Data Analysis in Physics and Astronomy

Code:

AST/FIS4002

Acronym:

AST/FIS4002

Level:

400

Keywords
Classification	Keyword
OFICIAL	Physics
OFICIAL	Astronomy

Instance: 2025/2026 - 2S

Active?	Yes
Responsible unit:	Department of Physics and Astronomy
Course/CS Responsible:	Master in Astronomy and Astrophysics

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
M:A_ASTR	5	Study plan since academic year 2024/2025	1	-	6	42	162
M:F	2	Official Study Plan	1	-	6	42	162

Teaching language

English
Obs.: As aulas decorrerão em português no caso de todos os estudantes entenderem esta língua.

Objectives

The general objective of this lecture course is to familiarize students with some techniques currently used in data analysis in Physics and Astronomy. In particular, it is intended that students develop an understanding of the main concepts underpinning the process of scientific inference and become capable of applying them when trying to solve problems in Physics and Astronomy.

Learning outcomes and competences

It is expected that the student will be able to apply the methods associated with the process of scientific inference to the analysis of data and the resolution of problems in Physics and Astronomy.

Working method

Presencial

Program

- Deductive and inductive inference in the scientific method: definition and manipulation of probabilities; Bayes' theorem; marginalization.
- Parameter estimation and model comparison: prior probability distributions and likelihood functions; exemplification through the analysis of spectra and images.
- Monte Carlo methods for characterizing the posterior probability distribution and calculating the evidence; validation and calibration; prior and posterior predictive checks; decision in the presence of uncertainty.
- Definition of experimental and observational strategies in Physics and Astronomy.
- Linear and hierarchical models.
- Supervised and unsupervised learning models.

Mandatory literature

P. C. Gregory; Bayesian Logical Data Analysis for the Physical Sciences, 2005
W. von der Linden, V. Dose, U. von Toussaint; Bayesian Probability Theory: Applications in the Physical Sciences, 2014

Complementary Bibliography

S. Andreon, B. Weaver; Bayesian Methods for the Physical Sciences, 2015
Bailer-Jones, C.A.L.; Practical Bayesian Inference: A Primer for Physical Scientists, 2017

Teaching methods and learning activities

In the theoretical-practical classes, the syllabus is explained and its application exemplified. Problems illustrating the concepts presented are also solved, and discussion is promoted in the classroom, contributing to the consolidation of knowledge and the development of a critical mind. In the practical-laboratorial classes, methods and techniques are implemented that can be used in the context of the analysis of data, such as spectra, time series and images, relevant for Physics and Astronomy.

Software

R
Python

keywords

Physical sciences > Astronomy
Physical sciences > Physics

Evaluation Type

Distributed evaluation with final exam

Assessment Components

designation	Weight (%)
Exame	40,00
Trabalho escrito	60,00
Total:	100,00

Amount of time allocated to each course unit

designation	Time (hours)
Estudo autónomo	60,00
Frequência das aulas	42,00
Elaboração de relatório/dissertação/tese	60,00
Total:	162,00

Eligibility for exams

In order to successefully complete the curricular unit, the student must attend the minimum number of classes (75%) stipulated in the student assessment regulations of the University of Porto (there will be an attendance sheet for all classes).

Calculation formula of final grade

The final classification is given by: Nf=0.4*Ex+0.1*T1+0.5*T2 where: Nf is the final classification; Ex is the classification in the final exam (evaluated in a scale of 0 to 20); T1 and T2 are, respectively, the intermediate and final classifications assigned to the practical work, assessed by means of a written report (evaluated in a scale of 0 to 20). It is necessary to obtain at least a classification of 8 on the exam for the student to be approved at the curricular unit.

Special assessment (TE, DA, ...)

Students with special status will be subject to the same type of assessment as the others.

Classification improvement

The improvement of the final classification can be made only by improving the classification in the written exam, that will still have a weigh of 40 percent in the final classification. It will not be possible to improve the classification in the pratical work task.

Observations

The jury of this curricular unit consists of Pedro Viana and Vítor Pereira.