Description

v0.0.2 available soon | currently under development (use template-powerlaw or agnostic branch for now)

"Discovered during the 1898 excavation of the tomb of Pa-di-Imen in Saqqara, Egypt, the SAQQARA bird artifact is dated to about 200 BCE and is of unresolved origin."

• SAQQARA is a Simulation-based Inference (SBI) library designed to perform analysis on stochastic gravitational wave (background) signals (SGWB). It is built on top of the swyft code, which implements neural ratio estimation to efficiently access marginal posteriors for all parameters of interest.

• Related paper: The details regarding the implementation of the TMNRE algorithm and the application to agnostic and template-based SGWB searches (in the presence of sub-threshold transients) is in: arxiv:2309.07954.

• Key benefits: We show in the above paper a proof-of-principle for simulation-based inference combined with implicit marginalisation (over nuisance parameters) to be very well suited for SGWB data analysis. Our results are additionally validated via comparison to traditional, likelihood-based algorithms.

• Contacts: For questions and comments on the code, please contact either James Alvey, Uddipta Bhardwaj, or Mauro Pieroni. Alternatively feel free to open a github issue.

• Citation: If you use SAQQARA in your analysis, or find it useful, we would ask that you please use the following citation.

@article{Alvey:2023npw,
    author = "Alvey, James and Bhardwaj, Uddipta and Domcke, Valerie and Pieroni, Mauro and Weniger, Christoph",
    title = "{Simulation-based inference for stochastic gravitational wave background data analysis}",
    eprint = "2309.07954",
    archivePrefix = "arXiv",
    primaryClass = "gr-qc",
    reportNumber = "CERN-TH-2023-167",
    month = "9",
    year = "2023"
}

• e.g. reconstruction of SGWB using SAQQARA.

Available Branches:

• template-powerlaw - SGWB search using a powerlaw template
• agnostic - agnostic SGWB search

Release Details:

• v0.0.1 | September 2023 | Public release matching companion paper:
  • Simulation-based inference for stochastic gravitational wave background data analysis