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POPxf: An Exchange Format for Polynomial Observable Predictions

Ilaria Brivio, Ken Mimasu, Peter Stangl, Anke Biekötter, Ana R. Cueto Gómez, Charlotte Knight, Luca Mantani, Eleonora Rossi, Alejo N. Rossia, Aleks Smolkovič

TL;DR

POPxf provides a standardized, machine-readable format for publishing observable predictions that can be expressed as polynomials in model parameters, with a particular emphasis on EFT applications. The approach supports both SP and FOP use cases, accommodates arbitrary polynomial degrees, and encodes central values, uncertainties, and correlations in a structured JSON (or HDF5) representation, including explicit metadata to ensure reproducibility. By formalizing polynomial expansions, parameter-dependent uncertainties, and separate correlation files, POPxf enables straightforward reuse in global fits, reinterpretations, and cross-community validation, thereby reducing duplication of effort. The format is backed by a public repository with validators and examples, aiming to streamline the publication and exchange of theoretical predictions across the particle physics community.

Abstract

We introduce the Polynomial Observable Prediction Exchange Format, POPxf, a structured, machine-readable data format for the publication and exchange of semi-analytical theoretical predictions in high energy physics. The format is designed to encode observables that can be expressed in terms of polynomials in model parameters, with particular emphasis on Effective Field Theory applications. All relevant assumptions and metadata are recorded explicitly, and the treatment of uncertainties and correlations is flexible enough to capture parameter-dependent effects. The format aims to improve reproducibility, facilitate global fits and reinterpretations, and streamline the use of theoretical predictions across the particle physics community.

POPxf: An Exchange Format for Polynomial Observable Predictions

TL;DR

POPxf provides a standardized, machine-readable format for publishing observable predictions that can be expressed as polynomials in model parameters, with a particular emphasis on EFT applications. The approach supports both SP and FOP use cases, accommodates arbitrary polynomial degrees, and encodes central values, uncertainties, and correlations in a structured JSON (or HDF5) representation, including explicit metadata to ensure reproducibility. By formalizing polynomial expansions, parameter-dependent uncertainties, and separate correlation files, POPxf enables straightforward reuse in global fits, reinterpretations, and cross-community validation, thereby reducing duplication of effort. The format is backed by a public repository with validators and examples, aiming to streamline the publication and exchange of theoretical predictions across the particle physics community.

Abstract

We introduce the Polynomial Observable Prediction Exchange Format, POPxf, a structured, machine-readable data format for the publication and exchange of semi-analytical theoretical predictions in high energy physics. The format is designed to encode observables that can be expressed in terms of polynomials in model parameters, with particular emphasis on Effective Field Theory applications. All relevant assumptions and metadata are recorded explicitly, and the treatment of uncertainties and correlations is flexible enough to capture parameter-dependent effects. The format aims to improve reproducibility, facilitate global fits and reinterpretations, and streamline the use of theoretical predictions across the particle physics community.

Paper Structure

This paper contains 39 sections, 29 equations, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. General Formalism
  3. Data Files
  4. Polynomials
  5. Observable Predictions
  6. Observable Uncertainties and Correlations
  7. Parameter-independent Uncertainties and Correlations
  8. Parameter-dependent Uncertainties and Correlations
  9. Overview of the POPxf JSON Format
  10. Top-Level Structure
  11. Two Modes of Use
  12. Structure of metadata
  13. Structure of data
  14. Specification of Fields in the POPxf JSON Format
  15. $schema Field
  16. ...and 24 more sections

Figures (2)

  • Figure 1: Graphical representation of the JSON structure of the POPxf data format for the two top-level fields, metadata and data. Coloured boxes indicate required fields (blue and green for SP and FOP modes, respectively, orange for both).
  • Figure 2: Structure of POPxf correlation file in JSON (left) and HDF5 (right).