DNN analysis of pp → WR → ℓNR → ℓℓjj at LHC Run 2 and HL-LHC improves exclusion limits on m_WR and m_NR for unmixed, maximal-mixing, and PMNS-like scenarios over cut-based methods and probes the |Ve1|–|Vμ1| plane.
Distinguishing Dirac/Majorana Sterile Neutrinos at the LHC
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
We study the purely leptonic decays of $W^\pm \to e^\pm e^\pm \mu^\mp \nu$ and $\mu^\pm \mu^\pm e^\mp \nu$ produced at the LHC, induced by sterile neutrinos with mass $m_N$ below $M_W$ in the intermediate state. Since the final state neutrino escapes detection, one cannot tell whether this process violates lepton number, what would indicate a Majorana character for the intermediate sterile neutrino. Our study shows that when the sterile neutrino mixings with electrons and muons are different enough, one can still discriminate between the Dirac and Majorana character of this intermediate neutrino by simply counting and comparing the above decay rates. After performing collider simulations and statistical analysis, we find that at the $14~\text{TeV}$ LHC with an integrated luminosity of $3000~\text{fb}^{-1}$, for two benchmark scenarios $m_N$ = 20 GeV and 50 GeV, at least a $3\sigma$ level of exclusion on the Dirac case can be achieved for disparities as mild as e.g. $|U_{Ne}|^2 < 0.7~ |U_{N\mu}|^2$ or $|U_{N\mu}|^2 < 0.7~ |U_{N e}|^2$, provided that $|U_{Ne}|^2$, $|U_{N\mu}|^2$ are both above $\sim 2\times 10^{-6}$.
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hep-ph 1years
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Probing lepton flavor mixing in $W_R$ searches with machine learning at the LHC
DNN analysis of pp → WR → ℓNR → ℓℓjj at LHC Run 2 and HL-LHC improves exclusion limits on m_WR and m_NR for unmixed, maximal-mixing, and PMNS-like scenarios over cut-based methods and probes the |Ve1|–|Vμ1| plane.