Signal mixture estimation for degenerate heavy Higgses using a deep neural network

TL;DR

This paper explores using deep neural networks to accurately estimate the composition of signals from nearly degenerate heavy Higgs bosons at the LHC, improving uncertainty estimates over traditional methods.

Contribution

It introduces a neural network-based approach for signal mixture estimation in complex Higgs scenarios, demonstrating a significant uncertainty reduction.

Findings

Neural network approach yields ~20% lower uncertainty in estimates.

Method outperforms single-variable fit in overlapping feature scenarios.

Applicable to future LHC data for identifying degenerate Higgs states.

Abstract

If a new signal is established in future LHC data, a next question will be to determine the signal composition, in particular whether the signal is due to multiple near-degenerate states. We investigate the performance of a deep learning approach to signal mixture estimation for the challenging scenario of a ditau signal coming from a pair of degenerate Higgs bosons of opposite CP charge. This constitutes a parameter estimation problem for a mixture model with highly overlapping features. We use an unbinned maximum likelihood fit to a neural network output, and compare the results to mixture estimation via a fit to a single kinematic variable. For our benchmark scenarios we find a ~20% improvement in the estimate uncertainty.