cbc-pe-demonstration
Produces a demonstration of how parameter estimation is used to understand gravitational wave observations of compact binary coalescences.
Installation
Download source code and install
$ git clone git@gitlab.com:dwysocki/cbc-pe-demonstration.git
$ cd cbc-pe-demonstration
$ pip install --user .
Usage
Check the command line usage
usage: cbc_pe_demo [-h] --trial-masses TRIAL_MASSES [TRIAL_MASSES ...]
[--trial-masses-post-fit TRIAL_MASSES_POST_FIT [TRIAL_MASSES_POST_FIT ...]]
[--approximant APPROXIMANT] [--psd PSD] [--f-low F_LOW]
[--delta-f DELTA_F] [--duration DURATION]
[--color-obs COLOR_OBS] [--color-model COLOR_MODEL]
[--plot-format PLOT_FORMAT] [--mpl-backend MPL_BACKEND]
output_dir M_total
Create slides for demonstrating how parameter estimation (PE) for gravitational
wave signals from compact binarie coalescences (CBCs) is performed, comparing
models to data.
positional arguments:
output_dir Directory to output files to. Will create directory if
it doesn't exist.
M_total True total mass.
optional arguments:
-h, --help show this help message and exit
--trial-masses TRIAL_MASSES [TRIAL_MASSES ...]
Sequence of trial M_total values to visualize.
--trial-masses-post-fit TRIAL_MASSES_POST_FIT [TRIAL_MASSES_POST_FIT ...]
Sequence of trial M_total values to visualize after
fitting.
--approximant APPROXIMANT
Waveform approximant model to use for signal and fit.
--psd PSD Name of PSD to use.
--f-low F_LOW Low frequency cutoff.
--delta-f DELTA_F Frequency bin size.
--duration DURATION Fix the plotted signal duration. Higher mass systems
should be set to a lower duration.
--color-obs COLOR_OBS
Color to use for observations.
--color-model COLOR_MODEL
Color to use for models.
--plot-format PLOT_FORMAT
File format used for plotting.
--mpl-backend MPL_BACKEND
Backend to use for matplotlib.
Example usage, fitting for a 50 solar mass binary black hole:
$ cbc_pe_demo output_dir 50.0 \
--trial-masses 40.0 60.0 45.0 55.0 \
--trial-masses-post-fit 48.0 52.0 49.0 51.0 50.0 \
--duration 0.2
Example
Running the example shown in the help message above, the following slides were produced.
Limitations
Currently only demonstrates zero-noise case, with all parameters assumed to be known except for total mass. Future developments will first add in noise, and eventually multiple dimensions may be used if it can be done clearly. Could also generalize to parameter estimation for arbitrary 1D problems.
Credit
Copyright (c) 2020 Daniel Wysocki
This project is free to distribute under the MIT License.
This was made possible by Python, NumPy, SciPy, and Matplotlib, and PyCBC.

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