clustertools

clustertools is a Python package for analysing star cluster simulations. The package is built around the StarCluster class, which will store all the necessary information from a given star cluster simulation to be used for anaylsis. All functions within clustertools are then designed to act on a StarCluster. clustertools can be used for unit and coordinate transformations, the calculation of key structural and kinematic parameters, analysis of the cluster’s orbit and tidal tails (with the help of galpy , and measuring common cluster properties like its mass function, density profile, and velocity dispersion profile (among others). While originally designed with star clusters in mind, clustertools can be used to study other types of N-body systems, including stellar streams and dark matter sub-halos.

The package contains functions for loading data from commonly used N-body codes, generic snapshots, and codes for generating initial conditions.

clustertools is developed on Github. Please go to https://github.com/webbjj/clustertools to report issues or contribute to the code.

Supported N-Body Simulation Codes, Snapshot Formats, and Packages

AMUSE (Portegies Zwart S., McMillan S., 2018, Astrophysical Recipes; The art ofAMUSE, doi:10.1088/978-0-7503-1320-9)

ASTROPY_TABLE (Astropy Collaboration 2013, A&A, 558, A33)

GALPY (Bovy J., 2015, ApJS, 216, 29)

LIMEPY (Gieles, M. & Zocchi, A. 2015, MNRAS, 454, 576)

NBODY6 (Aarseth S. J., 2003, Gravitational N-Body Simulations)

NBODY6ppGPU (Wang L., Spurzem R., Aarseth S., Nitadori K., Berczik P., Kouwenhoven M. B. N., Naab T., 2015, MNRAS, 450, 4070)

NEMO (Teuben P., 1995, The Stellar Dynamics Toolbox NEMO. p. 398)

Version 1.0 (May 6, 2022)

Thanks to anyone that has been using clustertools while it was in devlopment. I am happy to announce that Version 1.0 has now officially been released. A couple of minor changes that are worth noting include:

– For consistancy purposes, all profile calls now return the true values of the radial bins and the calculated value. Pre-Version 1.0, some profile calls returned the natual logarithm of one (or both) values, and sometimes normalized by the cluster’s effective radius. Any other returned values are returned as per their definition. For example, delta_alpha is still calculated as d(alpha)/d(ln(r/r_m)) even though calling alpha_prof returns r and alpha.

load_cluster now requires units and origin to be set, with the exception of when ctype=nbody6 or ctype=nbody6pp. No assumptions are made regarding the format of incoming data

– The setup_cluster function has been deprecated. You can now initialize a Galactic cluster using load_cluster(ctype='limepy',gcname='Pal5'). If you wish to initialize a cluster with a specific lowered isothermal model, you must do so in limepy first and then call load_cluster accordingly.

– Previously when centreing the cluster to orthonormal coordinates, positions were in radians and velocities were converted to radians/year, but not actually moved to the cluster’s centre. Positions are now returned to degrees and velocities remain in mas/yr. Furthermore the velocities are shifted to be relative to the cluster’s centre

The above changes were made to ensure consistency accross clustertools. Now that Version 1.0 is official, I will work to ensure that any future changes will be compatabile with past versions of clustertools.

Guide

Example Notebooks

Acknowledging clustertools

If you use clustertools in a publiclication, please cite the following digital object identifier (DOI):

https://zenodo.org/badge/272233602.svg

and link to https://github.com/webbjj/clustertools.

For specific functions, please consult the list below for the appropriate reference (if applicable).

