stanhf: HistFactory models in the probabilistic programming language Stan

In collider physics, experiments are often based on counting the numbers of events in bins of a histogram. We present a new way to build and analyze statistical models that describe these experiments, based on the probabilistic programming language Stan and the HistFactory specification. A command-l...

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Veröffentlicht in:The European physical journal. C, Particles and fields Jg. 85; H. 8; S. 923 - 12
1. Verfasser: Fowlie, Andrew
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 30.08.2025
Springer Nature B.V
SpringerOpen
Schlagworte:
Astronomy
Astrophysics and Cosmology
Elementary Particles
Hadrons
Heavy Ions
High energy physics
Measurement Science and Instrumentation
Metadata
Nuclear Energy
Nuclear Physics
Physics
Physics and Astronomy
Probability theory
Programming languages
Python
Quantum Field Theories
Quantum Field Theory
Regular Article - Computing
Software and Data Science
Specifications
Statistical analysis
Statistical models
String Theory
ISSN:1434-6052, 1434-6044, 1434-6052
Online-Zugang:Volltext
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Zusammenfassung:In collider physics, experiments are often based on counting the numbers of events in bins of a histogram. We present a new way to build and analyze statistical models that describe these experiments, based on the probabilistic programming language Stan and the HistFactory specification. A command-line tool transpiles HistFactory models into Stan code and data files. Because Stan is an imperative language, it enables richer and more detailed modeling, as modeling choices defined in the HistFactory declarative specification can be tweaked and adapted in the Stan language. Stan was constructed with automatic differentiation, allowing modern computational algorithms for sampling and optimization.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1434-6052
1434-6044
1434-6052
DOI:10.1140/epjc/s10052-025-14495-1