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Multivariate discrimination and the Higgs + W/Z search

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abstract

A systematic method for optimizing multivariate discriminants is developed and applied to the important example of a light Higgs boson search at the Tevatron and the LHC. The Significance Improvement Characteristic (SIC), defined as the signal efficiency of a cut or multivariate discriminant divided by the square root of the background efficiency, is shown to be an extremely powerful visualization tool. SIC curves demonstrate numerical instabilities in the multivariate discriminants, show convergence as the number of variables is increased, and display the sensitivity to the optimal cut values. For our application, we concentrate on Higgs boson production in association with a W or Z boson with H -> bb and compare to the irreducible standard model background, Z/W + bb. We explore thousands of experimentally motivated, physically motivated, and unmotivated single variable discriminants. Along with the standard kinematic variables, a number of new ones, such as twist, are described which should have applicability to many processes. We find that some single variables, such as the pull angle, are weak discriminants, but when combined with others they provide important marginal improvement. We also find that multiple Higgs boson-candidate mass measures, such as from mild and aggressively trimmed jets, when combined may provide additional discriminating power. Comparing the significance improvement from our variables to those used in recent CDF and DZero searches, we find that a 10-20% improvement in significance against Z/W + bb is possible. Our analysis also suggests that the H + W/Z channel with H -> bb is also viable at the LHC, without requiring a hard cut on the W/Z transverse momentum.

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representative citing papers

Graph theory inspired anomaly detection at the LHC

hep-ph · 2025-06-24 · conditional · novelty 6.0

Sparse globally rigid graph representations of jets, combined with roughly 30 reclustered subjets, improve graph autoencoder anomaly detection on the LHC Olympics benchmark.

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  • Graph theory inspired anomaly detection at the LHC hep-ph · 2025-06-24 · conditional · none · ref 92 · internal anchor

    Sparse globally rigid graph representations of jets, combined with roughly 30 reclustered subjets, improve graph autoencoder anomaly detection on the LHC Olympics benchmark.