Machine learning tagged boosted dark photon: A signature of fermionic portal matter at the LHC

Abstract

We use a Hybrid Deep Neural Network (HDNN) to identify a boosted dark photon jet as a signature of a heavy vector-like fermionic portal matter (PM) connecting the visible and the dark sectors. In this work, the fermionic PM, which mixes only with the Standard Model (SM) third-generation up-type quark, predominantly decays into a top quark and a dark photon pair. The dark photon then promptly decays to a pair of standard model fermions via the gauge kinetic mixing. We have analyzed two different final states, namely, (i) exactly one tagged dark photon and exactly one tagged top quark jet, and (ii) at least two tagged dark photons and at least one tagged top quark jet at the 13 and 14 TeV LHC center of mass energies. Both these final states receive significant contributions from the pair and single production processes of the top partner. The rich event topology of the signal processes, i.e., the presence of a boosted dark photon and top quark jet pair, along with the fact that the invariant mass of the system corresponds to the mass of the top partner, help us to significantly suppress potential SM backgrounds. We have shown that one can set a $2\sigma$ exclusion limit of $\sim 2.3$ TeV on the top partner mass with $\sin\theta_L=0.1$ and assuming $100\%$ branching ratio of the top partner in the final state with exactly one tagged dark photon and exactly one tagged top quark jet at the 14 TeV LHC center of mass energy assuming 300 fb$^{-1}$ of integrated luminosity.