inspiral-range 0.3.0

GW detector inspiral range calculation tools

GW detector inspiral range calculation tools

The inspiral_range package provides tools for calculating various binary inspiral range measures useful as figures of merit for gravitational wave detectors characterized by a strain noise spectral density.

It includes a command-line tool for calculating various inspiral ranges from a supplied file of detector noise spectral density (either ASD or PSD).

See the following references for more information:

• https://dcc.ligo.org/LIGO-P1600071 (https://arxiv.org/abs/1709.08079)
• https://dcc.ligo.org/LIGO-T1500491
• https://dcc.ligo.org/LIGO-T1100338
• https://dcc.ligo.org/LIGO-T030276

Authors:

• Jameson Rollins [e-mail]
• Jolien Creighton [e-mail]

Dependencies

inspiral_range depends on a couple of non-standard packages for cosmology calculations and waveform generation, as well as scipy for various optimization routines. These dependencies can be met in a couple different ways, but please note the caveats described below.

The LAL dependencies (both lal and lalsimulation) can be found in the lalsuite dependency in pip.

cosmology

inspiral_range needs to calculate luminosity distance and differential comoving volume as a function of redshift, which requires specifying a cosmology model. Two different packages are supported for these cosmological calculations:

• lal: not pure python so not as accessible, but much faster
• astropy: pure python and widely available, but slow

waveform generation

inspiral_range needs the amplitude of strain waveforms of binary inspiral signals in order to calculate the detection SNR for a given detector noise spectrum. Included in the package is a cached interpolant for equal mass, non-spinning BBH systems. For range calculations for other systems (BNS, non-equal mass, including spin) the lalsimulation package is required for waveform calculation.

Usage

library usage

The package includes multiple functions for calculating various range measures for a given PSD.

Analytical methods:

• sensemon_range
• int73

Cosmologically-corrected measures (see arxiv:1709.08079):

• horizon (Mpc)
• horizon_redshift (z)
• volume (Mpc^3)
• range (Mpc)
• response_frac (Mpc)
• response_frac_redshift (z)
• reach_frac (Mpc)
• reach_frac_redshift (z)

By default, all functions calculate measures for 1.4/1.4 M_sol BNS inspirals:

>>> import inspiral_range
>>> freq, psd = np.loadtxt('PSD.txt')
>>> range_bns = inspiral_range.range(freq, psd)

Other masses can be calculated as well:

>>> range_bbh = inspiral_range.range(freq, psd, m1=30, m2=30)

When calculating multiple measures together it is more efficient to generate the fiducial waveform first and then pass it to the various functions:

>>> H = inspiral_range.CBCWaveform(freq, m1=30, m2=30)
>>> horizon = inspiral_range.horizon(freq, psd, H=H)
>>> range = inspiral_range.range(freq, psd, H=H)

The cosmology being used can be modified with the Cosmology class:

>>> cosmo = inspiral_range.Cosmology(h=70.0)
>>> H = inspiral_range.CBCWaveform(freq, cosmo=cosmo)

The 'cosmo' parameter can also be supplied directly to the range calculator:

>>> range = inspiral_range.range(freq, psd, cosmo=cosmo)

A convenience function all_ranges is included that calculates most of the range metrics together in an efficient way (all return values in Mpc):

• horizon
• range
• response_50
• response_10
• reach_50
• reach_90
• sensemon_range
• sensemon_horizon

e.g.:

>>> metrics, H = inspiral_range.all_ranges(freq, psd)

command line interface

The package also include a command line interface for easily calculating ranges for a given ASD or PSD specified in a two-column text file:

$ python3 -m inspiral_range -p PSD.txt m1=30 m2=30

Tests

The packages includes a test interface that can compare the range calculations from the current source for various canonical detector ASDs against those calculated from an earlier git commit:

$ python3 -m inspiral_range.test --help