EUROPEAN ORGANIZATION FOR NUCLEAR RESEARCH (CERN)

 CERN-EP-2016-275 LHCb-PAPER-2016-048 28 April 2017

Evidence for the two-body

charmless baryonic decay ${{B}^{+}}\\!\rightarrow{p}{\kern
2.07413pt\overline{\kern-2.07413pt\mathchar 28931\relax}}$

The LHCb collaboration†††Authors are listed at the end of this paper.

A search for the rare two-body charmless baryonic decay
${{B}^{+}}\\!\rightarrow{p}{\kern 1.00006pt\overline{\kern-1.00006pt\mathchar
28931\relax}}$ is performed with $pp$ collision data, corresponding to an
integrated luminosity of 3 $\mbox{\,fb}^{-1}$, collected by the LHCb
experiment at centre-of-mass energies of 7 and 8
$\mathrm{\,Te\kern-1.00006ptV}$. An excess of
${{B}^{+}}\\!\rightarrow{p}{\kern 1.00006pt\overline{\kern-1.00006pt\mathchar
28931\relax}}$ candidates with respect to background expectations is seen with
a statistical significance of 4.1 standard deviations, and constitutes the
first evidence for this decay. The branching fraction, measured using the
${{B}^{+}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle S}}}{{\pi}^{+}}$
decay for normalisation, is

$\displaystyle{\mathcal{B}}({{B}^{+}}\\!\rightarrow{p}{\kern
1.00006pt\overline{\kern-1.00006pt\mathchar 28931\relax}})$ $\displaystyle=$
$\displaystyle(2.4\,^{+1.0}_{-0.8}\pm 0.3)\times 10^{-7}\,,$

where the first uncertainty is statistical and the second systematic.

Published in JHEP 04 (2017) 162

© CERN on behalf of the LHCb collaboration, licence CC-BY-4.0.

##  1 Introduction

The experimental study of $B$ meson decays to baryonic final states has a long
history, including numerous searches and observations by the asymmetric
$e^{+}e^{-}$ collider experiments BaBar and Belle [1]. In recent years the
LHCb collaboration reported the first observation of a two-body charmless
baryonic ${B}^{+}$ decay and the first evidence for a similar ${B}^{0}$ decay,
namely ${{B}^{+}}\\!\rightarrow{p}{\kern
1.00006pt\overline{\kern-1.00006pt\mathchar 28931\relax}}(1520)$ [2] and
${{B}^{0}}\\!\rightarrow{p}{\overline{p}}$ [3]. No other two-body charmless
baryonic $B$ decay modes have been observed. Their experimental study requires
large data samples, presently only available at the LHC, as baryonic $B$
decays to two-body final states are suppressed, with branching fractions
typically one to two orders of magnitude lower than similar baryonic decays to
multibody final states.

Experimental input on the branching fractions of the
${{B}^{+}}\\!\rightarrow{p}{\kern 1.00006pt\overline{\kern-1.00006pt\mathchar
28931\relax}}$ decay and other suppressed baryonic decays provides valuable
information on the dynamics of the decays of $B$ mesons to baryonic final
states. The ${{B}^{+}}\\!\rightarrow{p}{\kern
1.00006pt\overline{\kern-1.00006pt\mathchar 28931\relax}}$ decay mode is
expected to be dominated by a $b\rightarrow s$ loop transition, but tree-level
($V_{{u}{b}}$ suppressed) and annihilation diagrams also contribute. Various
theoretical predictions for its branching fraction are available. Calculations
based on QCD sum rules [4] predict a branching fraction smaller than $3\times
10^{-6}$ whereas a pole model [5] and a recent study [6], taking into account
the LHCb experimental result on the $B^{0}\rightarrow p\bar{p}$ branching
fraction [3], both predict a branching fraction around $2\times 10^{-7}$. The
violation of partial conservation of the axial-vector current at the
$\mathrm{\,Ge\kern-1.00006ptV}$ scale has been proposed as an alternative
approach to the understanding of the data available on two-body baryonic
decays of $B$ and ${D}^{+}_{s}$ mesons [7]. It explains the LHCb results on
the
${{B}^{0}_{\kern-0.70004pt{\scriptscriptstyle(}\kern-0.35002pt{s}\kern-0.20999pt{\scriptscriptstyle)}}}\\!\rightarrow{p}{\overline{p}}$
decay modes [3] and predicts a branching fraction for the
${{B}^{+}}\\!\rightarrow{p}{\kern 1.00006pt\overline{\kern-1.00006pt\mathchar
28931\relax}}$ decay of the order of $10^{-8}$.

The decay ${{B}^{+}}\\!\rightarrow{p}{\kern
1.00006pt\overline{\kern-1.00006pt\mathchar 28931\relax}}$ has been searched
for by the CLEO [8] and Belle [9] collaborations. The most stringent
experimental upper limit on the ${{B}^{+}}\\!\rightarrow{p}{\kern
1.00006pt\overline{\kern-1.00006pt\mathchar 28931\relax}}$ branching fraction
is $3.2\times 10^{-7}$ at 90% confidence level, determined by the Belle
collaboration using 414$\mbox{\,fb}^{-1}$ of integrated luminosity from
$e^{+}e^{-}$ collisions.

This paper presents a search for the rare decay mode
${{B}^{+}}\\!\rightarrow{p}{\kern 1.00006pt\overline{\kern-1.00006pt\mathchar
28931\relax}}$ with the full $pp$ collision data sample collected in 2011 and
2012 by the LHCb experiment. The branching fraction is measured with respect
to that of the topologically identical
${{B}^{+}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle S}}}{{\pi}^{+}}$
decay to suppress common systematic uncertainties. The $\mathchar 28931\relax$
baryon is reconstructed in the $\mathchar
28931\relax\\!\rightarrow{p}{{\pi}^{-}}$ final state whereas the
${K}^{0}_{\mathrm{\scriptscriptstyle S}}$ meson is reconstructed in its
${{K}^{0}_{\mathrm{\scriptscriptstyle
S}}}\\!\rightarrow{{\pi}^{+}}{{\pi}^{-}}$ final state. The inclusion of
charge-conjugate processes is implied throughout this paper.

