Phenomenology of photons-enriched semi-visible jets

TL;DR

This paper proposes a novel LHC search strategy for confining dark sectors that produce semi-visible jets enriched with non-isolated photons, using deep learning to improve detection efficiency and sensitivity.

Contribution

It introduces a new signature involving semi-visible jets with non-isolated photons and develops a deep neural network-based jet tagger to enhance detection of dark sector signals at the LHC.

Findings

Potential to discover or exclude a 5 TeV Z' boson with full Run 2 data.

Deep neural network improves signal-to-background discrimination.

New analysis strategy increases detection efficiency for dark sector signatures.

Abstract

This Letter proposes a new signature for confining dark sectors at the LHC. Under the assumption of a QCD-like hidden sector, hadronic jets containing stable dark bound states originating from hidden strong dynamics, known as semi-visible jets, could manifest in proton-proton collisions. In the proposed simplified model, a heavy $Z'$ mediator coupling to SM quarks allows the resonant production of dark quarks, subsequently hadronizing in stable and unstable dark bound states. The unstable dark bound states can then decay back to SM quarks via the same $Z'$ portal or photons via a lighter pseudo-scalar portal (such as an axion-like particle). This mechanism creates a new signature where semi-visible jets are enriched in non-isolated photons. We show that these exotic jets evade the phase space probed by current LHC searches exploiting jets or photons due to the expected high jet neutral electromagnetic fraction and photons candidates non-isolation, respectively. In the proposed analysis strategy to tackle such signature, we exploit jets as final state objects to represent the underlying QCD-like hidden sector. We show that, by removing any selection on the neutral electromagnetic fraction from the jet identification criteria, higher signal efficiency can be reached. To enhance the signal-to-background discrimination, we train a deep neural network as a jet tagger that exploits differences in the substructure of signal and background jets. We estimate that with the available triggers for Run 2 and this new strategy, a high mass search can claim a $5 \sigma$ discovery (exclusion) of the $Z'$ boson with a mass up to 5 TeV (5 TeV) with the full Run 2 data of the LHC when the fraction of unstable dark hadrons decaying to photons pairs is around 30 %, and with a coupling of the $Z'$ to SM quarks of 0.25.