Physics
6 minute read
This section starts by describing the LHC accelerator at CERN and evidence found by the experiments suggesting existence of a Higgs Boson. The huge number of authors on a paper, remarks on histograms and Feynman diagrams is followed by an accelerator picture gallery. The next unit is devoted to Python experiments looking at histograms of Higgs Boson production with various forms of shape of signal and various background and with various event totals. Then random variables and some simple principles of statistics are introduced with explanation as to why they are relevant to Physics counting experiments. The unit introduces Gaussian (normal) distributions and explains why they seen so often in natural phenomena. Several Python illustrations are given. Random Numbers with their Generators and Seeds lead to a discussion of Binomial and Poisson Distribution. MonteCarlo and acceptreject methods. The Central Limit Theorem concludes discussion.
Looking for Higgs Particles
Bumps in Histograms, Experiments and Accelerators
This unit is devoted to Python and Java experiments looking at histograms of Higgs Boson production with various forms of shape of signal and various background and with various event totals. The lectures use Python but use of Java is described.

<{gitcode}/physics/mrhiggs/higgsclassIsloping.py>
Particle Counting
We return to particle case with slides used in introduction and stress that particles often manifested as bumps in histograms and those bumps need to be large enough to stand out from background in a statistically significant fashion.
We give a few details on one LHC experiment ATLAS. Experimental physics papers have a staggering number of authors and quite big budgets. Feynman diagrams describe processes in a fundamental fashion.
Experimental Facilities
We give a few details on one LHC experiment ATLAS. Experimental physics papers have a staggering number of authors and quite big budgets. Feynman diagrams describe processes in a fundamental fashion.
Accelerator Picture Gallery of Big Science
This lesson gives a small picture gallery of accelerators. Accelerators, detection chambers and magnets in tunnels and a large underground laboratory used fpr experiments where you need to be shielded from background like cosmic rays.
Resources
 http://grids.ucs.indiana.edu/ptliupages/publications/Where%20does%20all%20the%20data%20come%20from%20v7.pdf [@fox2011does]
 http://www.sciencedirect.com/science/article/pii/S037026931200857X [@aad2012observation]
 http://www.nature.com/news/specials/lhc/interactive.html
Looking for Higgs Particles: Python Event Counting for Signal and Background (Part 2)
This unit is devoted to Python experiments looking at histograms of Higgs Boson production with various forms of shape of signal and various background and with various event totals.
Files:
 <{gitcode}/physics/mrhiggs/higgsclassIsloping.py>
 <{gitcode}/physics/numbertheory/higgsclassIII.py>
 <{gitcode}/physics/mrhiggs/higgsclassIIuniform.py>
Event Counting
We define event counting data collection environments. We discuss the python and Java code to generate events according to a particular scenario (the important idea of Monte Carlo data). Here a sloping background plus either a Higgs particle generated similarly to LHC observation or one observed with better resolution (smaller measurement error).
Monte Carlo
This uses Monte Carlo data both to generate data like the experimental observations and explore effect of changing amount of data and changing measurement resolution for Higgs.

With Python examples of Signal plus Background (7:33) This lesson continues the examination of Monte Carlo data looking at effect of change in number of Higgs particles produced and in change in shape of background.
Resources
 Python for Data Analysis: Agile Tools for Real World Data By Wes McKinney, Publisher: O’Reilly Media, Released: October 2012, Pages: 472. [@mckinneypython]
 http://jwork.org/scavis/api/ [@jworkapi]
 https://en.wikipedia.org/wiki/DataMelt [@wikipediadatamelt]
Random Variables, Physics and Normal Distributions
We introduce random variables and some simple principles of statistics and explains why they are relevant to Physics counting experiments. The unit introduces Gaussian (normal) distributions and explains why they seen so often in natural phenomena. Several Python illustrations are given. Java is currently not available in this unit.
 Higgs (39)
 <{gitcode}/physics/numbertheory/higgsclassIII.py>
Statistics Overview and Fundamental Idea: Random Variables
We go through the many different areas of statistics covered in the Physics unit. We define the statistics concept of a random variable.
Physics and Random Variables
We describe the DIKW pipeline for the analysis of this type of physics experiment and go through details of analysis pipeline for the LHC ATLAS experiment. We give examples of event displays showing the final state particles seen in a few events. We illustrate how physicists decide whats going on with a plot of expected Higgs production experimental cross sections (probabilities) for signal and background.
Statistics of Events with Normal Distributions
We introduce Poisson and Binomial distributions and define independent identically distributed (IID) random variables. We give the law of large numbers defining the errors in counting and leading to Gaussian distributions for many things. We demonstrate this in Python experiments.
Gaussian Distributions
We introduce the Gaussian distribution and give Python examples of the fluctuations in counting Gaussian distributions.
Using Statistics
We discuss the significance of a standard deviation and role of biases and insufficient statistics with a Python example in getting incorrect answers.
Resources
 http://indico.cern.ch/event/20453/session/6/contribution/15?materialId=slides
http://www.atlas.ch/photos/events.html(this link is outdated) https://cms.cern/ [@cms]
Random Numbers, Distributions and Central Limit Theorem
We discuss Random Numbers with their Generators and Seeds. It introduces Binomial and Poisson Distribution. MonteCarlo and acceptreject methods are discussed. The Central Limit Theorem and Bayes law concludes discussion. Python and Java (for student  not reviewed in class) examples and Physics applications are given.
Files:
 <{gitcode}/physics/calculateddiceroll/higgsclassIVseeds.py>
Generators and Seeds
We define random numbers and describe how to generate them on the computer giving Python examples. We define the seed used to define to specify how to start generation.
Binomial Distribution
We define binomial distribution and give LHC data as an example of where this distribution valid.
AcceptReject
We introduce an advanced method accept/reject for generating random variables with arbitrary distributions.
Monte Carlo Method
We define Monte Carlo method which usually uses accept/reject method in typical case for distribution.
Poisson Distribution
We extend the Binomial to the Poisson distribution and give a set of amusing examples from Wikipedia.
Central Limit Theorem
We introduce Central Limit Theorem and give examples from Wikipedia.
Interpretation of Probability: Bayes v. Frequency
This lesson describes difference between Bayes and frequency views of probability. Bayes’s law of conditional probability is derived and applied to Higgs example to enable information about Higgs from multiple channels and multiple experiments to be accumulated.
Resources
\TODO{integrate physicsreferences.bib}
SKA – Square Kilometer Array
Professor Diamond, accompanied by Dr. Rosie Bolton from the SKA Regional Centre Project gave a presentation at SC17 “into the deepest reaches of the observable universe as they describe the SKA’s international partnership that will map and study the entire sky in greater detail than ever before.”
A summary article about this effort is available at:
 https://www.hpcwire.com/2017/11/17/sc17keynotehpcpowersskaeffortspeerdeepcosmos/ The video is hosted at
 http://sc17.supercomputing.org/presentation/?id=inspkr101&sess=sess263 Start at about 1:03:00 (e.g. the one hour mark)