Welcome to BayesWave’s documentation!

A Bayesian algorithm designed to robustly distinguish gravitational wave signals from noise and instrumental glitches without relying on any prior assumptions of waveform morphology. BayesWave produces posterior probability distributions directly on gravitational wave signals and instrumental glitches, allowing robust, morphology-independent waveform reconstruction. See arXiv:1410.3835 for an introduction to the BayesWave algorithm. LSC/Virgo members: See the BayesWave wiki for further info.

General Info:

• Installation
  • Option 1: Running on the IGWN Pool
  • Option 2: Conda forge
  • Option 3: Installing from source
• Use of BayesWave in Scientific Publications
  • License
  • Citing BayesWave
    • Citing the scientific publications that describe the algorithms
    • Citing the BayesWave Software
• BayesWave Workflow
  • bayeswave_pipe
  • BayesWave
  • BayesWavePost
  • megaplot.py
  • megasky.py
• Algorith Overview
  • Basis functions
  • Reversible jump Markov chain Monte Carlo
    • Signal model
    • Glitch model
    • Joint model
    • CBC Model
    • Model Selection
  • Power Spectral Density Estimation
• Setting up a Run
  • Config files
    • [input]
    • [engine]
    • [datafind]
    • [bayeswave_options]
      • Run parameters
      • Model parameters
      • Priors and Proposals
      • Parallel Tempering parameters
      • LALInference injection options
      • Burst MDC injection options
      • BayesWave internal injection bayeswave_options
    • [bayeswave_post_options]
    • [injections]
    • [condor]
  • Running bayeswave_pipe
    • positional arguments
    • options
  • Launching the BayesWave Job
    • Using Condor
    • Without Condor
• Using BayesWave Output
  • BayesWave
    • Chain files
    • Evidence files
    • PSDs
  • BayesWavePost
    • Waveform Reconstructions
    • Waveform Moments and other Characteristics
    • Q-Scans
    • Injections
  • Megaplot and Output Pages

Examples:

• Simulated signal from xml file
  • Example 1: Making an XML file and injecting into simulated noise
    • Making an XML file
    • Config file
    • Call to bayeswave_pipe
  • Option 2: Injecting into real data
    • Config file
    • Call to bayeswave_pipe
• Real Data
  • GW150914
    • Config file
    • Call to bayeswave_pipe
• Burst style MDCs
  • Making an MDC cache
  • Injecting into real noise
    • Config file
    • Call to bayeswave_pipe
  • Injecting into simulated data
    • Config file
    • Call to bayeswave_pipe

Indices and tables

• Index

• Module Index

• Search Page