beansp 2.25.0

Bayesian Estimation of Accreting Neutron Stars parameters

BEANSp

Bayesian Estimation of Accreting Neutron Star parameters

• Free software: MIT license

• Documentation: https://beans-7.readthedocs.io/en/latest/

• Repo: https://github.com/adellej/beans

Features

This software uses a Markov Chain Monte Carlo approach to match observations of an accreting neutron star in outburst with a simple ignition model to constrain parameters including the neutron star mass, radius, surface gravity, distance and system inclination, and accreted fuel composition.

The code is written in Python 3, except for settle which is a C++ code with a python wrapper. It makes use of Dan Foreman-Mackey’s python implementation of MCMC, emcee, available at https://github.com/dfm/emcee.

Credits

Software written by Adelle Goodwin and Duncan Galloway; for a full description see Goodwin et al. (2019, https://doi.org/10.1093/mnras/stz2638 or preprint at https://arxiv.org/pdf/1907.00996).

The original algorithm is described in Galloway & Cumming (2006, https://iopscience.iop.org/article/10.1086/507598).

pySettle was forked from the original settle written by Andrew Cumming and available at https://github.com/andrewcumming/settle

Package installation and usage

BEANSp is on pyPI (https://pypi.org/project/beansp/) so installation is easy - either system-wide, or in virtual environment:

pip install beansp

You can then import the main Beans module as follows:

from beansp import Beans

(Please refer to this simple test script as an example.)

Build and installation from this github repository

Please refer to build instructions.

History

0.1.0 (2019-09-19)

• First release on PyPI.