
\vspace{0.2in}
{\bf Event Samples} \\ \nopagebreak

The $ZH$ events were generated using PYTHIA for Higgs masses of 90,
100, 110, 120 and 130 GeV/$c^2$.  The principal backgrounds to $ZH$
production are $ZZ$, $Zb\bar{b}$, single top and $t\bar{t}$. The
$Zb\bar{b}$ background sample was generated using CompHEP, with
fragmentation done using PYTHIA, while all other samples were
generated using PYTHIA.  We used SHW to simulate the detector response
and, as in the $WH$ study, we assumed that two jets are $b$-tagged.
The cross sections for signal and background are shown in
Tables~\ref{table4} and~\ref{table5}.

\begin{table}[h!]
\caption{$ZH\rightarrow\lpm\bb$ neural network analysis. Accepted cross section
         times branching ratio for signal and background processes.}
\label{table4}
\begin{center}
\begin{tabular}{lr}
$M_H$ (GeV/c$^2$) & $\sigma\times BR(\mbox{pb})$ \\ \tableline
90	&  0.020\\
100 	&  0.015\\ 
110 	&  0.011\\ 
120 	&  0.008\\ 
130 	&  0.006\\ \tableline
backgrounds	&		\\
$Zb\bar{b}$ 	& 0.35\\
$tbq$ 		& 0.80\\ \tableline 
		& $\sigma$ (pb) \\ 
$ZZ$ 		& 1.235\\
$tb$ 		& 1.0\\
$t\bar{t}$ 	& 7.5
\end{tabular}
%\caption{$ZH\rightarrow\lpm\bb$ neural network analysis. Accepted cross section
%         times branching ratio for signal and background processes.}
%\label{table4}
\end{center}
\end{table}

\begin{table}[h!]
\caption{$ZH, Z\to\lpm$, neural net analysis, signal and
         background expected in 1 fb$^{-1}$.}
\label{table5}
\begin{center}
\begin{tabular}{lrrrrr}
$M_H$ (GeV/c$^2$)&90 &    100 &   110 &     120 &   130\\ \tableline
Number of events &&&&& \\
$ZH$ 	    &   1.26 &   0.87 &   0.79 &   0.80 &   0.58\\ \hline
$Zb\bar{b}$ &   0.61 &   0.45 &   0.61 &   1.50 &   1.42\\
$ZZ$        &   2.04 &   1.44 &   1.42 &   0.83 &   0.31\\
$t\bar{t}$  &   0.28 &   0.05 &   0.23 &   0.44 &   0.18\\ \hline
total background & 2.93 & 1.94 &  2.26 &   2.77 &   1.91\\ \hline
$S/B$        &   0.43 & 0.45 & 0.35 &  0.29 &  0.31\\
$S/\sqrt{B}$ &   0.74 & 0.63 & 0.54 &  0.48 &  0.42
\end{tabular}
%\caption{$ZH, Z\to\lpm$, neural net analysis, signal and
%         background expected in 1 fb$^{-1}$.}
%\label{table5}
\end{center}
\end{table}

A network was trained for each Higgs mass
and for each of the backgrounds with the following variables
\begin{itemize}
\item
$\et$ of the $b$-tagged jets
\item
$\et$ of the two leptons
\item
$M_{b\bar{b}}$ -- invariant mass of the $b$-tagged jets
\item
$M_{l\bar{l}}$ -- invariant mass of the leptons
\item
$H_T$ -- sum of the transverse energy of all the jets
\item
$\Delta R$ between the first lepton and the first $b$-tagged jet
\end{itemize}


\begin{figure}
  \begin{center}
\centerline{\parbox{6.0in}{\epsfxsize=\hsize\epsffile{zhll/net_plots_zhll.eps}} }
  \end{center}
  \caption{Distributions of neural network outputs for selecting
           $ZH, Z\to\lpm$ events, for networks trained using signal 
           (heavy shading) vs. the backgrounds $ZZ$, $Z\bb$, 
           $t\bar{t}$ and single top.}
  \label{fig:zhll-net}
\end{figure}

\vspace{0.2in}
{\bf Results and Conclusions} \\  \nopagebreak

Figure~\ref{fig:zhll-net} shows the network output distributions.
From these figures it is clear that discriminating between $ZH$ and
the $ZZ$ backgrounds is much more difficult than for $WH$ and its
backgrounds.  Further study is needed to identify better variables if
the neural network technique is to prove as powerful in this channel
as in the others.  We summarize the results in Table~\ref{table5}.
