surfinBH 0.0.5.dev1

Surrogate Final BH properties.

Welcome to surfinBH!

surfinBH provides fits for Surrogate Final Black Hole properties from mergers of binary black holes (BBH). Just like Point Break, but with black holes! This package lives on GitHub.

These fits are described in the following papers:

[1] Vijay Varma, Davide Gerosa, Francois Hebert and Leo C. Stein, in preparation.

If you find this package useful in your work, please cite reference [1] and, if available, the relevant paper describing the particular model.

Installation

PyPi

surfinBH is available through PyPi.

pip install surfinBH

From source

git clone https://github.com/vijayvarma392/surfinBH
cd surfinBH
python setup.py install

If you do not have root permissions, replace the last step with python setup.py install --user

Dependencies

All of these can be installed through pip or conda.

• numpy
• scipy
• scikit-learn (at least 0.19.1)
• h5py

Usage

import surfinBH

See list of available fits

print(surfinBH.fits_collection.keys())
>>> ['surfinBH3dq8', 'surfinBH7dq2']

Pick your favorite fit and get some basic information about it.

fit_name = 'surfinBH7dq2'
surfinBH.fits_collection[fit_name].desc
>>> 'Fits for remnant mass, spin and kick veclocity for generically precessing BBH systems.'

surfinBH.fits_collection[fit_name].refs
>>> 'Varma:2018_inprep'

surfinBH.fits_collection[fit_name].refs_url
>>> 'arxiv.2018.xxxx'

Get data for the fit. This only needs to done once, ever.

surfinBH.DownloadData(fit_name)
>>> fit_7dq2.h5  100%[======================>]  42.85M  495KB/s  in 60s

Load the fit. This only needs to be done once at the start of your script.

fit = surfinBH.LoadFits(fit_name)
>>> Loaded surfinBH7dq2 fit.

The evaluation of each fit is different, so be sure to read the documentation. This also defines the frames in which different quantities are defined.

help(fit)

Evaluate the fit. Here we show the evaluation for the surfinBH7dq2 model.

q = 1.2
chiA = [0.1, 0.2, 0.3]
chiB = [0.2, -0.5, 0.3]
x = [q] + chiA + chiB

print(x)
>>> [1.2, 0.1, 0.2, 0.3, 0.2, -0.5, 0.3]

# Final mass and its 1-sigma error etimate
mC, mC_err_est = fit('mC', x)

# Final spin vector and its 1-sigma error estimate
chiC, chiC_err_est = fit('chiC', x)

# Final kick vector and its 1-sigma error estimate
velC, velC_err_est = fit('velC', x)

Credits

The code is developed and maintained by Vijay Varma. Please, report bugs to [e-mail].