Search for lepton-flavour-violating decays of the Higgs boson

The first direct search for lepton-flavour-violating decays of the recently discovered Higgs boson (H) is described. The search is performed in the and channels, where and are tau leptons reconstructed in the electronic and hadronic decay channels, respectively. The data sample used in this search was collected in pp collisions at a centre-of-mass energy of with the CMS experiment at the CERN LHC and corresponds to an integrated luminosity of 19.7. The sensitivity of the search is an order of magnitude better than the existing indirect limits. A slight excess of signal events with a significance of 2.4 standard deviations is observed. The -value of this excess at is 0.010. The best fit branching fraction is . A constraint on the branching fraction, at 95% confidence level is set. This limit is subsequently used to constrain the - Yukawa couplings to be less than .


CERN-PH-EP/2013-037 2019/\two@digits7/\two@digits14


Search for lepton-flavour-violating decays of the Higgs boson

The CMS Collaboration111See Appendix A for the list of collaboration members


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Published in Physics Letters B as doi:10.1016/j.physletb.2015.07.053.

© 2019 CERN for the benefit of the CMS Collaboration. CC-BY-3.0 license

1 Introduction

The discovery of the Higgs boson ([1, 2, 3] has generated great interest in exploring its properties. In the standard model (SM), lepton-flavour-violating (LFV) decays are forbidden if the theory is to be renormalizable [4]. If this requirement is relaxed, so the theory is valid only to a finite mass scale, then LFV couplings may be introduced. LFV decays can occur naturally in models with more than one Higgs doublet without abandoning renormalizability [5]. They also arise in supersymmetric models [6, 7, 8, 9], composite Higgs boson models [10, 11], models with flavour symmetries [12], Randall–Sundrum models [13, 14, 15], and many others [16, 17, 18, 19, 20, 21, 22, 23]. The presence of LFV couplings would allow , and transitions to proceed via a virtual Higgs boson [24, 25]. The experimental limits on these have recently been translated into constraints on the branching fractions  [26, 4]. The transition is strongly constrained by null search results for  [27], . However, the constraints on and are much less stringent. These come from searches for  [28, 29] and other rare decays [30], , and measurements [27]. Exclusion limits on the electron and muon electric dipole moments [31] also provide complementary constraints. These lead to the much less restrictive limits: , . The observation of the Higgs boson offers the possibility of sensitive direct searches for LFV Higgs boson decays. To date no dedicated searches have been performed. However, a theoretical reinterpretation of the ATLAS search results in terms of LFV decays by an independent group has been used to set limits at the 95% confidence level (CL) of ,  [4].

This letter describes a search for a LFV decay of a Higgs boson with at the CMS experiment. The 2012 dataset collected at a centre-of-mass energy of corresponding to an integrated luminosity of 19.7 is used. The search is performed in two channels, and , where and are tau leptons reconstructed in the electronic and hadronic decay channels, respectively. The signature is very similar to the SM and decays, where is a tau lepton decaying muonically, which have been studied by CMS in Refs. [32, 33] and ATLAS in Ref. [34], but with some significant kinematic differences. The comes promptly from the LFV decay and tends to have a larger momentum than in the SM case. There is only one tau lepton so there are typically fewer neutrinos in the decay. They are highly Lorentz boosted and tend to be collinear with the visible decay products.

The two channels are divided into categories based on the number of jets in order to separate the different boson production mechanisms. The signal sensitivity is enhanced by using different selection criteria for each category. The dominant production mechanism is gluon-gluon fusion but there is also a significant contribution from vector boson fusion which is enhanced by requiring jets to be present in the event. The dominant background in the channel is . Other much smaller backgrounds come from misidentified leptons in +jets, QCD multijets and events. In the channel the dominant background arises from misidentified leptons in +jets, QCD multijets and events. Less significant backgrounds come from and +jets. The principal backgrounds are estimated using data. There is also a small background from SM decays which is estimated with simulation. The presence or absence of a signal is established by fitting a mass distribution for signal and background using the asymptotic CL criterion [35, 36]. A “blind” analysis was performed. The data in the signal region were not studied until the selection criteria had been fixed and the background estimate finalized.

2 Detector and data sets

A detailed description of the CMS detector, together with a description of the coordinate system used and the relevant kinematic variables, can be found in ref. [37]. The momenta of charged particles are measured with a silicon pixel and strip tracker that covers the pseudorapidity range and is inside a 3.8 T axial magnetic field. Surrounding the tracker are a lead tungstate crystal electromagnetic calorimeter (ECAL) and a brass/scintillator hadron calorimeter, both consisting of a barrel assembly and two endcaps that extend to a pseudorapidity range of . A steel/quartz-fiber Cherenkov forward detector extends the calorimetric coverage to . The outermost component of the CMS detector is the muon system, consisting of gas-ionization detectors placed in the steel flux-return yoke of the magnet to measure the momenta of muons traversing the detector. The two-level CMS trigger system selects events of interest for permanent storage. The first trigger level, composed of custom hardware processors, uses information from the calorimeters and muon detectors to select events in less than 3.2. The high-level trigger software algorithms, executed on a farm of commercial processors, further reduce the event rate using information from all detector subsystems.

The channel selection begins by requiring a single trigger with a transverse momentum threshold in the pseudorapidity range , while the channel requires a - trigger with thresholds of 17 () for the and 8 () for the . Loose and identification criteria are applied at the trigger level. The leptons are also required to be isolated from other tracks and calorimeter energy deposits to maintain an acceptable trigger rate.

Simulated samples of signal and background events are produced using various Monte Carlo (MC) event generators, with the CMS detector response modeled with Geant4 [38]. Higgs bosons are produced in proton-proton collisions predominantly by gluon-gluon fusion, but also by vector boson fusion and in association with a or boson. It is assumed that the rate of new decays of the are sufficiently small that the narrow width approximation can be used. The LFV decay samples are produced with pythia 8.175 [39]. The background event samples with a SM are generated by powheg 1.0 [40, 41, 42, 43, 44] with the decays modelled by tauola [45]. The MadGraph 5.1 [46] generator is used for +jets, +jets, , and diboson production, and powheg for single top-quark production. The powheg and MadGraph generators are interfaced with pythia for parton shower and fragmentation.

