An ML-guided scan combined with a high-precision effective field theory finds a small region of the minimal Left-Right Symmetric Model where the first parity-breaking phase transition yields gravitational waves detectable at BBO and DECIGO with SNR around 1 to 10.
Probing the Higgs Sector of the Minimal Left-Right Symmetric Model at Future Hadron Colliders
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abstract
If neutrino masses arise from a TeV-scale minimal Left-Right seesaw model, the ensuing extended Higgs sector with neutral, singly and doubly-charged scalars has a plethora of implications for new Higgs boson searches beyond the Standard Model at future hadron colliders, such as the $\sqrt s=14$ TeV High-Luminosity Large Hadron Collider (HL-LHC) and the proposed $\sqrt s=100$ TeV collider (FCC-hh or SPPC). In this article, we provide a glimpse of this new physics in the Higgs sector. Our discussion focuses on the minimal non-supersymmetric version of the Left-Right model with high-scale parity breaking but TeV-scale $SU(2)_R$-breaking, a property desirable to suppress the type-II seesaw contribution to neutrino masses. We analyze the masses and couplings of the physical Higgs bosons in this model, and discuss their dominant production and decay modes at hadron colliders. We identify the best discovery channels for each of the non-SM Higgs bosons and estimate the expected SM backgrounds in these channels to derive the sensitivity reaches for the new Higgs sector at future hadron colliders under discussion. Following a rather conservative approach, we estimate that the heavy Higgs sector can be effectively probed up to 15 TeV at the $\sqrt s=100$ TeV machine. We also discuss how the LR Higgs sector can be distinguished from other extended Higgs sectors.
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Machine Learning Left-Right Breaking from Gravitational Waves
An ML-guided scan combined with a high-precision effective field theory finds a small region of the minimal Left-Right Symmetric Model where the first parity-breaking phase transition yields gravitational waves detectable at BBO and DECIGO with SNR around 1 to 10.