##  2 Detector and data sample

The data sample analysed corresponds to an integrated luminosity of
1$\mbox{\,fb}^{-1}$ at a centre-of-mass energy of
7$\mathrm{\,Te\kern-1.00006ptV}$ recorded in 2011 and 2$\mbox{\,fb}^{-1}$ at
8$\mathrm{\,Te\kern-1.00006ptV}$ recorded in 2012. The LHCb detector [10, 11]
is a single-arm forward spectrometer covering the pseudorapidity range
$2<\eta<5$, designed for the study of particles containing $b$ or $c$ quarks.
The detector includes a high-precision tracking system consisting of a
silicon-strip vertex detector surrounding the $pp$ interaction region, a
large-area silicon-strip detector located upstream of a dipole magnet with a
bending power of about $4{\mathrm{\,Tm}}$, and three stations of silicon-strip
detectors and straw drift tubes placed downstream of the magnet. The tracking
system provides a measurement of momentum, $p$, of charged particles with a
relative uncertainty that varies from 0.5% at low momentum to 1.0% at
200${\mathrm{\,Ge\kern-1.00006ptV\\!/}c}$. The minimum distance of a track to
a primary vertex (PV), the impact parameter (IP), is measured with a
resolution of $(15+29/\mbox{$p_{\mathrm{T}}$}){\,\upmu\mathrm{m}}$, where
$p_{\mathrm{T}}$ is the component of the momentum transverse to the beam, in
${\mathrm{\,Ge\kern-1.00006ptV\\!/}c}$. The different types of charged hadrons
are distinguished using information from two ring-imaging Cherenkov detectors.
Photons, electrons and hadrons are identified by a calorimeter system
consisting of scintillating-pad and preshower detectors, an electromagnetic
calorimeter and a hadronic calorimeter. Muons are identified by a system
composed of alternating layers of iron and multiwire proportional chambers.

The decays of the $V^{0}$ hadrons, namely $\mathchar
28931\relax\\!\rightarrow{p}{{\pi}^{-}}$ and
${{K}^{0}_{\mathrm{\scriptscriptstyle
S}}}\\!\rightarrow{{\pi}^{+}}{{\pi}^{-}}$, are reconstructed in two different
categories: the first consists of $V^{0}$ hadrons that decay early enough for
the daughter particles to be reconstructed in the vertex detector, and the
second contains those that decay later such that track segments cannot be
reconstructed in the vertex detector. These categories are referred to as
_long_ and _downstream_ , respectively. The candidates in the long category
have better mass, momentum and vertex resolution than those in the downstream
category.

Events are selected in a similar way for both the
${{B}^{+}}\\!\rightarrow{p}{\kern 1.00006pt\overline{\kern-1.00006pt\mathchar
28931\relax}}$ signal decay and the normalisation channel
${{B}^{+}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle S}}}{{\pi}^{+}}$.
The online event selection is performed by a trigger consisting of a hardware
stage, based on information from the calorimeter and muon systems, followed by
a software stage that performs a full event reconstruction, in which all
charged particles with
$\mbox{$p_{\mathrm{T}}$}>500\,(300){\mathrm{\,Me\kern-1.00006ptV\\!/}c}$ are
reconstructed for the 2011 (2012) data. At the hardware trigger stage, events
are required to have a muon with high $p_{\mathrm{T}}$ or a hadron, photon or
electron with high transverse energy in the calorimeters. The transverse
energy threshold for hadrons is set at 3.5$\mathrm{\,Ge\kern-1.00006ptV}$.
Signal candidates may come from events where the hardware trigger was
activated either by signal particles or by other particles in the event. The
proportion of events triggered by other particles in the event is found to be
very similar between the signal and the normalisation decay modes in both long
and downstream samples. The software trigger requires a two- or three-track
secondary vertex with a significant displacement from the primary $pp$
interaction vertices. At least one charged particle must have
$\mbox{$p_{\mathrm{T}}$}>1.7{\mathrm{\,Ge\kern-1.00006ptV\\!/}c}$ and be
inconsistent with originating from a PV. A multivariate algorithm [12] is used
for the identification of secondary vertices consistent with the decay of a
$b$ hadron to a final state of two or more particles.

The efficiency of the software trigger selection on both decay modes varied
during the data-taking period. During the 2011 data taking, downstream tracks
were not reconstructed in the software trigger. Such tracks were included in
the trigger during the 2012 data taking and a further significant improvement
in the algorithms was implemented mid-year. Consequently, the data are
subdivided into three data-taking periods (2011, 2012a and 2012b) in addition
to the two $V^{0}$ reconstruction categories (long and downstream). The 2012b
sample has the highest trigger efficiency, especially in the downstream
category, and is also the largest data set, corresponding to
1.4$\mbox{\,fb}^{-1}$ of integrated luminosity.

Simulated data samples are used to study the response of the detector and to
investigate possible sources of background to the signal and the normalisation
modes. The $pp$ collisions are generated using Pythia [13, *Sjostrand:2007gs]
with a specific LHCb configuration [15]. Decays of hadronic particles are
described by EvtGen [16], in which final-state radiation is generated using
Photos [17]. The interaction of the generated particles with the detector, and
its response, are implemented using the Geant4 toolkit [18,
*Agostinelli:2002hh] as described in Ref. [20].

##  3 Sample selection and composition

The selection consists of two stages, a preselection with high efficiency for
the signal decays, followed by a multivariate classifier. The selection
requirements of both signal and normalisation decays exploit the
characteristic topology and kinematic properties of two-body decays to final
states containing a $V^{0}$ hadron. The ${B}^{+}$ candidates are reconstructed
by combining, in a good-quality vertex, a $V^{0}$ candidate with a charged
particle hereafter referred to as the bachelor particle. Both the
${{B}^{+}}\\!\rightarrow{p}{\kern 1.00006pt\overline{\kern-1.00006pt\mathchar
28931\relax}}$ and the
${{B}^{+}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle S}}}{{\pi}^{+}}$
decay chains are refitted [21] using the known $\mathchar 28931\relax$ or
${K}^{0}_{\mathrm{\scriptscriptstyle S}}$ mass [22]. The resulting ${B}^{+}$
invariant-mass resolutions are improved and nearly identical for the long and
downstream $V^{0}$ candidates. The long and downstream samples are thus merged
after full selection, thereby simplifying the extraction of the signal yields.

A minimum $p_{\mathrm{T}}$ requirement is imposed for all final-state
particles. The $V^{0}$ decay products must have a large IP with respect to all
PVs; hence a minimum $\chi^{2}_{\text{IP}}$ with respect to the PVs is imposed
on each decay product, where $\chi^{2}_{\text{IP}}$ is defined as the
difference between the vertex-fit $\chi^{2}$ of a PV reconstructed with and
without the track in question. The $V^{0}$ decay products are also required to
form a good quality vertex. The $V^{0}$ candidate is associated to the PV that
gives the smallest $\chi^{2}_{\text{IP}}$. The selection favours long-lived
$V^{0}$ decays by requiring that the decay vertex and the associated PV are
well separated.

The $\mathchar 28931\relax$ decay products must satisfy
$|m({p}\pi)-m_{\mathchar
28931\relax}|<20(15){\mathrm{\,Me\kern-1.00006ptV\\!/}c^{2}}$ for downstream
(long) candidates, where $m_{\mathchar 28931\relax}$ is the known $\mathchar
28931\relax$ mass [22]. The corresponding criterion for the
${K}^{0}_{\mathrm{\scriptscriptstyle S}}$ decay products is
$|m(\pi\pi)-m_{{K}^{0}_{\mathrm{\scriptscriptstyle
S}}}|<30(15){\mathrm{\,Me\kern-1.00006ptV\\!/}c^{2}}$, where
$m_{{K}^{0}_{\mathrm{\scriptscriptstyle S}}}$ is the known
${K}^{0}_{\mathrm{\scriptscriptstyle S}}$ mass [22].

