Abstract
Results are reported from a search for the lepton flavor violating decay τ → 3μ in proton-proton collisions at sqrt{mathrm{s}} = 13 TeV. The data sample corresponds to an integrated luminosity of 33.2 fb−1 recorded by the CMS experiment at the LHC in 2016. The search exploits τ leptons produced in both W boson and heavy-flavor hadron decays. No significant excess above the expected background is observed. An upper limit on the branching fraction ℬ(τ → 3μ) of 8.0 × 10−8 at 90% confidence level is obtained, with an expected upper limit of 6.9 × 10−8.
Highlights
We present results from the CMS experiment of the first search for the LFV decay τ → 3μ from a combination of the two independent channels
For the analysis of τ leptons from heavy-flavor hadron decays, events were simulated using pythia 8.226 [17] with the CUETP8M1 tune [18] interfaced with evtgen 1.6.0 [19] for particle decays, with the τ decay kinematics determined by phase space, rather than a particular model
Events with at least one τ candidate with an invariant mass between 1.6 and 2.0 GeV are kept for analysis by two different algorithms, one optimized for production of τ leptons in W boson decays and the other optimized for production of τ leptons in heavy-flavor hadron decays
Summary
The central feature of the CMS apparatus is a superconducting solenoid of 6 m internal diameter, providing a magnetic field of 3.8 T. For the analysis of τ leptons from W boson decays, events were simulated using MadGraph5_amc@nlo 2.5.2 [15, 16] at leading order, assuming a two-Higgs-doublet model that allows for flavor changing neutral currents and LFV processes, interfaced with pythia for parton shower and hadronization descriptions. For the analysis of τ leptons from heavy-flavor hadron decays, events were simulated using pythia 8.226 [17] with the CUETP8M1 tune [18] interfaced with evtgen 1.6.0 [19] for particle decays, with the τ decay kinematics determined by phase space, rather than a particular model. The multiple pp collisions that occur within the same or nearby bunch crossings (pileup) are modeled by including additional minimum bias events generated with pythia with a distribution that matches the one observed in data. Simulated events are reconstructed with the same algorithms as used for data, including emulation of the triggers
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