Online particle detection with Neural Networks based on topological calorimetry information is a research paper published in Journal of Physics: Conference Series (2012). On theSindex it has a DataRank of 5.1. It has been cited 79 times, with 78 citing works in its 1-hop citation network.
This paper presents the latest results from the Ringer algorithm, which is based on artificial neural networks for the electron identification at the online filtering system of the ATLAS particle detector, in the context of the LHC experiment at CERN. The algorithm performs topological feature extraction using the ATLAS calorimetry information (energy measurements). The extracted information is presented to a neural network classifier. Studies showed that the Ringer algorithm achieves high detection efficiency, while keeping the false alarm rate low. Optimizations, guided by detailed analysis, reduced the algorithm execution time by 59%. Also, the total memory necessary to store the Ringer algorithm information represents less than 6.2 percent of the total filtering system amount.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.657
From this paper's citation signal
Citation Network Contribution
4.4
From 69 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 13% comes from its base citations and 87% from the citation network (69 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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