The ${B}^{+}$ candidate is required to have a small $\chi^{2}_{\text{IP}}$
with respect to the associated PV as its reconstructed momentum vector should
point to its production vertex. This pointing condition of the ${B}^{+}$
candidate is further reinforced by requiring that the angle between the
${B}^{+}$ candidate momentum vector and the line connecting the associated PV
and the ${B}^{+}$ decay vertex (${B}^{+}$ direction angle) is close to zero.

To avoid selection biases, ${p}{\kern
1.00006pt\overline{\kern-1.00006pt\mathchar 28931\relax}}$ candidates with
invariant mass within $64{\mathrm{\,Me\kern-1.00006ptV\\!/}c^{2}}$
(approximately four times the mass resolution) around the known ${B}^{+}$ mass
are not examined until all analysis choices are finalised. No such procedure
is applied to the spectrum of the well-known
${{B}^{+}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle S}}}{{\pi}^{+}}$
decay. The final selections of ${p}{\kern
1.00006pt\overline{\kern-1.00006pt\mathchar 28931\relax}}$ and
${{K}^{0}_{\mathrm{\scriptscriptstyle S}}}{{\pi}^{+}}$ candidates rely on
artificial neural networks [23], multilayer perceptrons (MLPs), as
multivariate classifiers to separate signal from background; the MLP
implementation is provided by the TMVA toolkit [24].

Separate MLPs are employed for the ${p}{\kern
1.00006pt\overline{\kern-1.00006pt\mathchar 28931\relax}}$ and the
${{K}^{0}_{\mathrm{\scriptscriptstyle S}}}{{\pi}^{+}}$ selection. The MLPs are
trained with simulated samples to represent the signals and with data from the
high-mass sideband in the range
5350–6420${\mathrm{\,Me\kern-1.00006ptV\\!/}c^{2}}$ for the background, to
avoid partially reconstructed backgrounds. For the well-known
${{K}^{0}_{\mathrm{\scriptscriptstyle S}}}{{\pi}^{+}}$ spectra both low- and
high-mass sidebands are used. The training and selection is performed
separately for each period of data taking (the 2012a and 2012b samples are
merged) and for downstream and long samples. Optimisation biases are avoided
by splitting each of these samples into three disjoint subsamples: each MLP is
trained on a different subsample in such a way that events used to train one
MLP are classified with another. The response of the MLPs is uncorrelated with
the mass of the ${p}{\kern 1.00006pt\overline{\kern-1.00006pt\mathchar
28931\relax}}$ and ${{K}^{0}_{\mathrm{\scriptscriptstyle S}}}{{\pi}^{+}}$
final states. The MLP training relies on an accurate description of the
distributions of the input variables in simulated events. The agreement
between data and simulation is verified with kinematic distributions from
${{B}^{+}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle S}}}{{\pi}^{+}}$
decays, where the combinatorial background in the invariant mass spectrum is
statistically suppressed using the sPlot technique [25]. No significant
deviations are found, giving confidence that the inputs to the MLPs represent
the data reliably. The variables used in the MLP classifiers are properties of
the ${B}^{+}$ candidate and of the bachelor particle and $V^{0}$ daughters.
The input variables are the following: the $\chi^{2}$ per degree of freedom of
the kinematic fit of the decay chain; the ${B}^{+}$ decay length,
$\chi^{2}_{\text{IP}}$ and direction angle; the difference between the
$z$-positions of the ${B}^{+}$ and the $V^{0}$ decay vertices divided by its
uncertainty squared; the bachelor particle $p_{\mathrm{T}}$; and the
$p_{\mathrm{T}}$ of the $V^{0}$ decay products. Extra variables are exploited
in the selection of the long samples: the $\chi^{2}_{\text{IP}}$ of the
bachelor particle and of both $V^{0}$ decay products.

In addition to the MLP selection, particle identification (PID) requirements
are necessary to reject sources of background coming from $B$ decays. A loose
PID requirement is imposed on the $V^{0}$ daughters, exploiting information
from the ring-imaging Cherenkov detectors, to remove background from
${K}^{0}_{\mathrm{\scriptscriptstyle S}}$ ($\mathchar 28931\relax$) decays in
the ${p}{\kern 1.00006pt\overline{\kern-1.00006pt\mathchar 28931\relax}}$
(${{K}^{0}_{\mathrm{\scriptscriptstyle S}}}{{\pi}^{+}}$) samples. The PID
selection on the bachelor particle is optimised together with the MLP
selection as follows. The figure of merit $\epsilon_{\rm
sig}/(a/2+\sqrt{B_{\rm exp}})$ suggested in Ref. [26] is used to determine the
optimal MLP and PID requirements for each ${{B}^{+}}\\!\rightarrow{p}{\kern
1.00006pt\overline{\kern-1.00006pt\mathchar 28931\relax}}$ subsample
separately, where $\epsilon_{\rm sig}$ represents the combined MLP and PID
selection efficiency. The term $a=3$ quantifies the target level of
significance in units of standard deviations. The expected number of
background candidates, $B_{\rm exp}$, within the (initially excluded) signal
region is estimated by extrapolating the result of a fit to the invariant mass
distribution of the data sidebands. A standard significance $S/\sqrt{S+B}$ is
used to optimise the selection of the
${{B}^{+}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle S}}}{{\pi}^{+}}$
candidates, where $B$ is the number of background candidates and $S$ the
number of signal candidates in the invariant mass range
$5000-5600{\mathrm{\,Me\kern-1.00006ptV\\!/}c^{2}}$. The presence of the
cross-feed background
${{B}^{+}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle S}}}{{K}^{+}}$ is
taken into account. The fraction of events with more than one selected
candidate is negligible; all candidates are kept.

Efficiencies are determined for each data-taking period and each $V^{0}$
reconstruction category, and subsequently combined accounting for the mixture
of these subsamples in data. The efficiency of the MLP selection is determined
from simulation. Large data control samples of
${{D}^{0}}\rightarrow{{K}^{-}}{{\pi}^{+}}$, $\mathchar
28931\relax\\!\rightarrow{p}{{\pi}^{-}}$ and ${{\mathchar
28931\relax}^{+}_{c}}\\!\rightarrow{p}{{K}^{-}}{{\pi}^{+}}$ decays are
employed [27] to determine the efficiency of the PID requirements. All other
selection efficiencies, i.e. trigger, reconstruction and preselection
efficiencies, are determined from simulation. The overall selection
efficiencies of this analysis are of order $10^{-4}$. The expected yield of
the control mode ${{B}^{+}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle
S}}}{{\pi}^{+}}$, calculated from the product of the integrated luminosity,
the ${b}{\overline{b}}$ cross-section, the ${b}$ hadronisation probability,
the ${{B}^{+}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle
S}}}{{\pi}^{+}}$ visible branching fraction and the total selection
efficiency, agrees with the yield obtained from the fit to the data at the
level of 1.4 standard deviations.

