Astronomy Lab

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

At the end of the course the student will be able to

1. Formulate a physical and statistical model of a well-posed astronomical
problem.

2. Understand and be able to apply Bayesian inference statistical techniques.

3. Use data from the major astronomical archives and perform with them some
preliminary analyses to improve the physical and statistical understanding of
a system.

4. Know the basics of scientific programming in the Python language and be
able to implement programs to perform Bayesian inference starting from
simulated and real data.

5. Appreciate the importance of numerical stability and efficiency of
algorithms, especially if applied to systeems with large data volumes.