藉由介紹自己的工作角色與研究內容,為高中生說明基礎科學研究的現況。並期許藉由跨界的合作,無論在業界或是研究工作上發揮所長。
By introduce the personal role in the fundamental research, the talk hopes to let high school students know about the international collaboration and to bring the cooperation between different domains.
玩轉 LHC 公開數據 (Play around with the LHC open data)Yuan CHAO
The document discusses analyzing LHC open data using tools like ROOT and Jupyter. It begins by introducing the speaker and their research using open-source software. Various LHC and CMS resources are then presented, including an animation of proton paths and the worldwide computing grid. Instructions are provided for setting up the analysis environment, including downloading a CERN virtual machine image and configuring the ROOT and Jupyter environments. Examples are given of analyzing CMS open data to search for particles like the Z boson and Higgs boson.
介紹 TrackML 挑戰 (Introduction to TrackML Kaggle challenge)Yuan CHAO
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.
淺嚐 LHCb 數據分析的滋味 Play around the LHCb Data on Kaggle with SK-Learn and MatPlotLibYuan CHAO
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
A local virtual signer project, LINNE, is proposed several years ago. However, to process a huge amount of sound-bank data is big problem. Here we make use of the python tool lib., PyMIR and SciKit-Learn, to help us extract the necessary information that needed for a song synthesizer, ex. UTAU.
玩轉 LHC 公開數據 (Play around with the LHC open data)Yuan CHAO
The document discusses analyzing LHC open data using tools like ROOT and Jupyter. It begins by introducing the speaker and their research using open-source software. Various LHC and CMS resources are then presented, including an animation of proton paths and the worldwide computing grid. Instructions are provided for setting up the analysis environment, including downloading a CERN virtual machine image and configuring the ROOT and Jupyter environments. Examples are given of analyzing CMS open data to search for particles like the Z boson and Higgs boson.
介紹 TrackML 挑戰 (Introduction to TrackML Kaggle challenge)Yuan CHAO
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.
淺嚐 LHCb 數據分析的滋味 Play around the LHCb Data on Kaggle with SK-Learn and MatPlotLibYuan CHAO
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
A local virtual signer project, LINNE, is proposed several years ago. However, to process a huge amount of sound-bank data is big problem. Here we make use of the python tool lib., PyMIR and SciKit-Learn, to help us extract the necessary information that needed for a song synthesizer, ex. UTAU.
This document discusses using Python to analyze LHC data from CERN's Higgs Boson machine learning challenge on Kaggle. It introduces ROOT and TMVA tools for working with particle physics data in C++ and Python. It also discusses SciPy and Scikit-Learn libraries that can be used for tasks like data preprocessing, machine learning algorithms, and visualization.
The document provides an overview of detector simulation. It introduces the goals of tracking systems, calorimeters, and muon detectors. It discusses full simulation with GEANT and fast simulation tools like PGS and Delphes. Events are generated, passed through the detector simulation, and then reconstructed. Visualization tools can display tracks and energy deposits. Exercises are provided to give hands-on experience with simulation and analysis concepts.
This document summarizes a presentation about the Worldwide LHC Computing Grid (WLCG) and how it provides computing resources for experiments at the Large Hadron Collider (LHC). The WLCG links computing grids from different regions to coordinate resources across over 300 computer centers. It provides over 340,000 CPU cores and moves about 10GB/s of data for each LHC experiment. The WLCG archives about 15 petabytes of data per year from the LHC experiments. The presentation discusses how the WLCG uses open source software like the Globus Toolkit, Scientific Linux, ROOT, and RooFit to provide its distributed computing services.
Some history about the Chinese font display under Linux with FreeType engine and the fracture problem on stroke-based composition fonts like the infamous MingLiu Dyna fonts.
This document discusses using Python to analyze LHC data from CERN's Higgs Boson machine learning challenge on Kaggle. It introduces ROOT and TMVA tools for working with particle physics data in C++ and Python. It also discusses SciPy and Scikit-Learn libraries that can be used for tasks like data preprocessing, machine learning algorithms, and visualization.
The document provides an overview of detector simulation. It introduces the goals of tracking systems, calorimeters, and muon detectors. It discusses full simulation with GEANT and fast simulation tools like PGS and Delphes. Events are generated, passed through the detector simulation, and then reconstructed. Visualization tools can display tracks and energy deposits. Exercises are provided to give hands-on experience with simulation and analysis concepts.
This document summarizes a presentation about the Worldwide LHC Computing Grid (WLCG) and how it provides computing resources for experiments at the Large Hadron Collider (LHC). The WLCG links computing grids from different regions to coordinate resources across over 300 computer centers. It provides over 340,000 CPU cores and moves about 10GB/s of data for each LHC experiment. The WLCG archives about 15 petabytes of data per year from the LHC experiments. The presentation discusses how the WLCG uses open source software like the Globus Toolkit, Scientific Linux, ROOT, and RooFit to provide its distributed computing services.
Some history about the Chinese font display under Linux with FreeType engine and the fracture problem on stroke-based composition fonts like the infamous MingLiu Dyna fonts.
17. 17
尚未解的問題尚未解的問題 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
宇宙對稱性與反物質的消失
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.
20. 32
對稱性與味物理對稱性與味物理
古今中外,人們相信自然界是對稱的。 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 貝他衰變 by C.S. Wu ( 吳健雄 )
in 1957. Nobel prize for Lee & Yang.
電荷宇稱不守恆 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.
22. 34
對稱性與味物理對稱性與味物理
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.
→ Searching for New Physics
24. 質子加速的軌跡
The path of the protons
– CERN Overview
animation
http://cds.cern.ch/record/2020780
http://www.youtube.com/watch?v=pQhbhpU9Wrg
http://amara.org/zh-tw/videos/ngrZNFAWTli2/info/
lhc-animation-the-path-of-the-protons/
43. "A computational grid is a hardware and software
infrastructure that provides dependable, consistent,
pervasive, and inexpensive access to high-end
computational capabilities."
... by Carl Kesselman and Ian Foster in 1998
取用軟硬體計算資源的基礎建設
44. "The sharing that we are concerned with is not primarily file
exchange but rather direct access to computers, software, data and
other resources, as is required by a range of collaborative problem-
solving and resource-brokering strategies emerging in industry,
science, and engineering. This sharing is, necessarily, highly
controlled, with resource providers and consumers defining clearly
and carefully just what is shared, who is allowed to share, and the
conditions under which sharing occurs. A set of individuals and/or
institutions defined by such sharing rules form what we call a
virtual organization"
... "The Anatomy of the Grid" in 2000
共享運算資源的虛擬研究機構
45. ~ 1 MByte / event x 40 MHz 碰撞
Capable Trigger Rate → ~ 300 KHz
Level-1 reduction → ~ 300 Hz
→ ~ 300 MByte / s RAW data x 2.x
→ ~ 10 – 15 PByte / year
46. ~ 1 MByte / event x 40 MHz 碰撞
Capable Trigger Rate → ~ 300 KHz
Level-1 reduction → ~ 300 Hz
→ ~ 300 MByte / s RAW data x 2.x
→ ~ 10 – 15 PByte / year
每年壹萬伍千顆 1TB 硬碟
Needs 15K 1TB HDD per year
49. 機器學習
早就是在高能物
理界廣泛使用的
People in Tevatron, B-factories,
LEP and LHC experiments
more or less use MVA in their
studies!
(LL, LD → BDT, NN, .. → DL?)
Supervised learning → MC vs. Data
54. 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