Possible sources of non-combinatorial background to the ${p}{\kern
1.00006pt\overline{\kern-1.00006pt\mathchar 28931\relax}}$ and
${{K}^{0}_{\mathrm{\scriptscriptstyle S}}}{{\pi}^{+}}$ spectra are
investigated using extensive simulation samples. These sources include
partially reconstructed backgrounds in which one or more particles from the
decay of a $b$ hadron are not associated with the signal candidate, and
$b$-hadron decays where one or more decay products are misidentified, such as
decays with ${K}^{0}_{\mathrm{\scriptscriptstyle S}}$ mesons misidentified as
$\mathchar 28931\relax$ baryons in the ${p}{\kern
1.00006pt\overline{\kern-1.00006pt\mathchar 28931\relax}}$ spectrum. The
peaking background from ${{B}^{+}}\\!\rightarrow{p}{\overline{p}}{{\pi}^{+}}$
decays in the ${p}{\kern 1.00006pt\overline{\kern-1.00006pt\mathchar
28931\relax}}$ spectrum is found to be insignificant after the MLP selection.
The currently unobserved ${{B}^{+}}\\!\rightarrow{p}\kern
1.99997pt\overline{\kern-1.99997pt\Sigma}{}^{0}$ decay is treated as a source
of systematic uncertainty. The ensemble of specific backgrounds does not peak
in the signal region but rather contributes a smooth ${p}{\kern
1.00006pt\overline{\kern-1.00006pt\mathchar 28931\relax}}$ mass spectrum,
which is indistinguishable from the dominant combinatorial background.

##  4 Signal yield determination

The yields of the signal and background candidates in both the signal and
normalisation samples are determined, after the full selection, using unbinned
extended maximum likelihood fits to the invariant mass spectra. The signal
lineshapes are found to be compatible between the data-taking periods and
between the long and downstream categories, so all subsamples are merged
together into a single spectrum.

The probability density functions (PDFs) of $B$-meson signals have asymmetric
tails that result from a combination of detector-related effects and effects
of final-state radiation. The signal mass distributions are verified in
simulation to be modelled accurately by the sum of two Crystal Ball (CB)
functions [28] describing the high- and low-mass asymmetric tails. The peak
values and the core widths of the two CB components are set to be the same.

Figure 1:  Invariant mass distribution of ${p}{\kern
1.00006pt\overline{\kern-1.00006pt\mathchar 28931\relax}}$ candidates after
full selection. The result of the fit to the data (blue, solid) is shown
together with each fit model component, namely the
${{B}^{+}}\\!\rightarrow{p}{\kern 1.00006pt\overline{\kern-1.00006pt\mathchar
28931\relax}}$ signal and the combinatorial background.

The ${p}{\kern 1.00006pt\overline{\kern-1.00006pt\mathchar 28931\relax}}$
spectrum comprises the ${{B}^{+}}\\!\rightarrow{p}{\kern
1.00006pt\overline{\kern-1.00006pt\mathchar 28931\relax}}$ signal and
combinatorial background. Contamination from partially reconstructed
backgrounds, with or without misidentified particles, is treated as a source
of systematic uncertainty. The peak position and tail parameters of the
${{B}^{+}}\\!\rightarrow{p}{\kern 1.00006pt\overline{\kern-1.00006pt\mathchar
28931\relax}}$ CB components are fixed to the values obtained from simulation.
The core width parameter, also fixed, is obtained by multiplying the value
from simulation by a scaling factor to account for differences in the
resolution between data and simulation. This factor, determined from the
${{B}^{+}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle S}}}{{\pi}^{+}}$
data and simulation samples, is compatible with unity ($1.01\pm 0.06$) and
gives a width of approximately 16${\mathrm{\,Me\kern-1.00006ptV\\!/}c^{2}}$.
The invariant mass distribution of the combinatorial background is described
by an exponential function, with the slope parameter determined from the fit.

The fit to the ${p}{\kern 1.00006pt\overline{\kern-1.00006pt\mathchar
28931\relax}}$ invariant mass distribution, presented in Fig. 1, determines
three parameters: two yields and the slope of the combinatorial background
model. An excess of ${{B}^{+}}\\!\rightarrow{p}{\kern
1.00006pt\overline{\kern-1.00006pt\mathchar 28931\relax}}$ candidates with
respect to background expectations is found, corresponding to a signal yield
of $N({{B}^{+}}\\!\rightarrow{p}{\kern
1.00006pt\overline{\kern-1.00006pt\mathchar
28931\relax}})=13.0^{+5.1}_{-4.3}$, where the uncertainties, obtained from a
profile likelihood scan, are statistical only.

The statistical significance of the ${{B}^{+}}\\!\rightarrow{p}{\kern
1.00006pt\overline{\kern-1.00006pt\mathchar 28931\relax}}$ signal is
determined with a large set of samples simulated assuming the presence of
background only. For each simulated sample, a number of events distributed
according to the exponential model of the background is drawn from a Poisson
distribution with mean equal to the number of observed background events. For
each sample, the log-likelihood ratio $2\ln(L_{S+B}/L_{B})$ is computed, where
$L_{S+B}$ and $L_{B}$ are the likelihoods from the full fit and from the fit
without the signal component, respectively. The fraction of samples that yield
log-likelihood ratios larger than the ratio observed in data is $3.4\times
10^{-5}$, which corresponds to a statistical significance of 4.1 standard
deviations. Inclusion of the 6.7% systematic uncertainty affecting the signal
yield gives only a marginal change in the signal significance.

Figure 2:  Invariant mass distribution of
${{K}^{0}_{\mathrm{\scriptscriptstyle S}}}{{\pi}^{+}}$ candidates after full
selection. The result of the fit to the data (blue, solid) is shown together
with each fit model component, namely the
${{B}^{+}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle S}}}{{\pi}^{+}}$
signal, the
${{B}^{0}_{\kern-0.70004pt{\scriptscriptstyle(}\kern-0.35002pt{s}\kern-0.20999pt{\scriptscriptstyle)}}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle
S}}}h^{+}h^{{}^{\prime}-}$ partially reconstructed background, and the
combinatorial background. The vanishingly small
${{B}^{+}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle S}}}{{K}^{+}}$
misidentified cross-feed is not displayed.

The ${{K}^{0}_{\mathrm{\scriptscriptstyle S}}}{{\pi}^{+}}$ mass spectrum of
the normalisation decay is described as the sum of components accounting for
the ${{B}^{+}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle
S}}}{{\pi}^{+}}$ signal, the
${{B}^{+}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle S}}}{{K}^{+}}$
misidentified background, backgrounds from partially reconstructed
${{B}^{0}_{\kern-0.70004pt{\scriptscriptstyle(}\kern-0.35002pt{s}\kern-0.20999pt{\scriptscriptstyle)}}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle
S}}}h^{+}h^{{}^{\prime}-}$ decays ($h^{(\prime)}={\pi},{K}$), and
combinatorial background. Any contamination from other decays is treated as a
source of systematic uncertainty.

