Spey: smooth inference for reinterpretation studies

TL;DR

Spey is a versatile Python package that unifies various likelihood methods for hypothesis testing, enabling flexible, accurate, and interoperable reinterpretation studies with simplified likelihood prescriptions and meta-analysis capabilities.

Contribution

Introduces a flexible, cross-platform Python package that unifies likelihood prescriptions and simplifies the process of hypothesis testing and reinterpretation studies.

Findings

Outperforms previous approximation methods with asymmetric uncertainties.

Facilitates integration of diverse likelihood combination routines.

Enables seamless interoperability across different likelihood and hypothesis models.

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 utilise 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.