Physical Review Letters · 2009 · 68 citations · 19 references
Natural SciencesPhysicsMachine LearningNuclear PhysicsParticle PhysicsCollider PhysicsDeep LearningFlavor PhysicsNeural NetworksParticle DetectorsHeavy Flavor PhysicsJoint Probability AnalysisJoint Probability Density FunctionsJoint Probability Mass FunctionsNeural Network ModelingPrecisionResolution FunctionsTop Quark Physics
We report a measurement of the top-quark mass ${M}_{t}$ in the dilepton decay channel $t\overline{t}\ensuremath{\rightarrow}b{l}^{\ensuremath{'}+}{\ensuremath{\nu}}_{l}^{\ensuremath{'}}\overline{b}{l}^{\ensuremath{-}}{\overline{\ensuremath{\nu}}}_{l}$. Events are selected with a neural network which has been directly optimized for statistical precision in top-quark mass using neuroevolution, a technique modeled on biological evolution. The top-quark mass is extracted from per-event probability densities that are formed by the convolution of leading order matrix elements and detector resolution functions. The joint probability is the product of the probability densities from 344 candidate events in $2.0\text{ }\text{ }{\mathrm{fb}}^{\ensuremath{-}1}$ of $p\overline{p}$ collisions collected with the CDF II detector, yielding a measurement of ${M}_{t}=171.2\ifmmode\pm\else\textpm\fi{}2.7(\mathrm{stat})\ifmmode\pm\else\textpm\fi{}2.9(\mathrm{syst})\text{ }\text{ }\mathrm{GeV}/{c}^{2}$.
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Evolving artificial neural networks
Xin Yao · Proceedings of the IEEE · 1999 · 3K citations
High-energy-physics event generation with Pythia 6.1
Torbjörn Sjöstrand, P. Edén, Christer Friberg et al. · Computer Physics Communications · 2001 · 2K citations · Full text