The ${{B}^{+}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle S}}}h^{+}$ CB
tail parameters and the relative normalisation of the two CB functions are
fixed to the values obtained from simulation. The mean and the width
(approximately 17${\mathrm{\,Me\kern-1.00006ptV\\!/}c^{2}}$) of the
${{B}^{+}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle S}}}{{\pi}^{+}}$
peak are allowed to vary in the fit to the data, whilst they are fixed for the
very small ${{B}^{+}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle
S}}}{{K}^{+}}$ peak contribution. The mean of the
${{B}^{+}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle S}}}{{K}^{+}}$
peak, around 5240${\mathrm{\,Me\kern-1.00006ptV\\!/}c^{2}}$, is fixed using
the mass difference between the
${{B}^{+}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle S}}}{{K}^{+}}$
and ${{B}^{+}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle
S}}}{{\pi}^{+}}$ peaks obtained from simulation. The
${{B}^{+}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle S}}}{{K}^{+}}$
yield is Gaussian constrained using the
${{B}^{+}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle S}}}{{\pi}^{+}}$
yield, taking into account the differences in branching fraction and selection
efficiency.

The partially reconstructed backgrounds that populate the lower-mass sideband
are assumed to arise from the
${{B}^{0}_{\kern-0.70004pt{\scriptscriptstyle(}\kern-0.35002pt{s}\kern-0.20999pt{\scriptscriptstyle)}}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle
S}}}h^{+}h^{{}^{\prime}-}$ decay modes with the largest branching fractions,
namely ${{B}^{0}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle
S}}}{{\pi}^{+}}{{\pi}^{-}}$,
${{B}^{0}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle
S}}}{{K}^{+}}{{K}^{-}}$,
${{B}^{0}_{s}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle
S}}}{{\pi}^{+}}{{\pi}^{-}}$ and
${{B}^{0}_{s}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle
S}}}{{K}^{\pm}}{{\pi}^{\mp}}$ [29]. Only
${{B}^{0}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle
S}}}{{\pi}^{+}}{{\pi}^{-}}$,
${{B}^{0}_{s}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle
S}}}{{\pi}^{+}}{{\pi}^{-}}$ and
${{B}^{0}_{s}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle
S}}}{{K}^{\pm}}{{\pi}^{\mp}}$ are considered given that
${{B}^{0}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle
S}}}{{K}^{+}}{{K}^{-}}$ is further suppressed because of a low kaon-to-pion
misidentification probability. The overall shape of the
${{B}^{0}_{\kern-0.70004pt{\scriptscriptstyle(}\kern-0.35002pt{s}\kern-0.20999pt{\scriptscriptstyle)}}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle
S}}}h^{+}h^{{}^{\prime}-}$ decay modes in the
${{K}^{0}_{\mathrm{\scriptscriptstyle S}}}{{\pi}^{+}}$ mass spectrum is
obtained from simulation accounting for the relative yields related to
different $B$-meson fragmentation probabilities, selection efficiencies and
branching fractions. The invariant mass distribution of the combinatorial
background is described by an exponential function, with the slope parameter
determined from the mass fit.

The resulting spectrum shows a prominent
${{B}^{+}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle S}}}{{\pi}^{+}}$
peak above little combinatorial and partially reconstructed background. The
fit to the ${{K}^{0}_{\mathrm{\scriptscriptstyle S}}}{{\pi}^{+}}$ spectrum,
presented in Fig. 2, determines seven parameters: three shape parameters and
four yields. The signal yield obtained is
$N({{B}^{+}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle
S}}}{{\pi}^{+}})=930\pm 34$, where the uncertainty is statistical only.

##  5 Systematic uncertainties

The systematic uncertainties are reduced by performing the branching fraction
measurement relative to a decay mode topologically identical to the decay of
interest. Uncertainties arise from imperfect knowledge of the selection
efficiencies, systematic uncertainties on the fitted yields, and uncertainties
on the branching fractions of decays involved in the calculation of the
${{B}^{+}}\\!\rightarrow{p}{\kern 1.00006pt\overline{\kern-1.00006pt\mathchar
28931\relax}}$ branching fraction. The systematic uncertainties assigned to
the measurement of the ${{B}^{+}}\\!\rightarrow{p}{\kern
1.00006pt\overline{\kern-1.00006pt\mathchar 28931\relax}}$ branching fraction
are summarised in Table 1.

Table 1: Summary of systematic uncertainties relative to the measured ${{B}^{+}}\\!\rightarrow{p}{\kern 0.90005pt\overline{\kern-0.90005pt\mathchar 28931\relax}}$ branching fraction. The contributions are split into those that come from the signal decay and those that come from the normalisation decay. The total corresponds to the sum of all contributions added in quadrature. Source | Value [%]
---|---
| ${{B}^{+}}\\!\rightarrow{p}{\kern 1.00006pt\overline{\kern-1.00006pt\mathchar 28931\relax}}$ | ${{B}^{+}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle S}}}{{\pi}^{+}}$
${\mathcal{B}}({{B}^{+}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle S}}}{{\pi}^{+}})$ | - | 3.2
Trigger efficiencies ratio | 3.5 | -
Selection efficiencies ratio | 2.2 | -
PID uncertainties | 1.2 | 3.5
Tracking efficiencies ratio | 6.0 | -
Yields from mass fits | 6.7 | 3.0
Simulation statistics | 1.7 | 3.3
Total | 10.1 | 6.5

The uncertainty on the branching fraction of the normalisation channel,
${\mathcal{B}}({{B}^{+}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle
S}}}{{\pi}^{+}})=(11.895\pm 0.375)\times 10^{-6}$ [30] (assuming that half of
the ${K}^{0}$ mesons decay as a ${K}^{0}_{\mathrm{\scriptscriptstyle S}}$), is
taken as a systematic uncertainty. The uncertainties on the branching
fractions ${\mathcal{B}}(\mathchar
28931\relax\\!\rightarrow{p}{{\pi}^{-}})=(63.9\pm 0.5)\%$ and
${\mathcal{B}}({{K}^{0}_{\mathrm{\scriptscriptstyle
S}}}\\!\rightarrow{{\pi}^{+}}{{\pi}^{-}})=(69.20\pm 0.05)\%$ are accounted
for, but omitted from the table as they are negligible compared to all other
sources of systematic uncertainty.

The determination of the selection efficiencies entails several sources of
systematic uncertainty. A systematic uncertainty is assigned to take into
account possible differences in the trigger efficiencies between data and
simulation, following the procedure and studies described in Ref. [31]. The
${{B}^{+}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle S}}}{{\pi}^{+}}$
mode is used as a proxy for the assessment of the systematic uncertainties
related to the MLP selection. Distributions for the
${{B}^{+}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle S}}}{{\pi}^{+}}$
MLP input variables are obtained from data using the sPlot technique. The
distributions from simulation showing the largest discrepancies are weighted
to match those of the data. The selection efficiencies are recalculated with
the same MLPs, but using the weighted distributions, to derive the variations
in efficiency, and hence the systematic uncertainty on the selection. The
uncertainty associated with the imperfect knowledge of PID selection
efficiencies is assessed varying the binning of the PID control samples in
track momentum and pseudorapidity, and also accounting for a dependence of the
efficiency on the event track multiplicity after weighting the distribution of
the latter to match that of the data. The two uncertainties are combined in
quadrature.

