WF4Py

User-friendly package implementing GW waveform models in pure python, thus enabling parallelization over multiple events at a time. All the waveforms are accurately checked with their implementation in LALSuite.

Developed by Francesco Iacovelli.

This package is released together with the papers arXiv:2207.02771 and arXiv:2207.06910, where detail of implementations and results can be found.

Code Organization

The organisation of the repository is the following:

WF4Py/WF4Py/
     ├── waveforms.py
            Import of the waveform models in the folder 'waveform_models/' for ease of use
     ├── waveform_models/
         	Core: implementation of various GW waveform models present in
				LALSimulation in pure Python
     ├── WFutils.py
			Auxiliary functions: constants, conversions,
				spherical harmonics and parameter checks
     ├── WFfiles
    		Folder containing some text files needed for waveform computation
WF4Py/
	├── WF4Py_tutorial.ipynb
		Jupyter notebook with tutorial for the usage
WF4Py/docs/ 
		Code documentation in Sphinx

Summary

• Documentation
• Installation
• Overview and usage
• Available models
• Testing
• Citation
• Bibliography

Documentation

WF4Py has its documentation hosted on Read the Docs here, and it can also be built from the docs directory.

Installation

To install the package without cloning the git repository simply run

pip install git+https://github.com/CosmoStatGW/WF4Py

Overview and usage

Each waveform is a derived of the abstract class WaveFormModel, and has built in functions Phi, Ampl, tau_star and fcut to compute the phase, amplitude, time to coalescence and cut frequency, respectively for the chosen catalog of events.

The functions accept as inputs the catalog as well as the frequencies at at which the computation has to be performed.

The catalog has to be a dictionary containing the parameters of the events, such as

events = {'Mc':np.array([...]),
          'dL':np.array([...]),
          'iota':np.array([...]),
          'eta':np.array([...]),
          'chi1z':np.array([...]),
          'chi2z':np.array([...]),
          'Lambda1':np.array([...]),
          'Lambda2':np.array([...])}

where Mc denotes the chirp mass (in units of solar masses), dL the luminosity distance (in Gpc), iota the inclination angle, eta the symmetric mass ratio, chi1z and chi2z the adimensional spin components of the two objects aligned with the orbital angular momentum and Lambda1 and Lambda2 the adimensional tidal deformability parameters of the objects in the tidal case.

Any waveform can then be easily used e.g. as

waveforms.IMRPhenomD().Ampl(fgrids, **events)

For a detailed tutorial refer to WF4Py_tutorial.ipynb

Available models

• (v1.0.0) TaylorF2_RestrictedPN (1., 2., 3., 4., 5.)
• (v1.0.0) IMRPhenomD (6., 7.)
• (v1.0.0) IMRPhenomD_NRTidalv2 (6., 7., 8.)
• (v1.0.0) IMRPhenomHM (9., 10.)
• (v1.0.0) IMRPhenomNSBH (8., 11.)
• (v1.0.0) IMRPhenomXAS (12.)

Testing

The adherence of all the models with their implementation in LALSuite is accuratly tested. As an example, we here report the comparison in the implementations of IMRPhenomXAS

Citation

If using this software, please cite this repository and the papers arXiv:2207.02771 and arXiv:2207.06910. Bibtex:

@article{Iacovelli:2022bbs,
    author = "Iacovelli, Francesco and Mancarella, Michele and Foffa, Stefano and Maggiore, Michele",
    title = "{Forecasting the Detection Capabilities of Third-generation Gravitational-wave Detectors Using GWFAST}",
    eprint = "2207.02771",
    archivePrefix = "arXiv",
    primaryClass = "gr-qc",
    doi = "10.3847/1538-4357/ac9cd4",
    journal = "Astrophys. J.",
    volume = "941",
    number = "2",
    pages = "208",
    year = "2022"
}

@article{Iacovelli:2022mbg,
    author = "Iacovelli, Francesco and Mancarella, Michele and Foffa, Stefano and Maggiore, Michele",
    title = "{GWFAST: A Fisher Information Matrix Python Code for Third-generation Gravitational-wave Detectors}",
    eprint = "2207.06910",
    archivePrefix = "arXiv",
    primaryClass = "astro-ph.IM",
    doi = "10.3847/1538-4365/ac9129",
    journal = "Astrophys. J. Supp.",
    volume = "263",
    number = "1",
    pages = "2",
    year = "2022"
}

Bibliography

1. A. Buonanno et al. (2009) arXiv:0907.0700
2. P. Ajith (2011) arXiv:1107.1267
3. C. K. Mishra et al. (2016) arXiv:1601.05588
4. L. Wade et al. (2014) arXiv:1402.5156
5. B. Moore et al. (2016) arXiv:1605.00304
6. S. Husa et al. (2015) arXiv:1508.07250
7. S. Khan et al. (2015) arXiv:1508.07253
8. T. Dietrich et al. (2019) arXiv:1905.06011
9. L. London et al. (2018) arXiv:1708.00404
10. C. Kalaghatgi et al. (2019) arXiv:1909.10010
11. F. Pannarale et al. (2015) arXiv:1509.00512
12. G. Pratten et al. (2020) arXiv:2001.11412