3 Event reconstruction

A particle-flow (PF) algorithm [47, 48] combines the information from all CMS sub-detectors to identify and reconstruct the individual particles emerging from all vertices: charged hadrons, neutral hadrons, photons, muons, and electrons. These particles are then used to reconstruct jets, hadronic decays, and to quantify the isolation of leptons and photons. The missing transverse energy vector is the negative vector sum of all particle transverse momenta and its magnitude is referred to as . The variable is used to measure the separation between reconstructed objects in the detector, where is the azimuthal angle (in radians) of the trajectory of the object in the plane transverse to the direction of the proton beams.

The large number of proton interactions occurring per LHC bunch crossing (pileup), with an average of 21 in 2012, makes the identification of the vertex corresponding to the hard-scattering process nontrivial. This affects most of the object reconstruction algorithms: jets, lepton isolation, etc. The tracking system is able to separate collision vertices as close as 0.5 along the beam direction [49]. For each vertex, the sum of the of all tracks associated with the vertex is computed. The vertex for which this quantity is the largest is assumed to correspond to the hard-scattering process, and is referred to as the primary vertex in the event reconstruction.

Muons are reconstructed using two algorithms [50]: one in which tracks in the silicon tracker are matched to signals in the muon detectors, and another in which a global track fit is performed, seeded by signals in the muon systems. The muon candidates used in the analysis are required to be successfully reconstructed by both algorithms. Further identification criteria are imposed on the muon candidates to reduce the fraction of tracks misidentified as muons. These include the number of measurements in the tracker and in the muon systems, the fit quality of the global muon track and its consistency with the primary vertex.

Electron reconstruction requires the matching of an energy cluster in the ECAL with a track in the silicon tracker [51, 52]. Identification criteria based on the ECAL shower shape, matching between the track and the ECAL cluster, and consistency with the primary vertex are imposed. Electron identification relies on a multivariate technique that combines observables sensitive to the amount of bremsstrahlung along the electron trajectory, the geometrical and momentum matching between the electron trajectory and associated clusters, as well as shower-shape observables. Additional requirements are imposed to remove electrons produced by photon conversions.

Jets are reconstructed from all the PF objects using the anti jet clustering algorithm [53] implemented in FastJet [54], with a distance parameter of 0.5. The jet energy is corrected for the contribution of particles created in pileup interactions and in the underlying event. Particles from different pileup vertices can be clustered into a pileup jet, or significantly overlap a jet from the primary vertex below the threshold applied in the analysis. Such jets are identified and removed [55].

Hadronically decaying leptons are reconstructed and identified using the hadron plus strips (HPS) algorithm [56] which targets the main decay modes by selecting PF candidates with one charged hadron and up to two neutral pions, or with three charged hadrons. A photon from a neutral-pion decay can convert in the tracker material into an electron and a positron, which can then radiate bremsstrahlung photons. These particles give rise to several ECAL energy deposits at the same value and separated in azimuthal angle, and are reconstructed as several photons by the PF algorithm. To increase the acceptance for such converted photons, the neutral pions are identified by clustering the reconstructed photons in narrow strips along the azimuthal direction.

4 Event selection

The event selection consists of three steps. First, a loose selection defining the basic signature is applied. The sample is then divided into categories, according to the number of jets in the event. Finally, requirements are placed on a set of kinematic variables designed to suppress the backgrounds.

The loose selection for the channel requires an isolated (, ) and an isolated (, ) of opposite charge lying within a region of the detector that allows good identification. The and are required to be separated by . The channel requires an isolated (, ) and an isolated hadronically decaying (, ) of opposite charge. Leptons are also required to be isolated from any jet in the event with by and to have an impact parameter consistent with the primary vertex.

The events are then divided into categories within each channel according to the number of jets in the event. Jets are required to pass identification criteria [55], have and lie within the range . The zero jet category contains signal events predominantly produced by gluon-gluon fusion. The one-jet category contains signal events predominantly produced by gluon-gluon fusion and a negligibly small number of events produced in association with a W or Z boson decaying hadronically. The two jet category is enriched with signal events produced by vector boson fusion.

[GeV] 0-jet 1-jet 2-jet 0-jet 1-jet 2-jet
50 45 25 45 35 30
10 10 10
35 40 40
65 65 25
50 40 15
50 35 35
0.5 0.5 0.3
2.7 1.0
Table 2: Selection criteria for the kinematic variables after the loose selection.

The main variable for the discrimination between the signal and background is the collinear mass, , which provides an estimator of the reconstructed mass using the observed decay products. This is constructed using the collinear approximation [57] which is based on the observation that since the mass of the is much greater than the mass of the , the decay products are highly Lorentz boosted in the direction of the . The neutrino momenta can be approximated to be in the same direction as the other visible decay products of the and the component of the missing transverse energy in the transverse direction of the visible decay products is used to estimate the transverse component of the neutrino momentum. Figure 1 shows distribution for the signal and background compared to data for each of the categories in each channel after the loose selection. The simulated signal for is shown. The principal backgrounds are estimated with data using techniques described in Section 5. There is good agreement between data and the background estimation. The agreement is similar in all of the kinematic variables that are subsequently used to suppress backgrounds. The analysis is performed “blinded” in the region .

Figure 1: Distributions of the collinear mass for signal with for clarity, and background processes after the loose selection requirements for the LFV candidates for the different channels and categories compared to data. The shaded grey bands indicate the total uncertainty. The bottom panel in each plot shows the fractional difference between the observed data and the total estimated background. Top left: 0-jet; top right: 0-jet; middle left: 1-jet; middle right: 1-jet; bottom left: 2-jet; bottom right 2-jet.

Next, a set of kinematic variables is defined and the criteria for selection are determined by optimizing for where S and B are the expected signal and background event yields in the mass window . The signal event yield corresponds to the SM production cross section at with . This value for the LFV branching fraction is chosen because it corresponds to the limit from indirect measurements as described in Ref. [4]. The optimization was also performed assuming and negligible change in the optimal values of selection criteria was observed. The criteria for each category, and in each channel, are given in Table 2. The variables used are the lepton transverse momenta with ; azimuthal angles between the leptons ; azimuthal angle ; the transverse mass . Events in the 2-jet category are required to have two jets separated by a pseudorapidity gap () and to have a dijet invariant mass greater than 550. In the channel events in which at least one of the jets identified as coming from a b-quark decay are using the combined secondary-vertex b-tagging algorithm [58] are vetoed, to suppress backgrounds from top quark decays.

5 Background Processes

The contributions of the dominant background processes are estimated with data while less significant backgrounds are estimated using simulation. The largest backgrounds come from and from misidentified leptons in +jets and QCD multijet production.