The signal decay has two baryons in the final state whilst only mesons are
present in the final state of the normalisation channel. Tracking efficiency
uncertainties do not cancel fully in this instance. The degree to which the
simulation describes the hadronic interactions with the material is less
accurate for baryons than it is for mesons. A systematic uncertainty of 4% per
proton is estimated whereas the corresponding uncertainty is 1.5% for pions
and kaons [32]. A non-negligible systematic uncertainty on the tracking
efficiencies as calculated from simulation, including correlations, results
from these sources of uncertainty.

Systematic uncertainties on the fit yields arise from potential mismodelling
of the fit components and from the uncertainties on the values of the
parameters fixed in the fits. They are investigated using data and by studying
a large number of simulated data samples, with parameters varying within their
estimated uncertainties. Changing the combinatorial background model to a
linear shape decreases the signal yield by 4.6%, with no significant effect on
the signal significance and the final result. Possible contamination from the
unobserved decay ${{B}^{+}}\\!\rightarrow{p}\kern
1.99997pt\overline{\kern-1.99997pt\Sigma}{}^{0}$ is studied by adding such a
component. The fitted ${{B}^{+}}\\!\rightarrow{p}\kern
1.99997pt\overline{\kern-1.99997pt\Sigma}{}^{0}$ yield is found to be
compatible with zero, and the shift in the ${{B}^{+}}\\!\rightarrow{p}{\kern
1.00006pt\overline{\kern-1.00006pt\mathchar 28931\relax}}$ yield with respect
to the nominal yield is taken as a systematic uncertainty.

The finite size of the simulation samples used in the analysis further
contributes as a source of systematic uncertainty. The total systematic
uncertainty on the ${{B}^{+}}\\!\rightarrow{p}{\kern
1.00006pt\overline{\kern-1.00006pt\mathchar 28931\relax}}$ branching fraction
is given by the sum of all contributions added in quadrature, amounting to
12.0%.

##  6 Results and conclusion

The ${{B}^{+}}\\!\rightarrow{p}{\kern
1.00006pt\overline{\kern-1.00006pt\mathchar 28931\relax}}$ branching fraction
is determined relative to that of the
${{B}^{+}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle S}}}{{\pi}^{+}}$
normalisation channel according to

${\mathcal{B}}({{B}^{+}}\\!\rightarrow{p}{\kern
1.00006pt\overline{\kern-1.00006pt\mathchar
28931\relax}})=\frac{N({{B}^{+}}\\!\rightarrow{p}{\kern
1.00006pt\overline{\kern-1.00006pt\mathchar
28931\relax}})}{N({{B}^{+}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle
S}}}{{\pi}^{+}})}\,\frac{\epsilon_{{{B}^{+}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle
S}}}{{\pi}^{+}}}}{\epsilon_{{{B}^{+}}\\!\rightarrow{p}{\kern
0.70004pt\overline{\kern-0.70004pt\mathchar
28931\relax}}}}\,\frac{{\mathcal{B}}({{K}^{0}_{\mathrm{\scriptscriptstyle
S}}}\\!\rightarrow{{\pi}^{+}}{{\pi}^{-}})}{{\mathcal{B}}(\mathchar
28931\relax\\!\rightarrow{p}{{\pi}^{-}})}\,{\mathcal{B}}({{B}^{+}}\\!\rightarrow{{K}^{0}_{\mathrm{\scriptscriptstyle
S}}}{{\pi}^{+}})\,,$

where $N$ represent the yields determined from the mass fits and $\epsilon$
are the selection efficiencies. It is measured to be

${\mathcal{B}}({{B}^{+}}\\!\rightarrow{p}{\kern
1.00006pt\overline{\kern-1.00006pt\mathchar
28931\relax}})=(2.4\,^{+1.0}_{-0.8}\pm 0.3)\times 10^{-7}\,,$

where the first uncertainty is statistical and the second systematic.

In summary, a search is reported for the rare two-body charmless baryonic
decay ${{B}^{+}}\\!\rightarrow{p}{\kern
1.00006pt\overline{\kern-1.00006pt\mathchar 28931\relax}}$ using a $pp$
collision data sample collected by the LHCb experiment at centre-of-mass
energies of 7 and 8 $\mathrm{\,Te\kern-1.00006ptV}$, corresponding to an
integrated luminosity of 3$\mbox{\,fb}^{-1}$. An excess of
${{B}^{+}}\\!\rightarrow{p}{\kern 1.00006pt\overline{\kern-1.00006pt\mathchar
28931\relax}}$ candidates with respect to background expectations is found
with a statistical significance of 4.1 standard deviations. This is the first
evidence for this decay process.

The measured branching fraction is compatible with the theoretical predictions
in Refs. [5, 6] but is in tension with calculations based on QCD sum rules [4]
and calculations based on factorisation with the hypothesis of the violation
of partial conservation of the axial-vector current at the
$\mathrm{\,Ge\kern-1.00006ptV}$ scale [7]. It helps shed light on an area of
hadronic physics in which experimental input is needed, namely the study of
the mechanisms responsible for decays of $B$ mesons to baryonic final states.

## Acknowledgements

We express our gratitude to our colleagues in the CERN accelerator departments
for the excellent performance of the LHC. We thank the technical and
administrative staff at the LHCb institutes. We acknowledge support from CERN
and from the national agencies: CAPES, CNPq, FAPERJ and FINEP (Brazil); NSFC
(China); CNRS/IN2P3 (France); BMBF, DFG and MPG (Germany); INFN (Italy); FOM
and NWO (The Netherlands); MNiSW and NCN (Poland); MEN/IFA (Romania); MinES
and FASO (Russia); MinECo (Spain); SNSF and SER (Switzerland); NASU (Ukraine);
STFC (United Kingdom); NSF (USA). We acknowledge the computing resources that
are provided by CERN, IN2P3 (France), KIT and DESY (Germany), INFN (Italy),
SURF (The Netherlands), PIC (Spain), GridPP (United Kingdom), RRCKI and Yandex
LLC (Russia), CSCS (Switzerland), IFIN-HH (Romania), CBPF (Brazil), PL-GRID
(Poland) and OSC (USA). We are indebted to the communities behind the multiple
open source software packages on which we depend. Individual groups or members
have received support from AvH Foundation (Germany), EPLANET, Marie
Skłodowska-Curie Actions and ERC (European Union), Conseil Général de Haute-
Savoie, Labex ENIGMASS and OCEVU, Région Auvergne (France), RFBR and Yandex
LLC (Russia), GVA, XuntaGal and GENCAT (Spain), Herchel Smith Fund, The Royal
Society, Royal Commission for the Exhibition of 1851 and the Leverhulme Trust
(United Kingdom).

