Processing and Data Analysis in Astronomy

Code:

AST413

Acronym:

AST413

Keywords
Classification	Keyword
OFICIAL	Astronomy

Instance: 2012/2013 - 1S

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:AST	11	Plano de Estudos do Mestrado em Astronomia	1	-	7,5	64	202,5

Teaching language

English

Objectives

The main objective of this lecture course is to make the students familiar with techniques presently used to process and analyze data in Astronomy. In particular, it is expected that the student, by the end of the 2011/2012 academic year, will understand the concepts underlying bayesian statistical inference, and be able to apply them to solve problems in Astronomy.

Learning outcomes and competences

It is expected that the student will be able to apply the methods associated with the process of bayesian statistical inference to the resolution of problems in Astronomy.

Working method

Presencial

Program

1. Introduction

2. Statistical Inference

2.1 Basic concepts

2.2 Bayes Theorem and applications

2.3 Introduction to bayesian inference

2.4 Random processes:probability distribution functions and moments

2.5 Central limit theorem

2.6 Confidence intervals

2.7 Hypothesis testing

2.8 Maximum entropy: concepts and applications

2.9 Applications of bayesian inference: gaussian errors, linear and non-linear models

2.10 Markov chains

3. Signal detection and characterization

3.1 Classical spectral analysis: Fourier series; convolution and correlation; Nyquist theorem; discret fourier transform; power spectrum.

3.2 Derivation and bayesian generalization of the Schuster and Lomb-Scargle statistics

3.3 Source detection and flux estimation

Mandatory literature

P. C. Gregory; Bayesian Logical Data Analysis for the Physical Sciences, 2005
J. V. Wall, C. R. Jenkins; Pratical Statistics for Astronomers, 2003
E.D. Feigelson, G.J. Babu; Modern Statistical Methods for Astronomy, 2012

Complementary Bibliography

E. T. Jaynes; Probability Theory: The Logic of Science, 2003
A. Papoulis, S. U. Pillai; Probability, Random Variables and Stochastic Processes, 2002
C. D. Paulino, M. A. A. Turkman, B. Murteira; Estatística Bayesiana, 2003
D. S. Sivia, J. Skilling; Data Analysis, 2006
A. Gelman, J. B. Carlin, H. S. Stern, D. B. Rubin; Bayesian Data Analysis, 2004

Teaching methods and learning activities

In the traditional lecture classes, the contents in the program are taught and their application clarified through examples. There will also be tutorial classes, where the students will be supervised in the application to real problems of the concepts and techniques introduced in the lecture classes.

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Description	Type	Time (hours)	Weight (%)	End date
Exam	Exame		50,00
Attendance (estimated)	Participação presencial	82,80
Project	Trabalho escrito		50,00
	Total:	-	100,00

Amount of time allocated to each course unit

Description	Type	Time (hours)	End date
Lecture Classes	Frequência das aulas	64
	Total:	64,00

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=0.5*Ex+0.5*Tr 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 exame (cannot be below 8 in a scale of 0 to 20) and Tr is the overall classification in the work tasks (between 0 and 20).

Examinations or Special Assignments

A few work tasks will be given to all students, and their overall classification will have a weight of 50 per cent towards the final classification.

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