The background contribution is estimated using an embedding technique [59, 33] as follows. A sample of events is taken from data using a loose selection. The two muons are then replaced with PF particles resulting from the reconstruction of simulated lepton decays. Thus, the key features of the event topology such as the jets, missing transverse energy and underlying event are taken directly from data with only the decays being simulated. The normalization of the sample is obtained from the simulation. The technique is validated by comparing the lepton identification efficiencies estimated with an embedded decay sample, using simulated events, to those from simulated decays.

5.2 Misidentified leptons

Leptons can arise from misidentified PF objects in +jets and QCD multijet processes. This background is estimated with data. A sample with similar kinematic properties to the signal sample but enriched in +jets and QCD multijets is defined. Then the probability for PF objects to be misidentified as leptons is measured in an independent data set, and this probability is applied to the enriched sample to compute the misidentified lepton background in the signal region. The technique is shown schematically in Table 4 in which four regions are defined including the signal and background enriched regions and two control regions used for validation of the technique. It is employed slightly differently in the and channels. The lepton isolation requirements used to define the enriched regions in each channel are slightly different.

In the channel, region I is the signal region in which an isolated and an isolated are required. Region III is a data sample in which all the analysis selection criteria are applied except that one of the leptons is required to be not-isolated. Thus, there are two components: events with an isolated and not-isolated events, as well as events with an isolated and not-isolated events. There is negligible number of signal events in region III. Regions II and IV are data samples formed with the same selection criteria as regions I and III, respectively, but with same-sign rather than opposite-sign leptons. The kinematic distributions of the same-sign samples are very similar to the opposite-sign samples

Region I Region II
(isolated) (isolated)
(isolated) (isolated)
Region III Region IV
(isolated) (isolated)
(not-isolated ) (not-isolated)
Table 4: Schematic to illustrate the application of the method used to estimate the misidentified lepton () background. Samples are defined by the charge of the two leptons and by the isolation requirements on each. Charged conjugates are assumed.

The sample in region III is dominated by +jets and QCD multijets but with small contributions from and that are subtracted using simulation. The misidentified background in region I is then estimated by multiplying the event yield in region III by a factor , where is the ratio of not-isolated to isolated ’s. It is computed in an independent data sample , where is an object identified as a , in bins of and . The sample is corrected for contributions from and using simulated samples. A correction is made to account for the difference in trigger efficiency for selection of events with isolated and not-isolated versus the events with isolated and isolated . The misidentified background is computed in exactly the same way. The technique is validated by using the same-sign data from regions II and IV as shown schematically in Table 4. In Fig. 2(left) the observed data yield in region II is compared to the estimate from scaling the region IV sample by the measured misidentification rates. The region II sample is dominated by misidentified leptons but also includes small contributions of true leptons arising from vector boson decays, estimated with simulated samples.

Figure 2: Distributions of for region II compared to the estimate from scaling the region IV sample by the measured misidentification rates. The bottom panel in each plot shows the fractional difference between the observed data and the estimate. Left: . Right: .

In the channel, the candidate can come from a misidentified jet with a number of sources, predominantly and QCD multijets, but also and . In this case the enriched background regions are defined with candidates that pass a looser isolation requirement, but do not pass the signal isolation requirement. The misidentification rate is then defined as the fraction of candidates with the looser isolation that also pass the signal isolation requirement. It is measured in observed events, where is an object identified as a . The misidentification rate measured in data is checked by comparing to that measured in simulation and found to be in good agreement. The misidentified background in the signal region (region I) is estimated by multiplying the event yield in region III by a factor . The procedure is validated with same-sign events in the same way as for the channel above. Figure 2(right) shows the data in region II compared to the estimate from scaling region IV by the misidentification rates.

The method assumes that the misidentification rate in events is the same as for +jets and QCD processes. To test this assumption the misidentification rates are measured in a QCD jet data control sample. They are found to be consistent. Finally as a cross-check the study has been performed also as a function of the number of jets in the event and similar agreement is found.

5.3 Other backgrounds

The SM decays in the channel provide a small background that is estimated with simulation. This background is suppressed by the kinematic selection criteria and peaks below 125. The leptonic decay from produces opposite-sign dileptons and . This background is estimated with simulated events using the shape of the distribution from simulation and a data control region for normalization. The control region is the 2-jet selection but with the additional requirement that at least one of the jets is b-tagged in order to enhance the contribution. Other smaller backgrounds come from , , and single top-quark production. Each of these is estimated with simulation.

6 Systematic uncertainties

To set upper bounds on the signal strength, or determine a signal significance, we use the CL method [35, 36]. A binned likelihood is used, based on the distributions of for the signal and the various background sources. Systematic uncertainties are represented by nuisance parameters, some of which only affect the background and signal normalizations, while others affect the shape and/or normalization of the distributions.

6.1 Normalization uncertainties

Systematic uncertainty
0-Jet 1-Jet 2-Jets 0-Jet 1-Jet 2-Jets
electron trigger/ID/isolation 3 3 3
muon trigger/ID/isolation 2 2 2 2 2 2
hadronic tau efficiency 9 9 9
luminosity 2.6 2.6 2.6 2.6 2.6 2.6
background 3+3* 3+5* 3+10* 3+5* 3+5* 3+10*
background 30 30 30 30 30 30
misidentified background 40 40 40
misidentified background 30+10* 30 30
background 15 15 15 15 15 65
background 10 10 10+10* 10 10 10+33*
background 100 100 100
b-tagging veto 3 3 3
single top production background 10 10 10 10 10 10
Table 6: Systematic uncertainties in the expected event yield in %. All uncertainties are treated as correlated between the categories, except where there are two numbers. In this case the number denoted with * is treated as uncorrelated between categories and the total uncertainty is the sum in quadrature of the two numbers.

The uncertainties are summarized in Tables 6 and 8. The uncertainties in the and selection efficiency (trigger, identification and isolation) are estimated using the “tag and probe” technique in data [59]. The identification efficiency of hadronic decays is estimated using the “tag and probe” technique in data [56]. The uncertainty in the background comes predominantly from the uncertainty in the efficiency. The uncertainties in the estimation of the misidentified lepton rate come from the difference in rates measured in different data samples (QCD multijets and +jets). The uncertainty in the production cross section of the backgrounds that have been estimated by simulation is also included.

