Physics:Quantum data analysis/Machine Learning - Revision history

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	'''Machine ~~learning~~''' in particle-physics data analysis refers to statistical and computational methods that learn patterns from simulated or recorded collision data. It is used for classification, regression, anomaly detection, detector reconstruction, fast simulation, and analysis optimisation.		'''Machine Learning''' is a topic in particle-physics data analysis. Machine learning in particle-physics data analysis refers to statistical and computational methods that learn patterns from simulated or recorded collision data. It is used for classification, regression, anomaly detection, detector reconstruction, fast simulation, and analysis optimisation. Machine-learning methods are widely used when the relation between detector-level observables and physics-level quantities is high-dimensional or difficult to express with simple cuts. In high-energy physics, common uses include particle identification, jet tagging, event selection, background rejection, energy calibration, and searches for rare or unexpected signatures. A machine-learning model must be validated against independent samples and control regions. Important checks include overtraining tests, stability under systematic variations, calibration of classifier scores, and comparison between simulated and observed data in regions that are not used for the signal extraction.
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'''Machine learning''' in particle-physics data analysis refers to statistical and computational methods that learn patterns from simulated or recorded collision data. It is used for classification, regression, anomaly detection, detector reconstruction, fast simulation, and analysis optimisation.
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== Overview ==
Machine-learning methods are widely used when the relation between detector-level observables and physics-level quantities is high-dimensional or difficult to express with simple cuts. In high-energy physics, common uses include particle identification, jet tagging, event selection, background rejection, energy calibration, and searches for rare or unexpected signatures.

== Validation ==
A machine-learning model must be validated against independent samples and control regions. Important checks include overtraining tests, stability under systematic variations, calibration of classifier scores, and comparison between simulated and observed data in regions that are not used for the signal extraction.

== Analysis role ==
The method is useful only when it remains interpretable enough for a physics measurement. Inputs, training samples, target definitions, preprocessing choices, and uncertainty propagation should be documented so that the result can be reproduced and compared with other analyses.<ref name="pdg-ml">{{cite journal |collaboration=Particle Data Group |title=Review of Particle Physics |journal=Physical Review D |volume=110 |issue=3 |pages=030001 |year=2024 |doi=10.1103/PhysRevD.110.030001}}</ref>

== See also ==
{{#invoke:PhysicsQC|tocHeadingAndList|Physics:Quantum basics/See also/Data Analysis}}

== References ==
{{reflist|3}}

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<div>{{Author|Sergei V. Chekanov}}</div>
<div>{{Author|Claude Pruneau}}</div>
<div>{{Author|Harold Foppele}}</div>
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{{Sourceattribution|Physics:Quantum data analysis/Machine Learning|1}}