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.

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

2. 我是誰？
Yuan CHAO (John)
YChao
...

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

6. 緊湊渺子線圈
Compact Muon Solenoid
(CMS)
如何對撞捕捉粒子？
https://www.youtube.com/watch?v=S99d9BQmGB0

7. 歐洲粒子物理研究機構
CERN
的地理位置

8. 瑞士
日內瓦近郊
跨越瑞法邊境

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...

12. https://home.cern/about/updates/2017/12/cms-releases-more-one-petabyte-open-data

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?)

15. Era of analog
~1980
↓
Digital
Processing

16. Digital
Processing
↓
AI ML Track
Reconstruction?

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!