There are several uncertainties on the production cross section, which depend on the production mechanism contribution and the analysis category. They are given in Table 8. These affect the LFV and the SM background equally, and are treated as 100% correlated. The parton distribution function (PDF) uncertainty is evaluated by comparing the yields in each category, when spanning the parameter range of a number of different independent PDF sets including CT10 [60], MSTW [61], NNPDF [62] as recommended by PDF4LHC [63]. The scale uncertainty is estimated by varying the renormalization, , and factorization scales, , up and down by one half or two times the nominal scale () under the constraint  [64]. The underlying event and parton shower uncertainty is estimated by using two different pythia tunes, AUET2 and Z2*. Anticorrelations arise due to migration of events between the categories and are expressed as negative numbers.

Systematic uncertainty Gluon-Gluon Fusion Vector Boson Fusion
0-Jets 1-Jets 2-Jets 0-Jet 1-Jet 2-Jets
parton distribution function
renormalization/factorization scale
underlying event/parton shower 1
Table 8: Theoretical uncertainties in % for Higgs boson production. Anticorrelations arise due to migration of events between the categories and are expressed as negative numbers.

6.2 shape uncertainties

The systematic uncertainties that lead to a change in the shape of the distribution are summarized in Table 10.

Systematic uncertainty
hadronic tau energy scale 3
jet energy scale 3–7 3–7
unclustered energy scale 10 10
bias 100
Table 10: Systematic uncertainties in % for the shape of the signal and background templates.

In the embedded distribution, used to estimate the background, a 1% shift has been observed with respect to simulations by comparing the means of both distributions. This occurs only in the channel. The distribution has been corrected for this effect and a 100% uncertainty on this shift is used as a systematic uncertainty for the possible bias. The jet energy scale has been studied extensively and a standard prescription for corrections [65] is used in all CMS analyses. The overall scale is set using +jets events and the most significant uncertainty arises from the photon energy scale. A number of other uncertainties such as jet fragmentation modeling, single pion response and uncertainties in the pileup corrections are also included. The jet energy scale uncertainties (3–7%) are applied as a function of and , including all correlations, to all jets in the event, propagated to the missing energy, and the resultant distribution is used in the fit. There is also an additional uncertainty to account for the unclustered energy scale uncertainty. The unclustered energy comes from jets below 10 and PF candidates not within jets. It is also propagated to the missing transverse energy. These effects cause a shift of the distribution. The energy scale is estimated by comparing events in data and simulation. An uncertainty of 3% is derived from this comparison. The uncertainty is applied by shifting the of the candidates in the event and using the resultant distribution in the fit. Finally, the distributions used in the fit have a statistical uncertainty in each mass bin that is included as an uncertainty which is uncorrelated between the bins.

Potential uncertainties in the shape of the misidentified lepton backgrounds have also been considered. In the channel the misidentified lepton rates are measured and applied in bins of lepton and . These rates are all adjusted up or down by one standard deviation () and the differences in the shape of the resultant distributions are then used as nuisance parameters in the fit. In the channel the misidentification rate is found to be approximately flat in and . To estimate the systematic uncertainty the distribution of is fit with a linear function and the rate recomputed from the fitted slope and intercept. The modified distribution that results from the recomputed background is then used to evaluate the systematic uncertainty.

Figure 3: Distributions of the collinear mass after fitting for signal and background for the LFV candidates in the different channels and categories compared to data. The distribution of the simulated LFV Higgs boson sample is shown for the best fit branching fraction of . The bottom panel in each plot shows the fractional difference between the observed data and the fitted background. Top left: 0-jet; top right: 0-jet; middle left: 1-jet; middle right: 1-jet; bottom left: 2-jet; bottom right 2-jet.

7 Results

The distributions after the fit for signal and background contributions are shown in Fig. 3 and the event yields in the mass range are shown in Table 12. The different channels and categories are combined to set a CL upper limit on the branching fraction of LFV decay in the channel, .

0-Jet 1-Jet 2-Jets 0-Jet 1-Jet 2-Jets
misidentified leptons
SM background
sum of backgrounds
LFV Higgs boson signal
Table 12: Event yields in the signal region, after fitting for signal and background. The expected contributions are normalized to an integrated luminosity of 19.7. The LFV Higgs boson signal is the expected yield for with the SM Higgs boson cross section.

The observed and the median expected CL upper limits on the for the mass at 125 are given for each category in Table 14. Combining all the channels, an expected upper limit of is obtained. The observed upper limit is which is above the expected limit due to an excess of the observed number of events above the background prediction. The fit can then be used to estimate the branching fraction if this excess were to be interpreted as a signal. The best fit values for the branching fractions are given in Table 14. The limits and best fit branching fractions are also summarized graphically in Fig. 4. The combined categories give a best fit of . The combined excess is 2.4 standard deviations which corresponds to a -value of 0.010 at . The observed and expected distributions combined for all channels and categories are shown in Fig. 5. The distributions are weighted in each channel and category by the ratio, where S and B are respectively the signal and background yields corresponding to the result of the global fit. The values for S and B are obtained in the region.

Expected Limits
0-Jet 1-Jet 2-Jets
(%) (%) (%)
1.32 (0.67) 1.66 (0.85) 3.77 (1.92)
2.34 (1.19) 2.07 (1.06) 2.31 (1.18)
0.75 (0.38 )
Observed Limits
2.04 2.38 3.84
2.61 2.22 3.68
Best Fit Branching Fractions
Table 14: The expected upper limits, observed upper limits and best fit values for the branching fractions for different jet categories for the process. The one standard-deviation probability intervals around the expected limits are shown in parentheses.
Figure 4: Left: 95% CL Upper limits by category for the LFV decays. Right: best fit branching fractions by category.
Figure 5: Left: Distribution of for all categories combined, with each category weighted by significance (). The significance is computed for the integral of the bins in the range using . The simulated Higgs signal shown is for . The bottom panel shows the fractional difference between the observed data and the fitted background. Right: background subtracted distribution for all categories combined.

8 Limits on lepton-flavour-violating couplings

Figure 6: Constraints on the flavour-violating Yukawa couplings, and . The black dashed lines are contours of for reference. The expected limit (red solid line) with one sigma (green) and two sigma (yellow) bands, and observed limit (black solid line) are derived from the limit on from the present analysis. The shaded regions are derived constraints from null searches for (dark green) and (lighter green). The yellow line is the limit from a theoretical reinterpretation of an ATLAS search [4]. The light blue region indicates the additional parameter space excluded by our result. The purple diagonal line is the theoretical naturalness limit .

