Computing the performance of the template bank

We wish to evaluate the performance of the template bank by computing fitting factors against a large set of simulated signals. We wish to do this for a varying number of harmonics to see how performance varies as a function of the number of harmonics used.

Code base

We used a modified branch of PyCBC to do this step. This is the same branch as used for other stages later on. The code we used is here

https://github.com/spxiwh/pycbc/tree/tha_development_work_rebased_again

We will aim to merge these changes back into the main PyCBC branch, but this was the branch used when creating the banks used in the paper. We are using some modified executables in this step. These have the suffix _tha. We will consider whether these can be merged with their original executables.

Computing the fitting factors

For this step we will need

Do look over the config file (at least the top of it) before running. It contains paths to the input template bank, which you will likely need to modify. It may also need modifying for a different cluster. For now PyCBC workflows require a cluster running condor, but it is possible to setup a local condor pool, which will “submit” jobs to run on a local machine. This will likely take a while to run! We are looking to support SLURM clusters with PyCBC pegasus workflows.

Then you just run:

bash launch.sh

to submit the workflow. This workflow will run for some time (please see the PyCBC website, and SLACK channel for help running such workflows), and will produce an output page with plots. The output HDF file from this stage is used when generating our result plots.

Our outputs

We have our own HDF output files for these runs, which form inputs to the plotting scripts, that you can download here