Muonphilic portals to fermionic asymmetric dark matter are constrained by existing data and can be probed further by 3 and 10 TeV muon colliders.
Dark Matter and Vectorlike Leptons from Gauged Lepton Number
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abstract
We investigate a simple model where Lepton number is promoted to a local $U(1)_L$ gauge symmetry which is then spontaneously broken, leading to a viable thermal DM candidate and vector-like leptons as a byproduct. The dark matter arises as part of the exotic lepton sector required by the need to satisfy anomaly cancellation and is a Dirac electroweak (mostly) singlet neutrino. It is stabilized by an accidental global symmetry of the renormalizable Lagrangian which is preserved even after the gauged lepton number is spontaneously broken and can annihilate efficiently to give the correct thermal relic abundance. We examine the ability of this model to give a viable DM candidate and discuss both direct and indirect detection implications. We also examine some of the LHC phenomenology of the associated exotic lepton sector and in particular its effects on Higgs decays.
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background 2representative citing papers
Updated LISA detection prospects for gravitational waves from phase transitions are derived from state-of-the-art sound-wave simulations, with a new web tool PTPlot provided for parameter scans.
XGBoost machine learning improves discrimination in LHC searches for singlet vector-like leptons, yielding projected 2σ mass exclusion limits of 620 GeV (three-lepton) and 490 GeV (four-lepton) at 14 TeV with 3000 fb^{-1}.
citing papers explorer
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Muonphilic asymmetric dark matter at a future muon collider
Muonphilic portals to fermionic asymmetric dark matter are constrained by existing data and can be probed further by 3 and 10 TeV muon colliders.
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Detecting gravitational waves from cosmological phase transitions with LISA: an update
Updated LISA detection prospects for gravitational waves from phase transitions are derived from state-of-the-art sound-wave simulations, with a new web tool PTPlot provided for parameter scans.
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Machine Learning Study on Single Production of a Singlet Vector-like Lepton at the Large Hadron Collider
XGBoost machine learning improves discrimination in LHC searches for singlet vector-like leptons, yielding projected 2σ mass exclusion limits of 620 GeV (three-lepton) and 490 GeV (four-lepton) at 14 TeV with 3000 fb^{-1}.