The constraint on can be interpreted in terms of LFV Yukawa couplings [4]. The LFV decays , , arise at tree level from the assumed flavour-violating Yukawa interactions, where denote the leptons, and . The decay width in terms of the Yukawa couplings is given by:

and the branching fraction by:

The SM decay width is assumed to be  [66] for . The 95% CL constraint on the Yukawa couplings derived from and the expression for the branching fraction above is:

Figure 6 compares this result to the constraints from previous indirect measurements.

9 Summary

The first direct search for lepton-flavour-violating decays of a Higgs boson to a - pair, based on the full 8 data set collected by CMS in 2012 is presented. It improves upon previously published indirect limits [26, 4] by an order of magnitude. A slight excess of events with a significance of is observed, corresponding to a -value of 0.010. The best fit branching fraction is . A constraint of at 95% confidence level is set. The limit is used to constrain the Yukawa couplings, . It improves the current bound by an order of magnitude.


We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at CERN and at other CMS institutes for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centres and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies: BMWFW and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST, and NSFC (China); COLCIENCIAS (Colombia); MSES and CSF (Croatia); RPF (Cyprus); MoER, ERC IUT and ERDF (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF (Germany); GSRT (Greece); OTKA and NIH (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); LAS (Lithuania); MOE and UM (Malaysia); CINVESTAV, CONACYT, SEP, and UASLP-FAI (Mexico); MBIE (New Zealand); PAEC (Pakistan); MSHE and NSC (Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom, RAS and RFBR (Russia); MESTD (Serbia); SEIDI and CPAN (Spain); Swiss Funding Agencies (Switzerland); MST (Taipei); ThEPCenter, IPST, STAR and NSTDA (Thailand); TUBITAK and TAEK (Turkey); NASU and SFFR (Ukraine); STFC (United Kingdom); DOE and NSF (USA).

Individuals have received support from the Marie-Curie programme and the European Research Council and EPLANET (European Union); the Leventis Foundation; the A. P. Sloan Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science Policy Office; the Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Council of Science and Industrial Research, India; the HOMING PLUS programme of Foundation for Polish Science, cofinanced from European Union, Regional Development Fund; the Compagnia di San Paolo (Torino); the Consorzio per la Fisica (Trieste); MIUR project 20108T4XTM (Italy); the Thalis and Aristeia programmes cofinanced by EU-ESF and the Greek NSRF; and the National Priorities Research Program by Qatar National Research Fund.


