spops

Data release supporting:

• Spin orientations of merging black holes formed from the evolution of stellar binaries. Davide Gerosa, Emanuele Berti, Richard O’Shaughnessy, Krzysztof Belczynski, Michael Kesden, Daniel Wysocki, Wojciech Gladysz. Physical Review D 98 (2018) 084036. arXiv:1808.02491 [astro-ph.HE].
• Multiband gravitational-wave event rates and stellar physics. Davide Gerosa, Sizheng Ma, Kaze W.K. Wong, Emanuele Berti, Richard O’Shaughnessy, Yanbei Chen, Krzysztof Belczynski. arXiv:1902.00021 [astro-ph.HE].

Here provide our database and a short python code to query it.

Credit

You are more than welcome to use our database in your research; we kindly ask you to cite our papers above. For questions or bugs, just ask me. Oh, and before you ask, SPopS means “Spinning black-hole binary POPulation Synthesis”. If you want to cite the database specifically, it’s

Examples

To access the effective spin distribution and the LIGO detection rates of one specific model with python:

import spops
db=spops.database()
model = {"kicks":"70", "spins":"collapse", "tides":"time", "detector":"LIGO"}
var='chieff'
print(db(model,var))  
var='detectionrate'
print(db(model,var))

For the same population synthesis simulation, this is the mass ratio of black hole binaries detectable by a multiband LISA+CosmicExplorer network

model = {"kicks":"70", "spins":"collapse", "tides":"time", "detector":"LISACosmicExplorer", "Tobs":"10", "SNRthr":"8"}
var='q'
print(db(model,var))
var='detectionrate'
print(db(model,var))

Database

We provide a database in h5 format containing all population sysnthesis distributions perfomed with StarTrack and post-processed with precession.

The database’s size is ~17GB, and needs to be downloaded in chunks from the GitHub release page. Execute the following:

for i in $(seq -f "%02g" 0 16); do
wget "https://github.com/dgerosa/spops/releases/download/v0.2/spops.h5_"$i;
done
cat spops.h5_* > spops.h5; rm spops.h5_*

(on mac: `brew install wget’).

Models are described by the following options:

• kicks. Magnitude of the Supernova kicks. Available options are ['0','25','50','70','130','200','265'].
• spins. Prescription for the spin magnitudes. Available options are ['collapse','max','uniform'].
• tides. Prescription for tidal spin alignment. Available options are ['time','alltides','notides'].
• detector. Targeted detector or multiband network. Available options are ['LIGO','Voyager','CosmicExplorer','LISA','LISALIGO','LISACosmicExplorer'].
• Tobs. Duration of the LISA mission in yrs. Available options are ['4','10].
• SNRthr. LISA SNR threshold. Available options are ['4','8].

Not all options are required for all the variables (for instance, LISA duration does not need to be specified to access ground-only information).

The following variables are available:

• Mzams_a: ZAMS mass of the heavier star (solar masses).
• Mzams_b: ZAMS mass of the lighter star (solar masses).
• M_a: Mass of the black hole formed by the heavier ZAMS star (solar masses).
• M_b: Mass of the black hole formed by the lighter ZAMS star (solar masses).
• zmer: Merger redshift.
• met: Metallicity.
• path: Formation pathway, convention in Sec. 2D of arXiv:1808.02491.
• tidealign: boolean flag marking if the stellar spin was realigned by tides.
• M: Black-hole binary total mass (solar masses).
• q: Black-hole binary mass ratio.
• chi1: Spin of the heavier black hole.
• chi2: Spin of the lighter black hole.
• chieff: Black-hole binary effective spin
• morph: Spin morphology (-1: Librating about 0, 0: Circulating, +1: Librating about pi)
• theta1: Tilt angle of the heavier black hole at 20 Hz.
• theta2: Tilt angle of the heavier black hole at 20 Hz.
• deltaphi: Difference between the azimuthal spin angles at 20 Hz.
• SNR: Signal-to-noise ratio. For ground-based detector returns the optimal SNR. For LISA returns SNR evaluated at Tobs.
• detectionrate: Detection rate for the targeted detector.

Python module

We also provide a simple python module to query the database. spops is compatible with both Python 2 and Python 3 and can be installed from the Python Package index using:

pip install spops

Remember to download and assemble the database as described above. The module contains a single class, called database. To initialize the class:

import spops
db = spops.database(h5filename='spops.h5',h5dir=None)

The input parameters are:

• h5filename: database file name, default is spops.h5.
• h5dir: directory of the database; if None (default) the code will look for detabase in both the location where the spops module is installed (this is os.path.dirname(os.path.abspath(spops.__file__))) and the execution location (this is .).

The population sysnthesis run of interest can be specified using a python dictionary with the keys as above, so for instance

model = {"kicks":"70", "spins":"collapse", "tides":"time", "detector":"LIGO"}

One can then access the datasets described above by just calling the database class:

var='chieff'
print(db(model,var))

To list the model options and the available variables use

print(db.options)
print(db.vars)

A few technical notes:

• The database class is a singleton: only one istance can exist at any time. Multiple calls will return pointers to the same instance. This is done to prevent useless memory allocation. For instance:

  db1=spops.database()
  db2=spops.database()
  print(db1==db2)
  
  
  >>>> True
  

• We do lazy loading here and only read in a dataset when/if the user asks for it. Moreover, spops remembers which dataset have already been loaded, such that each subsequent access is read in from memory, not disk. So for instance:

  from contexttimer import timer
  @timer()
  def read_from_spops(model,var):
      return db(model,var)
  var='Mzams_a'
  read_from_spops(model,var)
  read_from_spops(model,var)
  
  >>>> function read_from_spops execution time: 0.002
  >>>> function read_from_spops execution time: 0.000
  

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