淺嚐 LHCb 數據分析的滋味 Play around the LHCb Data on Kaggle with SK-Learn and MatPlotLib
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LHC實驗是現今粒子物理實驗的最先端，2012年所發現的希格斯粒子更是物理界的一大盛事。繼Atlas實驗在Kaggle公開Higgs挑戰之後，另一個LHC的LHCb實驗也將實驗數據搬上了Kaggle平台。本講題將簡介背後的實驗，並使用LHCb的數據以SciKit-Learn進行多維度數據分析與使用MatPlotLib視覺化。 Play around the LHCb Data on Kaggle with SK-Learn and MatPlotLib
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淺嚐 LHCb 數據分析的滋味 Play around the LHCb Data on Kaggle with SK-Learn and MatPlotLib
- 1. 淺嚐淺嚐 LHCbLHCb 數據分析的滋味數據分析的滋味 Play around the LHCb Data on Kaggle withPlay around the LHCb Data on Kaggle with SciKit-Learn and MatPlotLibSciKit-Learn and MatPlotLib Yuan CHAO ( 趙元 ) (National Taiwan University, Taipei, Taiwan) PyCon2017 2017/06/09-11
- 3. 研究員 高能物理 使用 OSS 做研究 ...
- 4. 全球 LHC 計算網格 Worldwide LHC Computing Grid (WLCG) 如何分析處理數據？ https://cdsweb.cern.ch/record/1541893 https://www.youtube.com/watch?v=jDC3-QSiLB4
- 7. LHC 周長 27 KM 位於地下 50~150 公尺
- 10. 質子團每秒通過 四千萬次 (40MHz) 平均每次有 15 個對撞
- 17. 事例重建 磁帶長期保存
- 19. 二級資料中心提供實驗學家模擬與分析數據 ( 前 ) 亞洲唯一 一級資料中心 中研院網格中心
- 20. 研究員 高能物理 使用 OSS 做研究 ... Member of CMS Experiment
- 22. 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
- 25. τ 濤子？ μ 渺子？？ 味物理？ 輕子味不守恆？ –- 請給我五分鐘
- 26. 26 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
- 27. 27 大霹靂大霹靂 The Origin of the UniverseThe Origin of the Universe
- 28. 28 四大問題四大問題 The QuestionsThe Questions LHC was built for the following purposes: 質量的來源 To find the origin of mass... the Higgs boson. 暗物質與暗能量 Looking for the unification.. Super-symmetry as well as other candidates of Dark Mater & Dark energy 反物質的消失 Investigate the mystery of anti-matter disappearance 宇宙初期狀態 Physics at the early stage of the universe: Heavy Ion Collisions and Quark-Gluon Plasma Courtesy of Center for European Nuclear Research (CERN), Geneva, Switzerland.
- 29. 29 Symmetry & Flavor PhysicsSymmetry & Flavor Physics People think the universe is symmetric? E = mc2 Parity violation introduced by T.D. Lee ( 李政道 ) and C.N. Yang ( 楊振寧 ) in 1956. –- 宇稱不守恆 Parity violation seen in a β decay by C.S. Wu ( 吳健雄 ) in 1957. Nobel prize for Lee & Yang. CP violation discovered in Kaon system in 1964. M. Kobayashi and T. Maskawa introduced CP violation in the Standard Model in 1973. –- 電荷・宇稱不守恆 Sanda and Carter pointed out the possibility of CP violation in the B meson system in 1980.
- 30. Prof. Wu's experiment in 1956. Prof. Li and Yang got Nobel Prize in 1957. http://de.wikipedia.org/wiki/Wu-Experiment
- 31. 31 Symmetry & Flavor PhysicsSymmetry & Flavor Physics KTeV experiment at FNAL established the direct CP violation in Kaon system and confirmed by NA48 at CERN in 1999. Belle and BaBar observed indirect CP violation B meson system in 2002. Belle observed the direct CP violation in B → ππ but not confirmed by BaBar in 2004 Belle and BaBar present the evidence of direct CP violation in B → Kππ in 2004. M. Kobayashi ( 小林誠 ) and T. Maskawa ( 益川敏英 ) share the Nobel Prize in 2008 with Y. Nambu ( 南部陽一郎 ). CP violation can't fully explain the Baryon asymmetry problem. → People cont. searching for NP
- 32. 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, NN, .. → DL?)
- 34. 物理到此為止 ...
- 35. 35 The Kaggle ChallengeThe Kaggle Challenge τ→3μ breaks lepton flavour conservation Basic Data operations Input variables Signal vs. Background Correlations K-S test, CvM test ROC and AUC Machine Learning Algor. Event weight Training and testing AUC score calculation Summary https://www.kaggle.com/c/flavours-of-physics/data Samle Events training.csv mixed MC & data τ→3μ test.csv mixed MC & data τ→3μ check_agre ement.csv mixed MC & data Ds→φ(μμ)π check_corr elation.csv real background data
- 36. 36 The GoalThe Goal Look for the rare events of τ→3μ Classifier not too dependent on MC and data Classifier not too dependent on the τ mass The score is counted using the weighted area under the ROC curve (AUC)
- 37. 37 The K-S TestThe K-S Test The τ→3μ process is not yet observed Signal is made with MC simulation Background are from real data The classifier should not pick up the difference A control channel Ds→φ(μμ)π is used for the similarity The Kolmogorov-Smirnov (KS) test used to evaluate the difference; requiring KS < 0.09 F are the cumulative distribution functions for MC and real data
- 38. 38 The CvM TestThe CvM Test The provided background events are not τ-free Classifier should not too much depend on τ-mass The distribution of τ-mass could be used to extract signal # The Cramer-von Mises (CvM) test is used to test the correlation; requiring CvM-value < 0.002 F are the predictions cumulative distribution functions for all data and data in some mass interval corresponding.
- 39. “Rules… Some of them can be bent, others are to be broken” – Morpheus https://artistotleonline.wordpress.com/category/climax/
- 40. 40 LiveLive DEMO with Jupyter NBDEMO with Jupyter NB Forked from Kaggle challenge package https://github.com/yandexdataschool/flavours-of-physics-start Now following my derived Jupyter notebook https://github.com/yuanchao/flavours-of-physics-start/blob/master/my_baseline.ipynb
- 41. 41 Related URLsRelated URLs LHC computing grid (LCG) and CERN overview video: https://cds.cern.ch/record/2020780 "Higgs ML" Kaggle Challenge https://www.kaggle.com/c/higgs-boson “Flavour of physics” Kaggle Challenge https://www.kaggle.com/c/flavours-of-physics 宇宙的尺度 http://htwins.net/scale2/ Heavy Flavour Data Mining workshop https://indico.cern.ch/event/433556/ Official jupyter NB: https://github.com/yandexdataschool/flavours-of-physics -start My derived jupter NB: https://github.com/yuanchao/flavours-of-physics-start
- 42. 以上 Thank YOU! 謝謝 Remercie de Votre Attention
- 43. 43 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!