Efficient, reliable and fast high-level triggering using a bonsai boosted decision tree
By Vladimir Vava Gligorov, Mike Williams. 2012
488 citations recorded by the source corpus.
High-level triggering is a vital component of many modern particle physics experiments. This paper describes a modification to the standard boosted decision tree (BDT) classifier, the so-called bonsai BDT, that has the following important properties: it is more efficient than traditional cut-based approaches; it is robust against detector instabilities, and it is very fast. Thus, it is fit-for-purpose for the online running conditions faced by any large-scale data acquisition system.
Subfields: Artificial Intelligence, Nuclear and High Energy Physics
View Paper: https://www.semanticscholar.org/paper/459fce4b1afe8caa6a13ea84e8f381faa8652580
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In Artificial Intelligence
In Nuclear and High Energy Physics
- G. Lanfranchi
- T. Blake
- Simone Bifani
- Vladimir Vava Gligorov
- U. Egede
- S. Farry
- J. Albrecht
- R. McNulty
Citations in the corpus
- Test of lepton universality using b+ → K+ℓ+ℓ- decays. (2014, methodology): “A multivariate algorithm [14] is used for the identification of secondary vertices consistent with the decay of a b hadron.”