Physicum D112
Weeks 36-39, 41-42, 44-48, 50
Exercise session Fridays 14.15-15.00
Physicum D112
Starting week 38
Lectures: Professor Kenneth Österberg
E-mail: kenneth.osterberg(at)helsinki.fi
Exercises: PhD Fredrik Oljemark
E-mail: fredrik.oljemark(at)helsinki.fi
- Lecture notes available here (pdf)
- Selected lecture recordings from previous years available here (mp4)
- Literature:
G. Cowan: Statistical Data Analysis (Oxford University Press 1998)
- A comprehensive reference of Statistical Methods in Physics. Contains both theoretical background and practical examples. Highly recommended.
Particle Data Group Reviews on Probability, Statistics, Monte Carlo techniques & Machine Learning (freely available on the web)
- Good but rather condensed summaries. Written by the same author, G. Cowan, except Machine Learning summary.
Recent summaries on Interpretable Machine Learning & Quantum Computing (in particle physics)
- Additional reading on state-of-the-art methods.
C. Walck: Hand-book on statistical distributions for experimentalists (freely available on the web)
- An opus on statistical distributions. When knowing your physics distribution, useful for finding out distribution characteristics and how to generate them. - Course content has significant overlap with PAP303 Statistical Inverse Methods.
Recommended to complete only one of the two so either PAP334 or PAP303.
PAP303 lecture notes from Antti Penttilä - Coding examples and help:
Matlab examples
Data analysis with Python
SimPy simulation examples with Python- Description of SimPy - Use of generative AI:
We follow general rules at University of Helsinki on AI usage on the course.
Usage of Large Language Models (LLMs) encouraged.
Usage of LLMs should be clearly stated including how LLM's were used.
Recommended to use LLMs provided by the university to students: Copilot (or CurreChat if you have access). Instructions.
- Fundamental concepts: experimental uncertainties and their correct interpretation, frequentist & Bayesian interpretation of probability, the most common distributions and their applications.
- Monte Carlo methods & statistical tests: basics of Monte Carlo methods, the concept of hypothesis and test statistic, rejection of a hypothesis, discriminant analysis (including machine learning) and goodness-of-fit tests.
- Parameter & uncertainty estimation (including fitting): the concept of estimation, the maximum likelihood method (suurimman uskottavuuden menetelmä) and the method of least squares (pienimmän neliösumman menetelmä).
- Confidence intervals & Unfolding: classical confidence intervals and their interpretation, unfolding techniques.
Exercises (9-10 papers) gives at maximum 36 points (weight: 50 %),
Home exam gives at maximum 36 points (weight: 50 %),
Written exam gives at maximum 36 points (weight: 50 %).
The two best of these three will be used for making the course grade
(you may choose to do only one out of the two exams if you wish).
Exercises given latest on Thursdays, notification by e-mail, to be returned following Thursday before 12.00 Finnish time.
Exercise paper available and your solutions to be returned on the moodle course page
Model answers to exercises available on moodle course page
Home exam: date & time to be determined
AND/OR
General exam: Fri 13.2.2026 12.30-16.30 in Chemical A110 (registration by Tue 3.2.2026 through SISU)
Previous departmental/general exams: 24.8.2017 19.1.2018 18.1.2019 17.1.2020 22.1.2021 16.2.2024 14.2.2025.
First lecture on Fri 5.9.2025 at 10.15!
No lecture on Fri 3.10.2025!
Special lecture on ROOT & TMWA Fri 17.10.2025!
No lecture on Fri 24.10.2025!
No lecture on Fri 14.11.2025!
Send any comments or questions about this page to kenneth.osterberg(at)helsinki.fi