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Spey: smooth inference for reinterpretation studies
by Jack Y. Araz
This Submission thread is now published as
Submission summary
| Authors (as registered SciPost users): | Jack Araz |
| Submission information | |
|---|---|
| Preprint Link: | https://arxiv.org/abs/2307.06996v3 (pdf) |
| Date accepted: | 2024-01-08 |
| Date submitted: | 2023-12-19 13:58 |
| Submitted by: | Araz, Jack |
| Submitted to: | SciPost Physics |
| Ontological classification | |
|---|---|
| Academic field: | Physics |
| Specialties: |
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| Approaches: | Theoretical, Computational, Phenomenological |
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.
List of changes
* Abstract has been updated.
* The introduction has been updated.
* Eq 2 and the discussion around it has been updated.
* An example has been added at the end of section 2.
Published as SciPost Phys. 16, 032 (2024)