Learning to See at the Large Hadron Collider
Chris Quigg

TL;DR
This paper proposes using visual event displays in (pseudo)rapidity-transverse-momentum space at the Large Hadron Collider to enhance understanding, identify interesting events, and test theoretical expectations during the collider's commissioning phase.
Contribution
It introduces a novel visual tool for analyzing particle collision data, aiding intuition and event classification during collider commissioning.
Findings
Event displays help identify unusual collision events.
The tool improves understanding of particle production features.
It assists in testing theoretical models against observed data.
Abstract
The staged commissioning of the Large Hadron Collider presents an opportunity to map gross features of particle production over a significant energy range. I suggest a visual tool - event displays in (pseudo)rapidity-transverse-momentum space - as a scenic route that may help sharpen intuition, identify interesting classes of events for further investigation, and test expectations about the underlying event that accompanies large-transverse-momentum phenomena.
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Taxonomy
TopicsBig Data Technologies and Applications · Particle physics theoretical and experimental studies · Computational Physics and Python Applications
