Physics:Quantum data analysis/Linear Regression
Overview
Linear Regression 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, linear regression 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.[1]
Quality checks
For linear regression, useful checks include closure tests on simulated samples, comparison with independent control regions, and stability tests under reasonable changes of selection, calibration, and binning. These checks help separate statistical fluctuations from analysis choices and detector effects.
Documentation
The page should record the definition of the objects being used, the data or simulation inputs, and the uncertainty model. That documentation is important because later measurements often reuse the same workflow with improved detector conditions or larger data sets.
See also
Table of contents (60 articles)
Index
Full contents
References
- ↑ "Review of Particle Physics". Physical Review D 110 (3): 030001. 2024. doi:10.1103/PhysRevD.110.030001.
Source attribution: Physics:Quantum data analysis/Linear Regression