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InClass Nets: Independent Classifier Networks for Nonparametric Estimation of Conditional Independence Mixture Models and Unsupervised Classification
Konstantin T. Matchev, Prasanth Shyamsundar
We introduce a new machine-learning-based approach, which we call the Independent Classifier networks (InClass nets) technique, for the nonparameteric estimation of conditional ind…
OASIS: Optimal Analysis-Specific Importance Sampling for event generation
Konstantin T. Matchev, Prasanth Shyamsundar
We propose a technique called Optimal Analysis-Specific Importance Sampling (OASIS) to reduce the number of simulated events required for a high-energy experimental analysis to rea…
Finding Wombling Boundaries in LHC Data with Voronoi and Delaunay Tessellations
Konstantin T. Matchev, Alexander Roman, Prasanth Shyamsundar
We address the problem of finding a wombling boundary in point data generated by a general Poisson point process, a specific example of which is an LHC event sample distributed in…
A Quantum Algorithm for Model-Independent Searches for New Physics
Konstantin T. Matchev, Prasanth Shyamsundar, Jordan Smolinsky
We propose a novel quantum technique to search for unmodeled anomalies in multidimensional binned collider data. We propose associating an Ising lattice spin site with each bin, wi…
Uncertainties associated with GAN-generated datasets in high energy physics
Konstantin T. Matchev, Alexander Roman, Prasanth Shyamsundar
Recently, Generative Adversarial Networks (GANs) trained on samples of traditionally simulated collider events have been proposed as a way of generating larger simulated datasets a…