Appendix A The CMS Collaboration

Yerevan Physics Institute, Yerevan, Armenia
V. Khachatryan, A.M. Sirunyan, A. Tumasyan Institut für Hochenergiephysik der OeAW, Wien, Austria
W. Adam, T. Bergauer, M. Dragicevic, J. Erö, M. Friedl, R. Frühwirth\@textsuperscript1, V.M. Ghete, C. Hartl, N. Hörmann, J. Hrubec, M. Jeitler\@textsuperscript1, W. Kiesenhofer, V. Knünz, M. Krammer\@textsuperscript1, I. Krätschmer, D. Liko, I. Mikulec, D. Rabady\@textsuperscript2, B. Rahbaran, H. Rohringer, R. Schöfbeck, J. Strauss, W. Treberer-Treberspurg, W. Waltenberger, C.-E. Wulz\@textsuperscript1 National Centre for Particle and High Energy Physics, Minsk, Belarus
V. Mossolov, N. Shumeiko, J. Suarez Gonzalez Universiteit Antwerpen, Antwerpen, Belgium
S. Alderweireldt, S. Bansal, T. Cornelis, E.A. De Wolf, X. Janssen, A. Knutsson, J. Lauwers, S. Luyckx, S. Ochesanu, R. Rougny, M. Van De Klundert, H. Van Haevermaet, P. Van Mechelen, N. Van Remortel, A. Van Spilbeeck Vrije Universiteit Brussel, Brussel, Belgium
F. Blekman, S. Blyweert, J. D’Hondt, N. Daci, N. Heracleous, J. Keaveney, S. Lowette, M. Maes, A. Olbrechts, Q. Python, D. Strom, S. Tavernier, W. Van Doninck, P. Van Mulders, G.P. Van Onsem, I. Villella Université Libre de Bruxelles, Bruxelles, Belgium
C. Caillol, B. Clerbaux, G. De Lentdecker, D. Dobur, L. Favart, A.P.R. Gay, A. Grebenyuk, A. Léonard, A. Mohammadi, L. Perniè\@textsuperscript2, A. Randle-conde, T. Reis, T. Seva, L. Thomas, C. Vander Velde, P. Vanlaer, J. Wang, F. Zenoni Ghent University, Ghent, Belgium
V. Adler, K. Beernaert, L. Benucci, A. Cimmino, S. Costantini, S. Crucy, A. Fagot, G. Garcia, J. Mccartin, A.A. Ocampo Rios, D. Poyraz, D. Ryckbosch, S. Salva Diblen, M. Sigamani, N. Strobbe, F. Thyssen, M. Tytgat, E. Yazgan, N. Zaganidis Université Catholique de Louvain, Louvain-la-Neuve, Belgium
S. Basegmez, C. Beluffi\@textsuperscript3, G. Bruno, R. Castello, A. Caudron, L. Ceard, G.G. Da Silveira, C. Delaere, T. du Pree, D. Favart, L. Forthomme, A. Giammanco\@textsuperscript4, J. Hollar, A. Jafari, P. Jez, M. Komm, V. Lemaitre, C. Nuttens, D. Pagano, L. Perrini, A. Pin, K. Piotrzkowski, A. Popov\@textsuperscript5, L. Quertenmont, M. Selvaggi, M. Vidal Marono, J.M. Vizan Garcia Université de Mons, Mons, Belgium
N. Beliy, T. Caebergs, E. Daubie, G.H. Hammad Centro Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil
W.L. Aldá Júnior, G.A. Alves, L. Brito, M. Correa Martins Junior, T. Dos Reis Martins, J. Molina, C. Mora Herrera, M.E. Pol, P. Rebello Teles Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
W. Carvalho, J. Chinellato\@textsuperscript6, A. Custódio, E.M. Da Costa, D. De Jesus Damiao, C. De Oliveira Martins, S. Fonseca De Souza, H. Malbouisson, D. Matos Figueiredo, L. Mundim, H. Nogima, W.L. Prado Da Silva, J. Santaolalla, A. Santoro, A. Sznajder, E.J. Tonelli Manganote\@textsuperscript6, A. Vilela Pereira Universidade Estadual Paulista ,  Universidade Federal do ABC ,  São Paulo, Brazil
C.A. Bernardes, S. Dogra, T.R. Fernandez Perez Tomei, E.M. Gregores, P.G. Mercadante, S.F. Novaes, Sandra S. Padula Institute for Nuclear Research and Nuclear Energy, Sofia, Bulgaria
A. Aleksandrov, V. Genchev\@textsuperscript2, R. Hadjiiska, P. Iaydjiev, A. Marinov, S. Piperov, M. Rodozov, S. Stoykova, G. Sultanov, M. Vutova University of Sofia, Sofia, Bulgaria
A. Dimitrov, I. Glushkov, L. Litov, B. Pavlov, P. Petkov Institute of High Energy Physics, Beijing, China
J.G. Bian, G.M. Chen, H.S. Chen, M. Chen, T. Cheng, R. Du, C.H. Jiang, R. Plestina\@textsuperscript7, F. Romeo, J. Tao, Z. Wang State Key Laboratory of Nuclear Physics and Technology, Peking University, Beijing, China
C. Asawatangtrakuldee, Y. Ban, S. Liu, Y. Mao, S.J. Qian, D. Wang, Z. Xu, F. Zhang\@textsuperscript8, L. Zhang, W. Zou Universidad de Los Andes, Bogota, Colombia
C. Avila, A. Cabrera, L.F. Chaparro Sierra, C. Florez, J.P. Gomez, B. Gomez Moreno, J.C. Sanabria University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, Split, Croatia
N. Godinovic, D. Lelas, D. Polic, I. Puljak University of Split, Faculty of Science, Split, Croatia
Z. Antunovic, M. Kovac Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, K. Kadija, J. Luetic, D. Mekterovic, L. Sudic University of Cyprus, Nicosia, Cyprus
A. Attikis, G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos, P.A. Razis, H. Rykaczewski Charles University, Prague, Czech Republic
M. Bodlak, M. Finger, M. Finger Jr.\@textsuperscript9 Academy of Scientific Research and Technology of the Arab Republic of Egypt, Egyptian Network of High Energy Physics, Cairo, Egypt
Y. Assran\@textsuperscript10, A. Ellithi Kamel\@textsuperscript11, M.A. Mahmoud\@textsuperscript12, A. Radi\@textsuperscript13\@textsuperscript14 National Institute of Chemical Physics and Biophysics, Tallinn, Estonia
M. Kadastik, M. Murumaa, M. Raidal, A. Tiko Department of Physics, University of Helsinki, Helsinki, Finland
P. Eerola, M. Voutilainen Helsinki Institute of Physics, Helsinki, Finland
J. Härkönen, V. Karimäki, R. Kinnunen, M.J. Kortelainen, T. Lampén, K. Lassila-Perini, S. Lehti, T. Lindén, P. Luukka, T. Mäenpää, T. Peltola, E. Tuominen, J. Tuominiemi, E. Tuovinen, L. Wendland Lappeenranta University of Technology, Lappeenranta, Finland
J. Talvitie, T. Tuuva DSM/IRFU, CEA/Saclay, Gif-sur-Yvette, France
M. Besancon, F. Couderc, M. Dejardin, D. Denegri, B. Fabbro, J.L. Faure, C. Favaro, F. Ferri, S. Ganjour, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry, E. Locci, J. Malcles, J. Rander, A. Rosowsky, M. Titov Laboratoire Leprince-Ringuet, Ecole Polytechnique, IN2P3-CNRS, Palaiseau, France
