pygwb 1.4.0

Lighweight python stochastic gravitational-wave background analysis pipeline

pygwb

pygwb: A python-based, user-friendly library for gravitational-wave background (GWB) searches with ground-based interferometers.

pygwb provides a modular and flexible codebase to analyse laser interferometer data and design a GWB search pipeline. It is tailored to current ground-based interferometers: LIGO Hanford, LIGO Livingston, and Virgo, but can be generalized to other configurations. It is based on the existing packages gwpy and bilby, for optimal integration with widely-used GW data anylsis tools.

pygwb also includes a set of pre-packaged analysis scripts which may be used to analyse data and perform large-scale searches on a high-performance computing cluster efficiently.

Documentation

Installation instructions

• Essentials to support pygwb are present in live igwn conda environments https://computing.docs.ligo.org/conda/

• More precisely, current dependencies are

  • numpy
  • scipy>=1.8.0
  • matplotlib
  • corner
  • gwpy>=3.0.1
  • bilby>=1.4
  • astropy>=5.2
  • lalsuite>=7.3
  • gwdetchar
  • gwsumm
  • pycondor
  • loguru
  • json5
  • seaborn

  Modules

  The code is structured into a set of modules and objects.

  • detector.py: contains the Interferometer object. The Interferometer manages data reading, preprocessing, and PSD estimation.
  • baseline.py: contains the Baseline object. The Baseline is the core manager object in the stochastic analysis.
  • network.py: contains the Network object. The Network is used to combine results from indibidual Baselines as well as simulating data across an Interferometer network.
  • preprocessing.py: methods for initial data-conditioning steps (high-pass filter and downsampling) on data from an individual detector. Supports importing public, private, or local data.
  • spectral.py: methods to calculate CSDs and PSDs for sub-segments in a dataset, made of coincident time segments for a pair of detectors.
  • postprocessing.py: methods to combine individual segment cross-correlation spectrograms into a final spectrum, in units of fractional energy density.
  • omega_spectra.py: contains the OmegaSpectrum and OmegaSpectrogram objects.
  • pe.py: contains model objects to perform pe with Bilby.
  • statistical_checks.py: Contains the StatisticalChecks object, and methods to run statistical checks on results from an analysis run.
  • simulator.py: Contains the Simulator object, which can simulate data for a set of detectors.
  • delta_sigma_cut.py: Methods to perform the delta-sigma data quality cut.
  • notch.py: Contains the StochNotchand StochNotchList objects, which store information about frequency notches to be applied to the analyzed data spectra.
  • constants.py: contains numerical values of constants used throughout the codebase.
  • orfs.py: Methods to calcuate overlap reduction functions.
  • parameters.py: Contains the Parameters dataclass.
  • util.py: contains miscellaneous useful functions used throughout the codebase.

  Scripts

  A set of scripts are included and maintained to run every-day stochastic tasks.

  • pygwb_pipe: runs the cross-correlation stochastic analysis over data from selected detector pair, within the timeframes requested.
  • pygwb_combine: combines over multiple pygwb_pipe output files. Useful when running long analyses in parallel.
  • pygwb_pe: runs parameter estimation on desired model.
  • pygwb_stats: produces regular statistical checks output.
  • pygwb_dag: supports the creation of a dag file for condor job submission.