Physics:Quantum data analysis/Software and Data Used in This Book
Software and Data Used in This Book is a topic in particle-physics data analysis. Software and data used in this book refers to the practical ecosystem needed to reproduce particle-physics exercises and examples. The central idea is to keep a clear chain from input events to histograms, tables, plots, selections, uncertainties, and final interpretations. In a real analysis this chain includes public datasets or simulated samples, documented code, versioned configurations, and enough metadata to understand what each event record represents. Particle-physics datasets commonly store reconstructed particles, detector-level objects, generated particles, weights, trigger decisions, and event-level metadata. Analyses reduce these objects into derived variables, selected event samples, histograms, efficiencies, and covariance information.
Data objects
Particle-physics datasets commonly store reconstructed particles, detector-level objects, generated particles, weights, trigger decisions, and event-level metadata. Analyses reduce these objects into derived variables, selected event samples, histograms, efficiencies, and covariance information.[1]
Reproducibility
Reproducible analysis depends on stable software versions, deterministic event selections, documented correction factors, and clear separation between raw inputs and derived outputs. This is especially important when results are compared with Monte Carlo predictions or combined with other measurements.[2][3]
Learning workflow
For teaching, a useful workflow starts with small event samples and transparent scripts before moving to larger frameworks. The goal is to show how detector-level records become physical statements such as cross sections, particle properties, or exclusion limits.[4]
Overview
Software and Data Used in This Book is used in particle-physics data analysis to turn detector output, simulated samples, and theoretical models into quantitative physics results. In high-energy experiments the term is connected with event selection, calibration, uncertainty treatment, validation, and comparison with Standard Model or beyond-Standard-Model predictions.
Analysis role
The analysis task is usually defined by the observable being measured or the signal being searched for. A robust workflow keeps raw detector information, reconstructed objects, simulated events, control samples, and statistical models traceable so that assumptions can be checked and systematic uncertainties can be propagated.
Practical considerations
In practice, software and data used in this book must be documented with selection definitions, units, binning choices, correction factors, and reproducible code or configuration. This makes the result easier to compare across experiments and easier to reinterpret when improved simulations, calibrations, or theoretical predictions become available.[5]
See also
Table of contents (60 articles)
Index
Full contents
References
- ↑ Maguire, Eamonn; Heinrich, Lukas; Watt, Graeme (2017). "HEPData: a repository for high energy physics data". Journal of Physics: Conference Series 898: 102006. doi:10.1088/1742-6596/898/10/102006.
- ↑ Brun, Rene; Rademakers, Fons (1997). "ROOT: An object oriented data analysis framework". Nuclear Instruments and Methods in Physics Research A 389 (1-2): 81-86. doi:10.1016/S0168-9002(97)00048-X.
- ↑ Buckley, Andy (2013). "Rivet user manual". Computer Physics Communications 184 (12): 2803-2819. doi:10.1016/j.cpc.2013.05.021.
- ↑ Cowan, Glen (1998). Statistical Data Analysis. Oxford University Press. ISBN 978-0-19-850156-5.
- ↑ "Review of Particle Physics". Physical Review D 110 (3): 030001. 2024. doi:10.1103/PhysRevD.110.030001.
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