Data Science in Physics

Year
1
Academic year
2020-2021
Code
02038668
Subject Area
Optional
Language of Instruction
Portuguese
Other Languages of Instruction
English
Mode of Delivery
Face-to-face
Duration
SEMESTRIAL
ECTS Credits
6.0
Type
Elective
Level
2nd Cycle Studies - Mestrado

Recommended Prerequisites

Advanced knowledge of statistics and programming.

Teaching Methods

Classes will be mostly practical using:

- experimental data from Particle Physics;

- computational tools for ab-initio Condensed Matter Physics simulations.

Learning Outcomes

- Understand the statistical data analysis techniques from the point of view of Particle Physics.

- Understand how to deal with systematic errors and how to establish criteria for identification of a discovery in Particle Physics.

- Understand the process of determination of experimental sensitivity.

- Use data science computational tools to reconstruct object such as, e.g., hadronic jets.

- Use Monte Carlo methods in Particle Physics data analysis.

- Understand the use of machine learning tools in Condensed Matter Physics.

- Understand the classifiers used in Condensed Matter Physics.

- Analyse, synthesise and process information.

- Prepare, process, interpret and communicate physical information, using relevant bibliographical sources, an appropriate speech and the right tools in classroom.

Work Placement(s)

No

Syllabus

Introduction.

Review of some topics in data analysis.

Experimental measurements and criteria to establish a discovery.

Determination of the experimental sensitivity.

Systematic errors.

Computational tools for data analysis.

Practical applications using experimental data from Particle Physics.

Machine learning in Condensed Matter Physics: materials' design and speeding-up of ab-initio methods.

Head Lecturer(s)

José Ricardo Morais Silva Gonçalo

Assessment Methods

Assessment
Resolution Problems: 40.0%
Project: 60.0%

Bibliography

Glen Cowan, Statistical Data Analysis, Oxford University Press, 1998

 

Ilya Narsky and Frank C. Porter, Statistical Analysis Techniques in Particle Physics, Wiley, 2014

 

Luca Lista, Statistical Methods for Data Analysis in Particle Physics, Springer, 2017

 

Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006

 

A. R. Oganov, ed., Modern methods of crystal structure prediction, Wiley, 2010

 

T. Lookman, S. Eidenbenz, F. Alexander, and C. Barnes, eds., Materials Discovery and Design By Means of Data Science and Optimal Learning, Springer, 2018

 

Jonathan Schmidt, Mário R. G. Marques, Silvana Botti, and Miguel A. L. Marques, Recent advances and applications of machine learning in solid-state materials science, Psi_k Scientific Highlight Of The Month, March 2019