PycWB: A User-friendly, Modular, and Python-based Framework for Gravitational Wave Unmodelled Search
arXiv:2308.08639 · doi:10.1016/j.softx.2024.101639
Abstract
Unmodelled searches and reconstruction is a critical aspect of gravitational wave data analysis, requiring sophisticated software tools for robust data analysis. This paper introduces PycWB, a user-friendly and modular Python-based framework developed to enhance such analyses based on the widely used unmodelled search and reconstruction algorithm Coherent Wave Burst (cWB). The main features include a transition from C++ scripts to YAML format for user-defined parameters, improved modularity, and a shift from complex class-encapsulated algorithms to compartmentalized modules. The pycWB architecture facilitates efficient dependency management, better error-checking, and the use of parallel computation for performance enhancement. Moreover, the use of Python harnesses its rich library of packages, facilitating post-production analysis and visualization. The PycWB framework is designed to improve the user experience and accelerate the development of unmodelled gravitational wave analysis.
16 pages, 4 figures
References in corpus (23)
- TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
- HEALPix -- a Framework for High Resolution Discretization, and Fast Analysis of Data Distributed on the Sphere
- GWTC-1: A Gravitational-Wave Transient Catalog of Compact Binary Mergers Observed by LIGO and Virgo during the First and Second Observing Runs
- GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run
- GW190521: A Binary Black Hole Merger with a Total Mass of
- Bilby: A user-friendly Bayesian inference library for gravitational-wave astronomy
- The PyCBC search for gravitational waves from compact binary coalescence
- GW150914: First results from the search for binary black hole coalescence with Advanced LIGO
- Method for detection and reconstruction of gravitational wave transients with networks of advanced detectors
- Open data from the third observing run of LIGO, Virgo, KAGRA and GEO
- PyCBC Inference: A Python-based parameter estimation toolkit for compact binary coalescence signals
- Enhancing Gravitational-Wave Science with Machine Learning
- Search for Eccentric Binary Black Hole Mergers with Advanced LIGO and Advanced Virgo during their First and Second Observing Runs
- Coherent WaveBurst, a pipeline for unmodeled gravitational-wave data analysis
- Search for intermediate mass black hole binaries in the third observing run of Advanced LIGO and Advanced Virgo
- All-sky search for short gravitational-wave bursts in the third Advanced LIGO and Advanced Virgo run
- SWIGLAL: Python and Octave interfaces to the LALSuite gravitational-wave data analysis libraries
- Search for nonlinear memory from subsolar mass compact binary mergers
- Impact of eccentricity on the gravitational wave searches for binary black holes: High mass case
- Observing an intermediate mass black hole GW190521 with minimal assumptions
- pygwb: Python-based library for gravitational-wave background searches
- An autoencoder neural network integrated into gravitational-wave burst searches to improve the rejection of noise transients
- Utilizing Gaussian mixture models in all-sky searches for short-duration gravitational wave bursts