Speaker
Frederic Dreyer
(MIT)
Description
We introduce a novel representation for emission patterns inside a
jet, by declustering a Cambridge-Aachen jet and using the
primary-emission Lund plane coordinates.
We present several possible variations of this method, and show how it
can be used to construct either an n by n pixel image or a graph,
which can be used as inputs for neural networks.
Using W tagging as an example, we show how these jet representations
can be used as inputs for convolutional neural networks or recurrent
neural networks, performing on par or better than other
state-of-the-art methods.
We illustrate in particular how networks trained on Lund coordinates
result in excellent discrimination at high pt.
Primary author
Frederic Dreyer
(MIT)
