get instead Open a terminal Type in the following commands: $ virtualenv -p python3 .scienv $ source .scienv/bin/activate ← activate the environment! $ pip3 install --upgrade pip $ pip3 install jupyter $ pip3 install scipy pandas sklearn ← you get all packages Then start the jupyter notebook server: $ jupyter notebook ← a web page will be loaded automatically Here we go!