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LHCb collaboration

R. Aaij40, B. Adeva39, M. Adinolfi48, Z. Ajaltouni5, S. Akar6, J. Albrecht10,
F. Alessio40, M. Alexander53, S. Ali43, G. Alkhazov31, P. Alvarez Cartelle55,
A.A. Alves Jr59, S. Amato2, S. Amerio23, Y. Amhis7, L. An41, L. Anderlini18,
G. Andreassi41, M. Andreotti17,g, J.E. Andrews60, R.B. Appleby56, F.
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I. Bediaga1, L.J. Bel43, V. Bellee41, N. Belloli21,i, K. Belous37, I.
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Bird56, A. Birnkraut10, A. Bitadze56, A. Bizzeti18,u, T. Blake50, F. Blanc41,
J. Blouw11,†, S. Blusk61, V. Bocci26, T. Boettcher58, A. Bondar36,w, N.
Bondar31,40, W. Bonivento16, I. Bordyuzhin32, A. Borgheresi21,i, S. Borghi56,
M. Borisyak35, M. Borsato39, F. Bossu7, M. Boubdir9, T.J.V. Bowcock54, E.
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Muheim52, M. Mulder43, M. Mussini15, D. Müller56, J. Müller10, K. Müller42, V.
Müller10, P. Naik48, T. Nakada41, R. Nandakumar51, A. Nandi57, I. Nasteva2, M.
Needham52, N. Neri22, S. Neubert12, N. Neufeld40, M. Neuner12, T.D. Nguyen41,
C. Nguyen-Mau41,n, S. Nieswand9, R. Niet10, N. Nikitin33, T. Nikodem12, A.
Novoselov37, D.P. O’Hanlon50, A. Oblakowska-Mucha28, V. Obraztsov37, S.
Ogilvy19, R. Oldeman16,f, C.J.G. Onderwater70, J.M. Otalora Goicochea2, A.
Otto40, P. Owen42, A. Oyanguren69, P.R. Pais41, A. Palano14,d, F. Palombo22,q,
M. Palutan19, J. Panman40, A. Papanestis51, M. Pappagallo14,d, L.L.
Pappalardo17,g, W. Parker60, C. Parkes56, G. Passaleva18, A. Pastore14,d, G.D.
Patel54, M. Patel55, C. Patrignani15,e, A. Pearce56,51, A. Pellegrino43, G.
Penso26, M. Pepe Altarelli40, S. Perazzini40, P. Perret5, L. Pescatore47, K.
Petridis48, A. Petrolini20,h, A. Petrov68, M. Petruzzo22,q, E. Picatoste
Olloqui38, B. Pietrzyk4, M. Pikies27, D. Pinci26, A. Pistone20, A. Piucci12,
V. Placinta30, S. Playfer52, M. Plo Casasus39, T. Poikela40, F. Polci8, A.
Poluektov50,36, I. Polyakov61, E. Polycarpo2, G.J. Pomery48, A. Popov37, D.
Popov11,40, B. Popovici30, S. Poslavskii37, C. Potterat2, E. Price48, J.D.
Price54, J. Prisciandaro39,40, A. Pritchard54, C. Prouve48, V. Pugatch46, A.
Puig Navarro42, G. Punzi24,p, W. Qian57, R. Quagliani7,48, B. Rachwal27, J.H.
Rademacker48, M. Rama24, M. Ramos Pernas39, M.S. Rangel2, I. Raniuk45, F.
Ratnikov35, G. Raven44, F. Redi55, S. Reichert10, A.C. dos Reis1, C. Remon
Alepuz69, V. Renaudin7, S. Ricciardi51, S. Richards48, M. Rihl40, K.
Rinnert54, V. Rives Molina38, P. Robbe7,40, A.B. Rodrigues1, E. Rodrigues59,
J.A. Rodriguez Lopez66, P. Rodriguez Perez56,†, A. Rogozhnikov35, S. Roiser40,
A. Rollings57, V. Romanovskiy37, A. Romero Vidal39, J.W. Ronayne13, M.
Rotondo19, M.S. Rudolph61, T. Ruf40, P. Ruiz Valls69, J.J. Saborido Silva39,
E. Sadykhov32, N. Sagidova31, B. Saitta16,f, V. Salustino Guimaraes1, C.
Sanchez Mayordomo69, B. Sanmartin Sedes39, R. Santacesaria26, C. Santamarina
Rios39, M. Santimaria19, E. Santovetti25,j, A. Sarti19,k, C. Satriano26,s, A.
Satta25, D.M. Saunders48, D. Savrina32,33, S. Schael9, M. Schellenberg10, M.
Schiller53, H. Schindler40, M. Schlupp10, M. Schmelling11, T. Schmelzer10, B.
Schmidt40, O. Schneider41, A. Schopper40, K. Schubert10, M. Schubiger41, M.-H.
Schune7, R. Schwemmer40, B. Sciascia19, A. Sciubba26,k, A. Semennikov32, A.
Sergi47, N. Serra42, J. Serrano6, L. Sestini23, P. Seyfert21, M. Shapkin37, I.
Shapoval45, Y. Shcheglov31, T. Shears54, L. Shekhtman36,w, V. Shevchenko68,
B.G. Siddi17,40, R. Silva Coutinho42, L. Silva de Oliveira2, G. Simi23,o, S.
Simone14,d, M. Sirendi49, N. Skidmore48, T. Skwarnicki61, E. Smith55, I.T.
Smith52, J. Smith49, M. Smith55, H. Snoek43, l. Soares Lavra1, M.D.
Sokoloff59, F.J.P. Soler53, B. Souza De Paula2, B. Spaan10, P. Spradlin53, S.
Sridharan40, F. Stagni40, M. Stahl12, S. Stahl40, P. Stefko41, S. Stefkova55,
O. Steinkamp42, S. Stemmle12, O. Stenyakin37, S. Stevenson57, S. Stoica30, S.
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Toriello61, E. Tournefier4, S. Tourneur41, K. Trabelsi41, M. Traill53, M.T.
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V. Vagnoni15,40, A. Valassi40, S. Valat40, G. Valenti15, A. Vallier7, R.
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Vilasis-Cardona38,m, M. Vitti49, V. Volkov33, A. Vollhardt42, B. Voneki40, A.
Vorobyev31, V. Vorobyev36,w, C. Voß67, J.A. de Vries43, C. Vázquez Sierra39,
R. Waldi67, C. Wallace50, R. Wallace13, J. Walsh24, J. Wang61, D.R. Ward49,
H.M. Wark54, N.K. Watson47, D. Websdale55, A. Weiden42, M. Whitehead40, J.
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Williams58, T. Williams47, F.F. Wilson51, J. Wimberley60, J. Wishahi10, W.
Wislicki29, M. Witek27, G. Wormser7, S.A. Wotton49, K. Wraight53, K. Wyllie40,
Y. Xie65, Z. Xing61, Z. Xu41, Z. Yang3, Y. Yao61, H. Yin65, J. Yu65, X.
Yuan36,w, O. Yushchenko37, K.A. Zarebski47, M. Zavertyaev11,c, L. Zhang3, Y.
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Zucchelli15.

