Machine learning tagged boosted dark photon: A signature of fermionic portal matter at the LHC
Shivam Verma, Sanjoy Biswas, Tanumoy Mandal, Subhadip Mitra

TL;DR
This paper employs a hybrid deep neural network to identify boosted dark photon jets as signatures of fermionic portal matter at the LHC, proposing a novel detection method for dark sector particles.
Contribution
It introduces a new deep learning approach to detect boosted dark photon jets from fermionic portal matter at the LHC, focusing on specific final states and event topologies.
Findings
Achieves a 2.3 TeV exclusion limit on top partner mass at 14 TeV LHC.
Demonstrates significant background suppression using invariant mass and jet tagging.
Provides a new methodology for dark sector particle detection with deep learning.
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,…
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Taxonomy
TopicsParticle physics theoretical and experimental studies · High-Energy Particle Collisions Research · Dark Matter and Cosmic Phenomena
