Tutorials
Below, we provide a series of tutorial which highlight the main features of the pygwb package.
This package is constituted of several modules, each with different functionalities. These can be
combined into a pipeline, which takes the user from gravitational-wave data to estimators of the
gravitational-wave background (GWB). For more details on the methodology of GWB searches, we refer
the reader to the pygwb paper.
The pygwb package comes with a default pipeline, pygwb_pipe, which combines the different modules
of the package. However, one of the assets of the code is its high level of modularity. Hence, users should
feel free to assemble a pipeline that addresses their needs. A quickstart manual of the default pygwb_pipe
pipeline is provided below.
When running pygwb on long data sets, it can be more convenient to split the large amount of data into smaller chunks, and run the analysis on those individually. This functionality is supported within
pygwb through the inclusion of two additional scripts: pygwb_dag and pygwb_combine. For more information, check out the tutorial below.
The pygwb package also comes with a statistical checks module, which provides a way to visualize the results of an analysis runs. Through a series of plots, it offers the possibility
to check the results for statistical consistency. To learn how to run a series of statistical checks, check out the tutorial below.
The different scripts above are conveniently grouped together into a workflow, which executes one script after the other. For more information on the workflow, we refer the user to the tutorial below.
In addition, the pygwb suite features a parameter estimation module, which relies on the bilby package.
Using Bayesian inference, the user can run parameter estimation on the output of a pygwb run to constrain different parameters of a given model. More on parameter estimation and how to
run it in pygwb below.
The pygwb package contains a data simulation module, which can be used to simulate a stochastic gravitational-wave background (GWB)
given by a specific power spectral density (PSD) or as the superposition of individual compact binary coalescences (CBCs). To learn how
to use the simulator module, check out the tutorial below.