Spey: Smooth inference for reinterpretation studies

Araz, Jack Y.

Abstract

Statistical models serve as the cornerstone for hypothesis testing in empirical studies. This paper introduces a new cross-platform Python-based package designed to utilize different likelihood prescriptions via a flexible plug-in system. This framework empowers users to propose, examine, and publish new likelihood prescriptions without developing software infrastructure, ultimately unifying and generalising different ways of constructing likelihoods and employing them for hypothesis testing within a unified platform. We propose a new simplified likelihood prescription, surpassing previous approximation accuracies by incorporating asymmetric uncertainties. Moreover, our package facilitates the integration of various likelihood combination routines, thereby broadening the scope of independent studies through a meta-analysis. By remaining agnostic to the source of the likelihood prescription and the signal hypothesis generator, our platform allows for the seamless implementation of packages with different likelihood prescriptions, fostering compatibility and interoperability

Citation

Araz, J. Y. (2024). Spey: Smooth inference for reinterpretation studies. SciPost Physics, 16(1), Article 032. https://doi.org/10.21468/scipostphys.16.1.032

• Journal Article Type: Article
• Acceptance Date: Jan 8, 2024
• Online Publication Date: Jan 26, 2024
• Publication Date: 2024-01
• Deposit Date: Apr 2, 2024
• Publicly Available Date: Apr 2, 2024
• Journal: SciPost Physics
• Print ISSN: 2542-4653
• Electronic ISSN: 2542-4653
• Publisher: SciPost
• Peer Reviewed: Peer Reviewed
• Volume: 16
• Issue: 1
• Article Number: 032
• DOI: https://doi.org/10.21468/scipostphys.16.1.032
• Keywords: General Physics and Astronomy
• Public URL: https://durham-repository.worktribe.com/output/2367608