2 citations · 2 across the 3 of their papers we have counts for
8 papers
Deep-Learned Event Variables for Collider Phenomenology
Doojin Kim, Kyoungchul Kong, Konstantin T. Matchev +2
The choice of optimal event variables is crucial for achieving the maximal sensitivity of experimental analyses. Over time, physicists have derived suitable kinematic variables for…
Non-Boolean Quantum Amplitude Amplification and Quantum Mean Estimation
Prasanth Shyamsundar
This paper generalizes the quantum amplitude amplification and amplitude estimation algorithms to work with non-boolean oracles. The action of a non-boolean oracle on an eige…
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…
Optimal event selection and categorization in high energy physics, Part 1: Signal discovery
Konstantin K. Matchev, Prasanth Shyamsundar
We provide a prescription to train optimal machine-learning-based event selectors and categorizers that maximize the statistical significance of a potential signal excess in high e…