PyCBC is software developed by a collaboration of LIGO, Virgo, and independent scientists. It is open source and freely available. We use PyCBC in the detection of gravitational waves from binary mergers such as GW150914. These examples explore how to analyze gravitational wave data, how we find potential signals, and learn about them.
These notebooks are available to view, download, or run in interactive sessions.
Tutorial 1: Accessing Gravitational-wave data
Tutorial 2: Data visualization and basic signal processing
Tutorial 3: Matched filtering to identify signals
Tutorial 4: Signal Consistency and Basic Significance Testing
Many of these tutorials will require you to make edits to config files as part of their exercises. At the moment this isn't easy to do on services like google colab. However, you can do them either on your local machine or by using services such as mybinder or sciserver which allow a full juypterhub experience with the ability to view and edit text files. Below we give links to the tutorials which should directly work in google colab, however.
Tutorial 0: Overview (this is a summary of inference tutorials 1-6; meant for a shorter tutorial session)
See video presentation of Tutorial 0 by Collin Capano
Tutorial 1: Models (likelihood function you are trying to sample + priors) and Samplers by Hand
Tutorial 5: Results files and Plotting
Tutorial 7: Adding Custom Waveforms to PyCBC
Tutorial 8: Black hole ringdown analysis with PyCBC
Tutorial 9: Adding custom models to PyCBC (e.g. alternate likelihoods, GRB / Kilonova models, etc.)
- How to access LIGO data
- How to do some basic signal processing
- Data visualization of LIGO data in time-frequency plots
- Matched filtering to extract a known signal