S. Baffioni, F. Beaudette, P. Busson, E. Chapon, C. Charlot, T. Dahms, L. Dobrzynski, N. Filipovic, A. Florent, R. Granier de Cassagnac, L. Mastrolorenzo, P. Miné, I.N. Naranjo, M. Nguyen, C. Ochando, G. Ortona, P. Paganini, S. Regnard, R. Salerno, J.B. Sauvan, Y. Sirois, C. Veelken, Y. Yilmaz, A. Zabi Institut Pluridisciplinaire Hubert Curien, Université de Strasbourg, Université de Haute Alsace Mulhouse, CNRS/IN2P3, Strasbourg, France
J.-L. Agram\@textsuperscript15, J. Andrea, A. Aubin, D. Bloch, J.-M. Brom, E.C. Chabert, C. Collard, E. Conte\@textsuperscript15, J.-C. Fontaine\@textsuperscript15, D. Gelé, U. Goerlach, C. Goetzmann, A.-C. Le Bihan, K. Skovpen, P. Van Hove Centre de Calcul de l’Institut National de Physique Nucleaire et de Physique des Particules, CNRS/IN2P3, Villeurbanne, France
S. Gadrat Université de Lyon, Université Claude Bernard Lyon 1,  CNRS-IN2P3, Institut de Physique Nucléaire de Lyon, Villeurbanne, France
S. Beauceron, N. Beaupere, C. Bernet\@textsuperscript7, G. Boudoul\@textsuperscript2, E. Bouvier, S. Brochet, C.A. Carrillo Montoya, J. Chasserat, R. Chierici, D. Contardo\@textsuperscript2, B. Courbon, P. Depasse, H. El Mamouni, J. Fan, J. Fay, S. Gascon, M. Gouzevitch, B. Ille, T. Kurca, M. Lethuillier, L. Mirabito, A.L. Pequegnot, S. Perries, J.D. Ruiz Alvarez, D. Sabes, L. Sgandurra, V. Sordini, M. Vander Donckt, P. Verdier, S. Viret, H. Xiao E. Andronikashvili Institute of Physics, Academy of Science, Tbilisi, Georgia
L. Rurua RWTH Aachen University, I. Physikalisches Institut, Aachen, Germany
C. Autermann, S. Beranek, M. Bontenackels, M. Edelhoff, L. Feld, A. Heister, K. Klein, M. Lipinski, A. Ostapchuk, M. Preuten, F. Raupach, J. Sammet, S. Schael, J.F. Schulte, H. Weber, B. Wittmer, V. Zhukov\@textsuperscript5 RWTH Aachen University, III. Physikalisches Institut A,  Aachen, Germany
M. Ata, M. Brodski, E. Dietz-Laursonn, D. Duchardt, M. Erdmann, R. Fischer, A. Güth, T. Hebbeker, C. Heidemann, K. Hoepfner, D. Klingebiel, S. Knutzen, P. Kreuzer, M. Merschmeyer, A. Meyer, P. Millet, M. Olschewski, K. Padeken, P. Papacz, H. Reithler, S.A. Schmitz, L. Sonnenschein, D. Teyssier, S. Thüer RWTH Aachen University, III. Physikalisches Institut B,  Aachen, Germany
V. Cherepanov, Y. Erdogan, G. Flügge, H. Geenen, M. Geisler, W. Haj Ahmad, F. Hoehle, B. Kargoll, T. Kress, Y. Kuessel, A. Künsken, J. Lingemann\@textsuperscript2, A. Nowack, I.M. Nugent, C. Pistone, O. Pooth, A. Stahl Deutsches Elektronen-Synchrotron, Hamburg, Germany
M. Aldaya Martin, I. Asin, N. Bartosik, J. Behr, U. Behrens, A.J. Bell, A. Bethani, K. Borras, A. Burgmeier, A. Cakir, L. Calligaris, A. Campbell, S. Choudhury, F. Costanza, C. Diez Pardos, G. Dolinska, S. Dooling, T. Dorland, G. Eckerlin, D. Eckstein, T. Eichhorn, G. Flucke, J. Garay Garcia, A. Geiser, A. Gizhko, P. Gunnellini, J. Hauk, M. Hempel\@textsuperscript16, H. Jung, A. Kalogeropoulos, O. Karacheban\@textsuperscript16, M. Kasemann, P. Katsas, J. Kieseler, C. Kleinwort, I. Korol, D. Krücker, W. Lange, J. Leonard, K. Lipka, A. Lobanov, W. Lohmann\@textsuperscript16, B. Lutz, R. Mankel, I. Marfin\@textsuperscript16, I.-A. Melzer-Pellmann, A.B. Meyer, G. Mittag, J. Mnich, A. Mussgiller, S. Naumann-Emme, A. Nayak, E. Ntomari, H. Perrey, D. Pitzl, R. Placakyte, A. Raspereza, P.M. Ribeiro Cipriano, B. Roland, E. Ron, M.Ö. Sahin, J. Salfeld-Nebgen, P. Saxena, T. Schoerner-Sadenius, M. Schröder, C. Seitz, S. Spannagel, A.D.R. Vargas Trevino, R. Walsh, C. Wissing University of Hamburg, Hamburg, Germany
V. Blobel, M. Centis Vignali, A.R. Draeger, J. Erfle, E. Garutti, K. Goebel, M. Görner, J. Haller, M. Hoffmann, R.S. Höing, A. Junkes, H. Kirschenmann, R. Klanner, R. Kogler, T. Lapsien, T. Lenz, I. Marchesini, D. Marconi, J. Ott, T. Peiffer, A. Perieanu, N. Pietsch, J. Poehlsen, T. Poehlsen, D. Rathjens, C. Sander, H. Schettler, P. Schleper, E. Schlieckau, A. Schmidt, M. Seidel, V. Sola, H. Stadie, G. Steinbrück, D. Troendle, E. Usai, L. Vanelderen, A. Vanhoefer Institut für Experimentelle Kernphysik, Karlsruhe, Germany
C. Barth, C. Baus, J. Berger, C. Böser, E. Butz, T. Chwalek, W. De Boer, A. Descroix, A. Dierlamm, M. Feindt, F. Frensch, M. Giffels, A. Gilbert, F. Hartmann\@textsuperscript2, T. Hauth, U. Husemann, I. Katkov\@textsuperscript5, A. Kornmayer\@textsuperscript2, P. Lobelle Pardo, M.U. Mozer, T. Müller, Th. Müller, A. Nürnberg, G. Quast, K. Rabbertz, S. Röcker, H.J. Simonis, F.M. Stober, R. Ulrich, J. Wagner-Kuhr, S. Wayand, T. Weiler, R. Wolf Institute of Nuclear and Particle Physics (INPP),  NCSR Demokritos, Aghia Paraskevi, Greece
G. Anagnostou, G. Daskalakis, T. Geralis, V.A. Giakoumopoulou, A. Kyriakis, D. Loukas, A. Markou, C. Markou, A. Psallidas, I. Topsis-Giotis University of Athens, Athens, Greece
A. Agapitos, S. Kesisoglou, A. Panagiotou, N. Saoulidou, E. Stiliaris, E. Tziaferi University of Ioánnina, Ioánnina, Greece
X. Aslanoglou, I. Evangelou, G. Flouris, C. Foudas, P. Kokkas, N. Manthos, I. Papadopoulos, E. Paradas, J. Strologas Wigner Research Centre for Physics, Budapest, Hungary
G. Bencze, C. Hajdu, P. Hidas, D. Horvath\@textsuperscript17, F. Sikler, V. Veszpremi, G. Vesztergombi\@textsuperscript18, A.J. Zsigmond Institute of Nuclear Research ATOMKI, Debrecen, Hungary
N. Beni, S. Czellar, J. Karancsi\@textsuperscript19, J. Molnar, J. Palinkas, Z. Szillasi University of Debrecen, Debrecen, Hungary
A. Makovec, P. Raics, Z.L. Trocsanyi, B. Ujvari National Institute of Science Education and Research, Bhubaneswar, India
S.K. Swain Panjab University, Chandigarh, India