References

alpha_prof - Webb, J.J. & Vesperini, E. 2016, MNRAS, 463, 2383 (ADS)

core_relaxation_time - Stone, N.C. & Ostriker, J.P. 2015, ApJ, 806, 28 (ADS)

ckin - Bianchini, P. et al. 2018, MNRAS, 475, 96 (ADS)

cyl_coords ,``cart_to_cy``,``cyl_to_cart`` - Bovy J., 2015, ApJS, 216, 29 (ADS)

find_centre(density=True) (default) - Harfst, S., Gualandris, A., Merritt, D., et al. 2007, NewA, 12, 357 (ADS) or Casertano, S., Hut, P. 1985, ApJ, 298, 80 (ADS)

half_mass_relaxation_time - Spitzer, L. 1987, Dynamical evolution of globular clusters (ADS)

initialize_orbit, initialize_orbits, interpolate_orbit, interpolate_orbits - Bovy J., 2015, ApJS, 216, 29 (ADS)

load_cluster('limepy','gcname') (default) - Bovy J., 2015, ApJS, 216, 29 (ADS) - Gieles, M. & Zocchi, A. 2015, MNRAS, 454, 576 (ADS) - de Boer, T. J. L., Gieles, M., Balbinot, E., Hénault-Brunet, V., Sollima, A., Watkins, L. L., Claydon, I. 2019, MNRAS, 485, 4906 (ADS) - Vasiliev E., 2019, MNRAS, 484,2832 (ADS)

load_cluster('limepy',gcname', source='harris') - Harris, W.E. 1996 (2010 Edition), AJ, 112, 1487 - Bovy J., 2015, ApJS, 216, 29 (ADS) - Gieles, M. & Zocchi, A. 2015, MNRAS, 454, 576 (ADS) - Vasiliev E., 2019, MNRAS, 484,2832 (ADS)

meq_function, meq_prof - Bianchini, P. et al. 2016, MNRAS, 458, 3644 (ADS)

orbital_path, orbital_path_match - Bovy J., 2015, ApJS, 216, 29 (ADS)

relaxation_time - Spitzer, L. Jr, Hart, M.H. 1971, ApJ, 164, 399 (ADS)

rcore - Casertano, S., Hut, P. 1985, ApJ, 298, 80 (ADS)

rlimiting - Bovy J., 2015, ApJS, 216, 29 (ADS)

rtidal - Bertin, G. & Varri, A.L. 2008, ApJ, 689, 1005 - Bovy J., 2015, ApJS, 216, 29 (ADS) - Webb, J.J., Bovy, J., Carlberg, R.G., Gieles, M. 2019, MNRAS, 448, 4 (ADS)

tail_path, tail_path_match, to_tail - Bovy J., 2015, ApJS, 216, 29 (ADS)

to_centre(method='orthonormal') - GAIA Collaboration, 2018, A&A, 616, A12 (ADS)

to_centre(method='VandeVen') - van de Ven, G. 2005, PhD Thesis, Leiden University (ADS)

to_cluster(method='orthonormal') - GAIA Collaboration, 2018, A&A, 616, A12 (ADS)

to_cluster(method='VandeVen') - van de Ven, G. 2005, PhD Thesis, Leiden University (ADS)

to_sky,``sky_coords``,``cart_to_sky`` - Bovy J., 2015, ApJS, 216, 29 (ADS)

virial_radius(method='critical_density') - Bovy J., 2015, ApJS, 216, 29 (ADS)

virial_radius(method=inverse_distance) - Portegies Zwart S., McMillan S., 2018, Astrophysical Recipes; The art ofAMUSE, doi:10.1088/978-0-7503-1320-9 (ADS)

Contributers

Jo Bovy - https://github.com/jobovy Erik Gillis - https://github.com/e-gillis Nathaniel Starkman - https://github.com/nstarman

Library Reference

Planned Future Additions

  • Add PeTar to list of readable codes (in progress)

  • Allow for the use of astropy units

  • Be able to initialize orbits and clusters based on Holger Baumgardt’s Galactic Globular Clusters Database (https://people.smp.uq.edu.au/HolgerBaumgardt/globular/)

  • Incorporate other methods for estimating a cluster’s dynamical age (A+, Blue Stragglers)

  • More analysis features for binary stars

  • Allow for stars to be tagged as members of different subpopulations for the study of multiple populations

  • More analysis features that are comparable to standard techniques used by observers

  • Include options for planetary systems around individual stars

  • A simulation class that reads in all timesteps of a simulation