Data Analysis in Physics and Astronomy

Code:

AST/FIS4002

Acronym:

AST/FIS4002

Level:

400

Keywords
Classification	Keyword
OFICIAL	Physics
OFICIAL	Astronomy

Instance: 2023/2024 - 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	14	Study plan since academic year 2023/2024	1	-	6	42	162
M:F	0	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.
- Parameter estimation and model comparison in Physics and Astronomy: exemplification through the analysis of spectra and detection of sources.
- Analytical fitting of linear physical models in the presence of Gaussian uncertainties.
- Computational fitting of nonlinear physical models.
- Analysis of time series and images.
- Definition of experimental and observational strategies in Physics and Astronomy.

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
J.M. Hilbe, R.S. de Souza and E.E.O. Ishida; Bayesian Models for Astrophysical Data, 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.

Evaluation Type

Distributed evaluation with final exam

Assessment Components

designation	Weight (%)
Exame	30,00
Trabalho escrito	70,00
Total:	100,00

Amount of time allocated to each course unit

designation	Time (hours)
Estudo autónomo	120,00
Frequência das aulas	42,00
Total:	162,00

Eligibility for exams

In the final exam students are required to obtain a minimum classification of 8 in 20.

Calculation formula of final grade

The final classification is given by: Nf=0.30*Ex+0.35*Tr1+0.35*Tr2 where Nf is the final classification (cannot be below 10 in a scale of 0 to 20), Ex is the classification in the final exam (cannot be below 8 in a scale of 0 to 20), Tr1 and Tr2 are the overall classifications respectively in the first and second pratical work tasks with written report (between 0 and 20).

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 30 percent in the final classification. It will not be possible to improve the classification in the pratical work tasks.

Observations

The jury of this curricular unit consists of Pedro Viana, Mário João Monteiro and Vítor Pereira.