Physics:Quantum data analysis/Data Acquisition Electronics and Systems: Difference between revisions
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== Analysis consequences == | == Analysis consequences == | ||
DAQ choices affect trigger efficiency, dead time, prescales, data quality, and event content. Analyses must understand these effects when defining datasets, control samples, and systematic uncertainties.<ref name="cowan">{{cite book |last=Cowan |first=Glen |title=Statistical Data Analysis |publisher=Oxford University Press |year=1998 |isbn=978-0-19-850156-5}}</ref> | DAQ choices affect trigger efficiency, dead time, prescales, data quality, and event content. Analyses must understand these effects when defining datasets, control samples, and systematic uncertainties.<ref name="cowan">{{cite book |last=Cowan |first=Glen |title=Statistical Data Analysis |publisher=Oxford University Press |year=1998 |isbn=978-0-19-850156-5}}</ref> | ||
== Overview == | |||
'''Data Acquisition Electronics and Systems''' 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, data acquisition electronics and systems 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.<ref name="pdg-data">{{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= | =See also= | ||
Revision as of 23:09, 19 May 2026
Data acquisition electronics and systems convert detector signals into recorded event data. In particle-physics experiments, most collisions cannot be stored permanently, so front-end electronics, timing systems, triggers, buffers, readout networks, and online processing decide which information becomes part of the analyzable dataset. DAQ is therefore not a background service; it shapes the physics reach of the experiment.[1]
Signal chain
A typical DAQ chain begins with detector sensors and front-end electronics, then digitizes signals, applies timing and synchronization, stores data temporarily in buffers, and transfers accepted events to online computing systems.[1][2]
Trigger systems
Trigger systems select potentially interesting events from very high collision rates. Hardware triggers can make fast decisions from coarse detector information, while software triggers apply more detailed reconstruction and selection.[3][4]
Analysis consequences
DAQ choices affect trigger efficiency, dead time, prescales, data quality, and event content. Analyses must understand these effects when defining datasets, control samples, and systematic uncertainties.[5]
Overview
Data Acquisition Electronics and Systems 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, data acquisition electronics and systems 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.[6]
See also
Table of contents (60 articles)
Index
Full contents
References
- ↑ 1.0 1.1 Paschalidis, Nicholas P. (2017). Data Acquisition Systems: From Fundamentals to Applied Design. CRC Press. ISBN 978-1-4987-7762-9.
- ↑ Leo, William R. (1994). Techniques for Nuclear and Particle Physics Experiments. Springer. ISBN 978-3-540-57280-0.
- ↑ "The ATLAS Experiment at the CERN Large Hadron Collider". Journal of Instrumentation 3: S08003. 2008. doi:10.1088/1748-0221/3/08/S08003.
- ↑ "The CMS experiment at the CERN LHC". Journal of Instrumentation 3: S08004. 2008. doi:10.1088/1748-0221/3/08/S08004.
- ↑ 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.
Source attribution: Physics:Quantum data analysis/Data Acquisition Electronics and Systems