Archive for October 4, 2014
Lepton flavor violation (LFV) occurs when neutrinos oscillate in flavor, but is not supposed to occur (at tree level) when charged leptons are involved. Beyond the standard model, however, speculative models predict observable levels of LFV and since flavor is so difficult to understand theoretically, searches for LFV are inherently worthwhile.
A striking signature for LFV would be the neutrinoless decay of a tau lepton to three muons (or, for that matter, the decay of a muon to three electrons). The Belle and BaBar experiments at the B-factories have searched for τ→3μ; Belle set the best limit in 2010: BF(τ→3μ) < 2.1×10-8.
Tau leptons are produced copiously in high-energy pp collisions. They come from the semileptonic decays of b and c hadrons, which themselves are produced with huge cross sections. The LHCb experiment is designed to study b and c hadrons and is very successful at that. A key piece of the LHCb apparatus is a very nice muon spectrometer that provides triggers for the readout and a high-quality reconstruction of muon trajectories. This would seem to be an excellent place to search for τ→3μ decays – and it is, as reported this week (arXiv:1409.8548).
The selection begins, of course, with three muons that together form a displaced vertex (taking advantage of the tau and charm hadron lifetimes). The momentum vector of the three muons should be nearly collinear with a vector pointing from the primary vertex to the tri-muon vertex — there are no neutrinos in the signal, after all, and the tau lepton takes most of the energy of the charm hadron, and therefore closely follows the direction of the charm hadron. (Charm hadron decays produce most of the tau leptons, so those coming from b hadrons are lost, but this does not matter much.) Here is a depiction of a signal event, which comes from a talk given by Gerco Onderwater at NUFACT2014:
I like the way the analysis is designed: there is the all-important tri-muon invariant mass distribution, there is a classifier for “geometry” – i.e., topology, and a second one for muon identification. Clearly, this analysis is challenging.
The geometry classifier M(3body) incorporates information about the vertex and the pointing angle. The classifier itself is surprisingly sophisticated, involving two Fisher discriminants, four artificial neural networks, one function-discriminant analysis and one linear discriminant — all combined into a blended boosted decision tree (BDT)! Interestingly, the analyzers use one-half of their event sample to train the artificial neural networks, etc., and the other to train the BDT. The performance of the BDT is validated with a sample of Ds→φπ decays, with φ→2μ.
The muon ID classifier M(PID) uses detector information from the ring-imaging cherenkov detectors, calorimeters and muon detectors to provide a likelihood that each muon candidate is a muon. The smallest of the three likelihoods is used as the discriminating quantity. M(PID) employs an artificial neural network that is validated using J/ψ decays to muon pairs.
The LHCb physicists take advantage of their large sample of Ds→μμπ decays to model the tri-muon invariant mass distribution accurately. The line shape is parameterized by a pair of Gaussians that are then rescaled to the mass and width of the tau lepton.
Backgrounds are not large, and consist of one irreducible background and several reducible ones, which is where M(PID) plays a key role. The signal rate is normalized to the rate of Ds→φπ decays, which is relatively well known, and which also has a robust signal in LHCb.
The paper contains tables of yields in grids of M(PID) and M(3body), and there is no signs of a signal. The picture from their Fig. 3 is clear:
No signal. Taking relatively modest systematics into account, they use the usual CLs method to set an upper limit. The actual result is BF(τ→3μ) < 4.6×10-8 at 90% CL, slightly better than expected. This limit is not quite as constraining as the earlier Belle result, but points the way to stronger results when larger data samples have been collected. The mass window shown above is not heavily populated by background.
I think this is a nice analysis, done intelligently. I hope I can learn more about the advanced analysis techniques employed.