# Data driven method for predicting the shape of the dijet mass in $Z\,h + {E_{\rm T}}^{\rm {miss}}$ analysis

## TL;DR

This paper introduces a data-driven approach to predict the dijet mass distribution in a specific LHC search for new physics involving Z bosons, Higgs bosons, and missing energy, improving background estimation accuracy.

## Contribution

It presents a novel data-driven method for modeling the dijet mass distribution in $Z h + E_T^{miss}$ analyses at the LHC, enhancing background prediction for new physics searches.

## Key findings

- Effective background reduction using data-driven techniques.
- Improved dijet mass distribution modeling.
- Potential for increased sensitivity in new physics searches.

## Abstract

We present an analysis of new physics searches in $Z\,h$ with missing energy final states at the Large Hadron Collider considering $Z \to l^+ l^-$ (where $l^\pm = e^\pm, \mu^\pm$) and $h \to b\bar b$ decay modes. For this analysis we consider production of a $CP$-odd scalar $A$ through gluon-fusion which decay into a heavy $CP$-even neutral scalar $H$ with $Z$-boson. Further $H$ decays into a lighter $CP$-even Higgs boson $h$ in association with dark matter candidate $\chi$ - a source of missing energy. The masses of these scalars are considered as $m_h = 125$ GeV, $m_\chi = 60$ GeV, $2 m_h < m_H < 2 m_t$ and $m_A > 2 m_t$. A data-driven method have been applied to reduce the considerable backgrounds from electroweak processes $W$+ jets and $Z$+ jets, in addition with top-pair and single-top production. The di-jet mass distributions have been studied with same- and opposite-flavour lepton selections.