6,594 research outputs found
Jet-Images: Computer Vision Inspired Techniques for Jet Tagging
We introduce a novel approach to jet tagging and classification through the
use of techniques inspired by computer vision. Drawing parallels to the problem
of facial recognition in images, we define a jet-image using calorimeter towers
as the elements of the image and establish jet-image preprocessing methods. For
the jet-image processing step, we develop a discriminant for classifying the
jet-images derived using Fisher discriminant analysis. The effectiveness of the
technique is shown within the context of identifying boosted hadronic W boson
decays with respect to a background of quark- and gluon- initiated jets. Using
Monte Carlo simulation, we demonstrate that the performance of this technique
introduces additional discriminating power over other substructure approaches,
and gives significant insight into the internal structure of jets
A Simple Iterative Algorithm for Parsimonious Binary Kernel Fisher Discrimination
By applying recent results in optimization theory variously known as optimization transfer or majorize/minimize algorithms, an algorithm for binary, kernel, Fisher discriminant analysis is introduced that makes use of a non-smooth penalty on the coefficients to provide a parsimonious solution. The problem is converted into a smooth optimization that can be solved iteratively with no greater overhead than iteratively re-weighted least-squares. The result is simple, easily programmed and is shown to perform, in terms of both accuracy and parsimony, as well as or better than a number of leading machine learning algorithms on two well-studied and substantial benchmarks
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