Table of contents

1. Schedule Spring 2021 2. Lecture notes 3. Problem sets 4. Numerical project:

5. Slack group you can join to ask questions/get help / ITA UiO Astro forum for this course

About the course

In this graduate course you will learn about the large scale structure and the Cosmic Microwave Background (CMB) fluctuations: what is the underlying theory and the different physical effects that leads to observable signatures. We follow Dodelson and Baumann, but provide comprehensive online lecture notes and problem-sets so its possible to follow it without having the textbook (or attending the lectures if you like self-study). The aim of the course is not just to teach you the theory, but also how to code up the equations to make your own Einstein-Boltzmann solver that computes predictions that can be compared with actual observations. These things are already coded up for you in great packages like CAMB and CLASS which are flexible, full of features and is more accurate and runs faster than you will be able to do in this project (though its possible to get pretty close). But by doing it yourself you get experience in writing a bigger code and with this in hand you can easily explore the consequence of changing the cosmological parameters or turning on/off some of the physics and see how that affects the result to better understand the physics. We will also go through how to structure a code like this, how to test it and make sure it works correctly and the relevant algorithms you need to know to do it efficiently. You will also get experience in writing a proper research article based on the results of the numerical project. All these things - knowing the theory, understanding the physics and to be able to numerically solve for the predictions - are important to know to be a good modern cosmologist.

Code template on Github
Code template on Github

Figure: Key cosmological observables: the CMB angular power-spectrum and the matter power-spectrum that you will learn the theory, the physical understanding and how to numerically compute the theoretical prediction for in this course.