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A comprehensive real-time analysis model at the LHCb experiment

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Abstract

An evolved real-time data processing strategy is proposed for high-energy physics experiments, and its implementation at the LHCb experiment is presented. The reduced event model allows not only the signal candidate firing the trigger to be persisted, as previously available, but also an arbitrary set of other reconstructed or raw objects from the event. This allows for higher trigger rates for a given output data bandwidth, when compared to the traditional model of saving the full raw detector data for each trigger, whilst accommodating inclusive triggers and preserving data mining capabilities. The gains in physics reach and savings in computing resources already made possible by the model are discussed, along with the prospects of employing it more widely for Run 3 of the Large Hadron Collider.

Figures and captions

An overview of the LHCb trigger scheme in Run 2 [9]. The green boxes represent trigger stages, the blue ellipses represent storage units, and arrows between elements represent data flow, labelled with approximate flow rates. Events that reach a terminal storage unit are kept for offline analysis.

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Data flow in saving and restoring trigger objects from online (left) to offline (right) [2].

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A cartoon of the same reconstructed event with varying levels of object persistence: Turbo (top); selective persistence (middle); and complete reconstruction persistence (bottom). Solid objects are those persisted in each case. A trigger selection may also ask for one or more sub-detector raw banks to also be stored, shown as solid rectangles.

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Algorithm flow to decide whether a given raw bank is persisted for the current event. The resulting list of banks persisted for a given event is the superset of those required by the individual lines.

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Animated gif made out of all figures.

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Tables and captions

Average event sizes for trigger lines requesting varying levels of information persistence, measured on data collected in 2018.

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tex code
Table_1.pdf

Created on 24 October 2023.