What does Bidirectional LSTM Neural Networks has to do with Top Quarks?

Author(s): Riccardo Di Sipio

Originally published on Towards AI.

And how it turned out that looking at a sequence of vectors in four dimensions from two opposite sides was the key to solve a decades-old problem

In a recent paper Bidirectional Long Short-Term Memory (BLSTM) neural networks for reconstruction of top-quark pair decay kinematics (preprint: arXiv:1909.01144), my summer student Fardin explored a number of techniques to reconstruct the decay chain of a fundamental particle called top quark that is abundantly produced at the LHC. This particle decays preferably into a W boson and a bottom quark (t→Wb). The W boson, in turn, can decay into a pair of quarks (W→qq’) in two-thirds of the cases or a charged lepton and a neutrino (W→lv) in the remaining one-third of the cases. The most common process leading to… Read the full blog for free on Medium.

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Published via Towards AI