S.B. Beri, V. Bhatnagar, R. Gupta, U.Bhawandeep, A.K. Kalsi, M. Kaur, R. Kumar, M. Mittal, N. Nishu, J.B. Singh University of Delhi, Delhi, India
Ashok Kumar, Arun Kumar, S. Ahuja, A. Bhardwaj, B.C. Choudhary, A. Kumar, S. Malhotra, M. Naimuddin, K. Ranjan, V. Sharma Saha Institute of Nuclear Physics, Kolkata, India
S. Banerjee, S. Bhattacharya, K. Chatterjee, S. Dutta, B. Gomber, Sa. Jain, Sh. Jain, R. Khurana, A. Modak, S. Mukherjee, D. Roy, S. Sarkar, M. Sharan Bhabha Atomic Research Centre, Mumbai, India
A. Abdulsalam, D. Dutta, V. Kumar, A.K. Mohanty\@textsuperscript2, L.M. Pant, P. Shukla, A. Topkar Tata Institute of Fundamental Research, Mumbai, India
T. Aziz, S. Banerjee, S. Bhowmik\@textsuperscript20, R.M. Chatterjee, R.K. Dewanjee, S. Dugad, S. Ganguly, S. Ghosh, M. Guchait, A. Gurtu\@textsuperscript21, G. Kole, S. Kumar, M. Maity\@textsuperscript20, G. Majumder, K. Mazumdar, G.B. Mohanty, B. Parida, K. Sudhakar, N. Wickramage\@textsuperscript22 Indian Institute of Science Education and Research (IISER),  Pune, India
S. Sharma Institute for Research in Fundamental Sciences (IPM),  Tehran, Iran
H. Bakhshiansohi, H. Behnamian, S.M. Etesami\@textsuperscript23, A. Fahim\@textsuperscript24, R. Goldouzian, M. Khakzad, M. Mohammadi Najafabadi, M. Naseri, S. Paktinat Mehdiabadi, F. Rezaei Hosseinabadi, B. Safarzadeh\@textsuperscript25, M. Zeinali University College Dublin, Dublin, Ireland
M. Felcini, M. Grunewald INFN Sezione di Bari , Università di Bari , Politecnico di Bari ,  Bari, Italy
M. Abbrescia, C. Calabria, S.S. Chhibra, A. Colaleo, D. Creanza, L. Cristella, N. De Filippis, M. De Palma, L. Fiore, G. Iaselli, G. Maggi, M. Maggi, S. My, S. Nuzzo, A. Pompili, G. Pugliese, R. Radogna\@textsuperscript2, G. Selvaggi, A. Sharma, L. Silvestris\@textsuperscript2, R. Venditti, P. Verwilligen INFN Sezione di Bologna , Università di Bologna ,  Bologna, Italy
G. Abbiendi, A.C. Benvenuti, D. Bonacorsi, S. Braibant-Giacomelli, L. Brigliadori, R. Campanini, P. Capiluppi, A. Castro, F.R. Cavallo, G. Codispoti, M. Cuffiani, G.M. Dallavalle, F. Fabbri, A. Fanfani, D. Fasanella, P. Giacomelli, C. Grandi, L. Guiducci, S. Marcellini, G. Masetti, A. Montanari, F.L. Navarria, A. Perrotta, A.M. Rossi, T. Rovelli, G.P. Siroli, N. Tosi, R. Travaglini INFN Sezione di Catania , Università di Catania , CSFNSM ,  Catania, Italy
S. Albergo, G. Cappello, M. Chiorboli, S. Costa, F. Giordano\@textsuperscript2, R. Potenza, A. Tricomi, C. Tuve INFN Sezione di Firenze , Università di Firenze ,  Firenze, Italy
G. Barbagli, V. Ciulli, C. Civinini, R. D’Alessandro, E. Focardi, E. Gallo, S. Gonzi, V. Gori, P. Lenzi, M. Meschini, S. Paoletti, G. Sguazzoni, A. Tropiano INFN Laboratori Nazionali di Frascati, Frascati, Italy
L. Benussi, S. Bianco, F. Fabbri, D. Piccolo INFN Sezione di Genova , Università di Genova ,  Genova, Italy
R. Ferretti, F. Ferro, M. Lo Vetere, E. Robutti, S. Tosi INFN Sezione di Milano-Bicocca , Università di Milano-Bicocca ,  Milano, Italy
M.E. Dinardo, S. Fiorendi, S. Gennai\@textsuperscript2, R. Gerosa\@textsuperscript2, A. Ghezzi, P. Govoni, M.T. Lucchini\@textsuperscript2, S. Malvezzi, R.A. Manzoni, A. Martelli, B. Marzocchi\@textsuperscript2, D. Menasce, L. Moroni, M. Paganoni, D. Pedrini, S. Ragazzi, N. Redaelli, T. Tabarelli de Fatis INFN Sezione di Napoli , Università di Napoli ’Federico II’ , Napoli, Italy, Università della Basilicata , Potenza, Italy, Università G. Marconi , Roma, Italy
S. Buontempo, N. Cavallo, S. Di Guida\@textsuperscript2, F. Fabozzi, A.O.M. Iorio, L. Lista, S. Meola\@textsuperscript2, M. Merola, P. Paolucci\@textsuperscript2 INFN Sezione di Padova , Università di Padova , Padova, Italy, Università di Trento , Trento, Italy
P. Azzi, N. Bacchetta, D. Bisello, A. Branca, R. Carlin, P. Checchia, M. Dall’Osso, T. Dorigo, U. Dosselli, F. Gasparini, U. Gasparini, A. Gozzelino, K. Kanishchev, S. Lacaprara, M. Margoni, A.T. Meneguzzo, J. Pazzini, N. Pozzobon, P. Ronchese, F. Simonetto, E. Torassa, M. Tosi, P. Zotto, A. Zucchetta, G. Zumerle INFN Sezione di Pavia , Università di Pavia ,  Pavia, Italy
M. Gabusi, S.P. Ratti, V. Re, C. Riccardi, P. Salvini, P. Vitulo INFN Sezione di Perugia , Università di Perugia ,  Perugia, Italy
M. Biasini, G.M. Bilei, D. Ciangottini\@textsuperscript2, L. Fanò, P. Lariccia, G. Mantovani, M. Menichelli, A. Saha, A. Santocchia, A. Spiezia\@textsuperscript2 INFN Sezione di Pisa , Università di Pisa , Scuola Normale Superiore di Pisa ,  Pisa, Italy
K. Androsov\@textsuperscript26, P. Azzurri, G. Bagliesi, J. Bernardini, T. Boccali, G. Broccolo, R. Castaldi, M.A. Ciocci\@textsuperscript26, R. Dell’Orso, S. Donato\@textsuperscript2, G. Fedi, F. Fiori, L. Foà, A. Giassi, M.T. Grippo\@textsuperscript26, F. Ligabue, T. Lomtadze, L. Martini, A. Messineo, C.S. Moon\@textsuperscript27, F. Palla\@textsuperscript2, A. Rizzi, A. Savoy-Navarro\@textsuperscript28, A.T. Serban, P. Spagnolo, P. Squillacioti\@textsuperscript26, R. Tenchini, G. Tonelli, A. Venturi, P.G. Verdini, C. Vernieri INFN Sezione di Roma , Università di Roma ,  Roma, Italy
L. Barone, F. Cavallari, G. D’imperio, D. Del Re, M. Diemoz, C. Jorda, E. Longo, F. Margaroli, P. Meridiani, F. Micheli\@textsuperscript2, G. Organtini, R. Paramatti, S. Rahatlou, C. Rovelli, F. Santanastasio, L. Soffi, P. Traczyk\@textsuperscript2 INFN Sezione di Torino , Università di Torino , Torino, Italy, Università del Piemonte Orientale , Novara, Italy
N. Amapane, R. Arcidiacono, S. Argiro, M. Arneodo, R. Bellan, C. Biino, N. Cartiglia, S. Casasso\@textsuperscript2, M. Costa