Optimizing $\tilde{X}^{\pm}_{W} \rightarrow h^0\ell^{\pm}_{i}$ Reconstruction Efficiency with Small-R (R = 0.4) and Large-R (R = 1.0) Jets

TL;DR

This paper improves the reconstruction efficiency of Higgs bosons decaying into b-quarks in high-energy physics experiments by optimizing jet parameters, using Monte Carlo simulations and truth data analysis, with potential for neural network enhancements.

Contribution

It introduces optimized parameters for small-R and large-R jet reconstruction to enhance Higgs boson measurement accuracy in BSM searches.

Findings

Optimized jet parameters improve reconstruction efficiency.

Monte Carlo and truth data analyses validate parameter choices.

Higher chargino masses benefit more from optimized jet selection.

Abstract

Many Beyond the Standard Model searches at ATLAS employ jets to simplify event reconstruction. These jets cluster particle shower products into calculable objects, which are then used to obtain information about parent particles. Large-R (R = 1.0) jets combine these products into one jet that spans 2 radians, while small-R (R = 0.4) jets are used to further refine individual b-quark trajectories. Optimizing the use of jets is crucial for making precision measurements of Higgs bosons with high transverse momenta, and this paper uses b-quarks produced in the Wino chargino LSP decay to identify parameters that best do so. Monte Carlo simulation found that parameters such as the distance between Higgs bosons and the distance between b-jets were relevant in selecting the most accurate small-R reconstructions. Truth data analysis corroborated the paper's findings, especially for charginos with higher mass. These parameters were then used to refine small-R jet selection, increasing reconstruction efficiency across chargino masses. Further analysis into large-R jet selection would enhance these results, especially at higher chargino masses. A neural network would also prove useful for exploring the effects of combinations of parameters across chargino masses.