Astronomy Lab
A.Y. 2018/2019
Learning objectives
To develop statistical and programming skills that will allow the students to perform their own measurements of relevant astrophysical quantities. Many of the measurements considered in the course will require the use of large databases ("big data science"; see for example the Gaia astrometric satellite). The first part of the course will focus on the statistical tools required for the various projects, with particular emphasis on Bayesian statistics. Students will also have the opportunity to learn the Python programming language. Most of the proposed measurements will be of astrophysical nature, but the skills developed during the course will also be useful for students with different interests and backgrounds.
Expected learning outcomes
Undefined
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
Second semester
Course syllabus
Introduction to probability theory
· Random variables, probability distributions
· Meaning of probability: frequentist and Bayesian interpretation
· Correlated random variables, moments, dependent variables
· Bayes' theorem and its simplest interpretation
· Bayesian inference
· Maximum likelihood
· Laplace's method
· Simple clustering algorithms
· Bayesian model comparison
· Monte Carlo methods
Sample applications
· Local star clusters from Gaia DR1 and 2
· Analysis of microlensing events from the OGLE database
· Distance of the Large Magellanic Cloud from eclipsing binaries
· Misurement of H0 from cefeids
· Analysis of the kinematics of a spiral galaxy from integral field unit data
· Analysis of a strong gravitational lens
· Fundamental plane using the SLOAN Digital Sky Survey
· Random variables, probability distributions
· Meaning of probability: frequentist and Bayesian interpretation
· Correlated random variables, moments, dependent variables
· Bayes' theorem and its simplest interpretation
· Bayesian inference
· Maximum likelihood
· Laplace's method
· Simple clustering algorithms
· Bayesian model comparison
· Monte Carlo methods
Sample applications
· Local star clusters from Gaia DR1 and 2
· Analysis of microlensing events from the OGLE database
· Distance of the Large Magellanic Cloud from eclipsing binaries
· Misurement of H0 from cefeids
· Analysis of the kinematics of a spiral galaxy from integral field unit data
· Analysis of a strong gravitational lens
· Fundamental plane using the SLOAN Digital Sky Survey
FIS/01 - EXPERIMENTAL PHYSICS
FIS/05 - ASTRONOMY AND ASTROPHYSICS
FIS/05 - ASTRONOMY AND ASTROPHYSICS
Laboratories: 54 hours
Lessons: 12 hours
Lessons: 12 hours
Professors:
Grillo Claudio, Lombardi Marco
Professor(s)
Reception:
Friday, 9:30-12:30 (by appointment)
Physics Department, via Giovanni Celoria, 16, 20133 Milano