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PhD	TR-NQF-HE: Level 8	QF-EHEA: Third Cycle	EQF-LLL: Level 8

Course Introduction and Application Information

Course Code:

PHYS6203

Course Name:

Statistical Methods in Particle Physics

Semester:

Fall

Course Credits:

ECTS

Language of instruction:

English

Course Condition:

Does the Course Require Work Experience?:

Type of course:

Compulsory Courses

Course Level:

PhD

TR-NQF-HE:8. Master`s Degree

QF-EHEA:Third Cycle

EQF-LLL:8. Master`s Degree

Mode of Delivery:

Face to face

Course Coordinator:

Doç. Dr. ANDREW JOHN BEDDALL

Course Lecturer(s):

Doç. Dr. Andrew Beddall

Course Assistants:

Course Objective and Content

Course Objectives:	Topics in probability and statistics are introduced through their definitions leading to the development of common statistical methods with emphasis placed on using these tools to solve problems in engineering and physics. Some special applications in particle physics are studied.
Course Content:	Probability theory, random numbers and Monte Carlo methods, transformation methods, probability distribution functions, cumulative probability distributions, Bayesian probability, parameter estimation, model fitting, goodness of fit estimation, combining measurements and error propagation, confidence intervals, hypothesis testing, applications in particle physics.

Learning Outcomes

The students who have succeeded in this course;
1) Identify the probabilistic nature of a system and calculate the probability of possible outcomes
2) Build a rudimentary Monte Carlo model of a probabilistic system
3) Identify and use a variety of probability distributions
4) Apply Bayesian statistics to problems in engineering and particle physics
5) Estimate model parameters
6) Fit models to experimental data and determine the goodness of fit
7) Combine experimental results with a correct treatment of errors
8) Form confidence intervals for a model parameter
9) Form hypothesis tests
10) Apply the knowledge learned in class to special topics particle physics

Course Flow Plan

Week	Subject	Related Preparation
1)	Introduction. The Reliability Block Diagram.
2)	Conditional Probability
3)	Random Variables and Expectation values. Monte Carlo methods for generating random variables.
4)	Revision / First mid-term exam
5)	Special Discrete Probability Distributions (I & II)
6)	Special Continuous Probability Distributions (I & II)
7)	Sampling (I & II)
8)	Discussion / Second mid-term exam
9)	Hypothesis testing. Confidence Intervals (I).
10)	Confidence Intervals (II). Modelling and parameter estimation.
11)	Revision / Final Exam

Sources

Course Notes / Textbooks:	Introduction to Statistics and Data Analysis for Physicists Bohm, G.; Zech, G. https://bib-pubdb1.desy.de/record/389738 Luca Lista "Statistical Methods for Data Analysis in Particle Physics" Second Edition; Spinger.
References:	Various web references are given in the lectures.

Course - Program Learning Outcome Relationship

Course Learning Outcomes	1	2	3	4	5	6	7	8	9	10
Program Outcomes
1) Possession of fundamental and recents theories and experimental techniques in the field of high energy and particle physics.
2) Effective use of the theoretical knowledge on applications.
3) Competence in using analysis tools and equipment in experimental studies.	1	1	1	1	1	1	1	1	1	1
4) Advanced design competence about particle detectors and/or particle accelerators.
5) Possession of data acquisition, data analysis and data processing skills.	2	2	2	2	2	2	2	2	2	2
6) Competence to do independent research in the field of High Energy and Particle Physics.
7) Having R&D and/or P&D experience on Particle Detectors and Particle Accelerators.
8) Collaborative work competence required by experimental and phenomenological research activities in the field of High Energy and Particle Physics.
9) Competence in understanding, using and developing the software and hardware required by particle physics research and applications, from data analysis to detector and accelerator design.	1	1	1	1	1	1	1	1	1	1

Course - Learning Outcome Relationship

No Effect	1 Lowest	2 Average	3 Highest

	Program Outcomes	Level of Contribution
1)	Possession of fundamental and recents theories and experimental techniques in the field of high energy and particle physics.	2
2)	Effective use of the theoretical knowledge on applications.	1
3)	Competence in using analysis tools and equipment in experimental studies.	3
4)	Advanced design competence about particle detectors and/or particle accelerators.
5)	Possession of data acquisition, data analysis and data processing skills.	2
6)	Competence to do independent research in the field of High Energy and Particle Physics.
7)	Having R&D and/or P&D experience on Particle Detectors and Particle Accelerators.
8)	Collaborative work competence required by experimental and phenomenological research activities in the field of High Energy and Particle Physics.
9)	Competence in understanding, using and developing the software and hardware required by particle physics research and applications, from data analysis to detector and accelerator design.

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Homework Assignments	16	% 20
Midterms	2	% 40
Final	1	% 40
total		% 100
PERCENTAGE OF SEMESTER WORK		% 60
PERCENTAGE OF FINAL WORK		% 40
total		% 100

Workload and ECTS Credit Calculation

Activities	Number of Activities	Workload
Course Hours	14	62.5
Study Hours Out of Class	14	150
Homework Assignments	14	150
Midterms	2	4
Final	1	3
Total Workload		369.5