Course: Learn Laser Interferometry with Finesse

Learn Laser Interferometry

A self-study course on interferometry for precision measurements,
using Python notebooks and Finesse.

Daniel Töyrä
Daniel Brown
Andreas Freise
and others
since 2016

This page provides resources and self-study material on laser interferometry. In particular we cover the topics related to the use of optical systems for gravitational wave detectors such as LIGO. At the same time this is a collection of reference examples for using PyKat.

This course is based on Finesse 2 and Pykat. If you are just starting, we recommend that you use the latest version, Finesse 3, from Many of the concepts are still the same but the detailed syntax is different. Finesse 3 combines the functionaility of of Pykat and Finesse in a better way.

Table of Contents

Schools and Workshops

Notebooks generated from this course have been used for in-person teaching in a several workshops. You can find and download the material from some of these workshops on dedicated pages:

Get started!

The course is presented as a selection of IPython notebooks (or Jupyter notebooks), interactive Python notebooks that run in a web browser. You can interact with the notebooks in two ways:

Screen-shots of IPython notebooks from this course.


Creative Commons Licence
Learn Laser Interferometry by D. Toyra, D. Brown and A. Freise is licensed under a Creative Commons Attribution 4.0 International License. The code is also made available under the GNU General Public License (GPLv3).

Permissions beyond the scope of this license may be available via

About the course

At the Gravitational Wave Group in Birmingham, UK, we work on the optical design and technologies of the laser interferometers for gravitational wave detection. For example, we led the optical design of the Einstein Telescope, the Optical Simulation and Design group towards the first baseline design of Advanced Virgo and are contributing to the Advanced Interferometer Configurations group of the LIGO Scientific Collaboration. In this course we combine material we have used in the past to train our graduate students starting in this field, for example as part of the GraWIToN training network.