1Centro Brasileiro de Pesquisas Físicas (CBPF), Rio de Janeiro, Brazil
2Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
3Center for High Energy Physics, Tsinghua University, Beijing, China
4LAPP, Université Savoie Mont-Blanc, CNRS/IN2P3, Annecy-Le-Vieux, France
5Clermont Université, Université Blaise Pascal, CNRS/IN2P3, LPC, Clermont-
Ferrand, France
6CPPM, Aix-Marseille Université, CNRS/IN2P3, Marseille, France
7LAL, Université Paris-Sud, CNRS/IN2P3, Orsay, France
8LPNHE, Université Pierre et Marie Curie, Université Paris Diderot,
CNRS/IN2P3, Paris, France
9I. Physikalisches Institut, RWTH Aachen University, Aachen, Germany
10Fakultät Physik, Technische Universität Dortmund, Dortmund, Germany
11Max-Planck-Institut für Kernphysik (MPIK), Heidelberg, Germany
12Physikalisches Institut, Ruprecht-Karls-Universität Heidelberg, Heidelberg,
Germany
13School of Physics, University College Dublin, Dublin, Ireland
14Sezione INFN di Bari, Bari, Italy
15Sezione INFN di Bologna, Bologna, Italy
16Sezione INFN di Cagliari, Cagliari, Italy
17Sezione INFN di Ferrara, Ferrara, Italy
18Sezione INFN di Firenze, Firenze, Italy
19Laboratori Nazionali dell’INFN di Frascati, Frascati, Italy
20Sezione INFN di Genova, Genova, Italy
21Sezione INFN di Milano Bicocca, Milano, Italy
22Sezione INFN di Milano, Milano, Italy
23Sezione INFN di Padova, Padova, Italy
24Sezione INFN di Pisa, Pisa, Italy
25Sezione INFN di Roma Tor Vergata, Roma, Italy
26Sezione INFN di Roma La Sapienza, Roma, Italy
27Henryk Niewodniczanski Institute of Nuclear Physics Polish Academy of
Sciences, Kraków, Poland
28AGH - University of Science and Technology, Faculty of Physics and Applied
Computer Science, Kraków, Poland
29National Center for Nuclear Research (NCBJ), Warsaw, Poland
30Horia Hulubei National Institute of Physics and Nuclear Engineering,
Bucharest-Magurele, Romania
31Petersburg Nuclear Physics Institute (PNPI), Gatchina, Russia
32Institute of Theoretical and Experimental Physics (ITEP), Moscow, Russia
33Institute of Nuclear Physics, Moscow State University (SINP MSU), Moscow,
Russia
34Institute for Nuclear Research of the Russian Academy of Sciences (INR RAN),
Moscow, Russia
35Yandex School of Data Analysis, Moscow, Russia
36Budker Institute of Nuclear Physics (SB RAS), Novosibirsk, Russia
37Institute for High Energy Physics (IHEP), Protvino, Russia
38ICCUB, Universitat de Barcelona, Barcelona, Spain
39Universidad de Santiago de Compostela, Santiago de Compostela, Spain
40European Organization for Nuclear Research (CERN), Geneva, Switzerland
41Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
42Physik-Institut, Universität Zürich, Zürich, Switzerland
43Nikhef National Institute for Subatomic Physics, Amsterdam, The Netherlands
44Nikhef National Institute for Subatomic Physics and VU University Amsterdam,
Amsterdam, The Netherlands
45NSC Kharkiv Institute of Physics and Technology (NSC KIPT), Kharkiv, Ukraine
46Institute for Nuclear Research of the National Academy of Sciences (KINR),
Kyiv, Ukraine
47University of Birmingham, Birmingham, United Kingdom
48H.H. Wills Physics Laboratory, University of Bristol, Bristol, United
Kingdom
49Cavendish Laboratory, University of Cambridge, Cambridge, United Kingdom
50Department of Physics, University of Warwick, Coventry, United Kingdom
51STFC Rutherford Appleton Laboratory, Didcot, United Kingdom
52School of Physics and Astronomy, University of Edinburgh, Edinburgh, United
Kingdom
53School of Physics and Astronomy, University of Glasgow, Glasgow, United
Kingdom
54Oliver Lodge Laboratory, University of Liverpool, Liverpool, United Kingdom
55Imperial College London, London, United Kingdom
56School of Physics and Astronomy, University of Manchester, Manchester,
United Kingdom
57Department of Physics, University of Oxford, Oxford, United Kingdom
58Massachusetts Institute of Technology, Cambridge, MA, United States
59University of Cincinnati, Cincinnati, OH, United States
60University of Maryland, College Park, MD, United States
61Syracuse University, Syracuse, NY, United States
62Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio), Rio de
Janeiro, Brazil, associated to 2
63University of Chinese Academy of Sciences, Beijing, China, associated to 3
64School of Physics and Technology, Wuhan University, Wuhan, China, associated
to 3
65Institute of Particle Physics, Central China Normal University, Wuhan,
Hubei, China, associated to 3
66Departamento de Fisica , Universidad Nacional de Colombia, Bogota, Colombia,
associated to 8
67Institut für Physik, Universität Rostock, Rostock, Germany, associated to 12
68National Research Centre Kurchatov Institute, Moscow, Russia, associated to
32
69Instituto de Fisica Corpuscular (IFIC), Universitat de Valencia-CSIC,
Valencia, Spain, associated to 38
70Van Swinderen Institute, University of Groningen, Groningen, The
Netherlands, associated to 43

aUniversidade Federal do Triângulo Mineiro (UFTM), Uberaba-MG, Brazil
bLaboratoire Leprince-Ringuet, Palaiseau, France
cP.N. Lebedev Physical Institute, Russian Academy of Science (LPI RAS),
Moscow, Russia
dUniversità di Bari, Bari, Italy
eUniversità di Bologna, Bologna, Italy
fUniversità di Cagliari, Cagliari, Italy
gUniversità di Ferrara, Ferrara, Italy
hUniversità di Genova, Genova, Italy
iUniversità di Milano Bicocca, Milano, Italy
jUniversità di Roma Tor Vergata, Roma, Italy
kUniversità di Roma La Sapienza, Roma, Italy
lAGH - University of Science and Technology, Faculty of Computer Science,
Electronics and Telecommunications, Kraków, Poland
mLIFAELS, La Salle, Universitat Ramon Llull, Barcelona, Spain
nHanoi University of Science, Hanoi, Viet Nam
oUniversità di Padova, Padova, Italy
pUniversità di Pisa, Pisa, Italy
qUniversità degli Studi di Milano, Milano, Italy
rUniversità di Urbino, Urbino, Italy
sUniversità della Basilicata, Potenza, Italy
tScuola Normale Superiore, Pisa, Italy
uUniversità di Modena e Reggio Emilia, Modena, Italy
vIligan Institute of Technology (IIT), Iligan, Philippines
wNovosibirsk State University, Novosibirsk, Russia
†Deceased