Higgs Boson pair production merged to one jet

TL;DR

This work develops a Monte Carlo framework that merges exact one-jet matrix elements for Higgs boson pair production with a parton shower, leveraging OpenLoops for matrix elements and HERWIG++ for event generation. It systematically studies merging-related uncertainties and demonstrates that the merged samples reduce theoretical uncertainties in key observables, improving the reliability of di-Higgs analyses and Higgs self-coupling constraints. The approach retains full top and bottom mass dependence in loops and includes real-emission diagrams up to one jet, offering more accurate kinematics than LO+PS simulations. The code is distributed as an add-on to HERWIG++ at the authors' website.

Abstract

We develop a Monte Carlo event generator for Higgs Boson pair production merged to exact one-jet matrix elements. The matrix elements are generated with OpenLoops and event generation is performed with the HERWIG++ general-purpose event generator. This allows us to simulate fully-exclusive hadronic final states with accurate description of the kinematics of the leading jet in conjunction with a parton shower. We use the implementation to examine in detail the systematic uncertainties which result from the merging procedure. We assess the magnitude of the impact of the merging on experimental searches of Standard Model di-Higgs production that aim to constrain the Higgs boson self-coupling. We find that the use of a merged sample can reduce theoretical systematic uncertainties in the efficiencies of cuts on certain observables. This constitutes the most accurate simulation of the process available to date. The Monte Carlo event generator developed for this project is available as an add-on to the HERWIG++ event generator at http://www.itp.uzh.ch/~andreasp/hh

Figures (10)

• Figure 1: The Higgs pair production diagrams contributing to the gluon fusion process at LO are shown for a generic fermion $f$.
• Figure 2: Diagram classes which contribute to Higgs boson pair production in association with one extra parton are shown for a generic fermion $f$ running in the loop.
• Figure 3: The transverse momentum of the di-Higgs system and the transverse momentum of a Higgs boson, $p^{hh}_\perp$ and $p_\perp^h$ respectively (top), the distance between the two Higgs bosons, $\Delta R(h,h)$, and the $p_\perp$ of the leading jet (bottom). A comparison between the two different smoothing schemes, 'Sinusoidal' and 'Uniform' is shown. The clustering parameters were set to $\bar{E}_{Tclus} =60$ GeV, $\epsilon_{clus}=30$ GeV in both cases. We also show the un-merged sample ('0j inc.') with $\mu = m_h$, with respect to which the ratio sub-plot is taken.
• Figure 4: The transverse momentum of the di-Higgs system and the transverse momentum of a Higgs boson, $p^{hh}_\perp$ and $p_\perp^h$ respectively (top), the distance between the two Higgs bosons, $\Delta R(h,h)$, and the $p_\perp$ of the leading jet (bottom). The merged samples are shown in blue, with the blue line corresponding to $\mu = 2 ( m_h + p^{hh}_\perp )$ and the un-merged samples are shown in red, with the red line corresponding to $\mu = 2 m_h$. The bands show the envelope of scale variations between $\mu = m_h + p^{hh}_\perp$ and $\mu = 4 ( m_h + p^{hh}_\perp)$ for the merged sample and $\mu = m_h$ and $\mu = 4 m_h$ for the un-merged sample. The merging parameters were chosen to be $\bar{E}_{Tclus} = 60$ GeV, $\epsilon_{clus}=30$ GeV. The ratio sub-plot is taken with respect to the un-merged sample with $\mu = 2 m_h$.
• Figure 5: The transverse momentum of the di-Higgs system and the transverse momentum of a Higgs boson, $p^{hh}_\perp$ and $p_\perp^h$ respectively (top), the distance between the two Higgs bosons, $\Delta R(h,h)$, and the $p_\perp$ of the leading jet (bottom). Different $\epsilon_{clus}$ are chosen and the other parameters set to $\bar{E}_{Tclus} = 60$ GeV, $\mu=m_h + p_\perp^{hh}$. The ratio sub-plot is taken with respect to the un-merged sample with $\mu = m_h$ ('0j inc.') and the yellow bands in the ratio sub-plot represent the Monte Carlo statistical uncertainty in that sample.