EUROPEAN ORGANIZATION FOR NUCLEAR RESEARCH (CERN)

 CERN-EP-2016-251 LHCb-PAPER-2016-039 19 October 2016

New algorithms for identifying the flavour of ${B}^{0}$ mesons using pions and
protons

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

Two new algorithms for use in the analysis of $pp$ collision are developed to
identify the flavour of ${B}^{0}$ mesons at production using pions and protons
from the hadronization process. The algorithms are optimized and calibrated on
data, using ${{B}^{0}}\\!\rightarrow D^{-}\pi^{+}$ decays from $pp$ collision
data collected by LHCb at centre-of-mass energies of 7 and
8$\mathrm{\,Te\kern-1.00006ptV}$. The tagging power of the new pion algorithm
is 60% greater than the previously available one; the algorithm using protons
to identify the flavour of a ${B}^{0}$ meson is the first of its kind.

Published in EPJC

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

##  1 Introduction

Violation of $C\\!P$ symmetry in the $B$ system was observed for the first
time in the interference between mixing and decay processes [1]. Any
measurement of a decay-time-dependent asymmetry requires the determination of
the flavour of the $B$ meson at production. For $B$ mesons produced in $pp$
collisions, this information is obtained by means of several flavour-tagging
algorithms that exploit the correlations between $B$ flavour and other
particles in the event.

Algorithms determining the flavour content of $B$ meson by using particles
associated to its production are called same-side (SS) taggers. As an example,
in the production of ${B}^{0}$ mesons from excited charged $B$ mesons decaying
via strong interaction to ${{B}^{0}}\pi^{+}$, the pion charge identifies the
initial flavour of the ${B}^{0}$ meson.111The inclusion of charge-conjugate
processes is implied throughout the paper, unless otherwise noted. A charge
correlation can also arise from the hadronization process of the $b$ quark.
When a $\overline{b}$ and a $d$ quark hadronize as a ${B}^{0}$ meson, it is
likely that the corresponding $\overline{d}$ quark ends up in a charged pion
($u$ $\overline{d}$), or in an antiproton (${\overline{u}}\hskip
1.00006pt{\overline{u}}\hskip 1.00006pt{\overline{d}}$). The ${B}^{0}$ meson
and the pion or antiproton are produced in nearby regions of phase space.
Other algorithms used at LHCb, called opposite-side (OS) taggers [2, 3],
attempt to identify the flavour of the other $b$ hadron produced in the same
event.

A simple cut-based SS algorithm selecting pions was successfully used by LHCb
for tagging ${{B}^{0}}\\!\rightarrow{{J\mskip-3.0mu/\mskip-2.0mu\psi\mskip
2.0mu}}{{K}^{0}_{\rm\scriptscriptstyle S}}$ decays [4] in the measurement of
$\sin 2\beta$, and an SS kaon tagger [5] based on a neural network was used to
determine the flavour of ${B}^{0}_{s}$ mesons in measurements of the
$C\\!P$-violating phase $\phi_{{s}}$ [6, 7, 8]. This paper presents two new SS
algorithms exploiting the charge correlation of pions and protons with
${B}^{0}$ mesons, denoted $\rm SS$$\pi$ and $\rm SS$$p$. This is the first
time that protons are used for flavour tagging. The two algorithms are
combined into a single tagger, $\rm SScomb$. Both algorithms are based on
multivariate selections and are optmized, calibrated and validated using
${{B}^{0}}\\!\rightarrow D^{-}\pi^{+}$ and
${{B}^{0}}\\!\rightarrow{{K}^{+}}{{\pi}^{-}}$ decays collected by LHCb in Run
1.

The performance of a flavour-tagging algorithm is measured by its tagging
efficiency $\varepsilon_{\mathrm{tag}}$, mistag fraction $\omega$, dilution
$D$, and tagging power $\varepsilon_{\mathrm{eff}}$, defined as

$\varepsilon_{\mathrm{tag}}=\frac{R+W}{R+W+U},\ \ \ \ \ \omega=\frac{W}{R+W},\
\ \ D=1-2\omega,\ \ \
\varepsilon_{\mathrm{eff}}=\varepsilon_{\mathrm{tag}}D^{2},$ (1)

where $R$, $W$, and $U$ are the numbers of correctly-tagged, incorrectly-
tagged, and untagged ${B}^{0}$ signal candidates. The tagging power determines
the sensitivity to the measurement of a decay-time-dependent $C\\!P$ asymmetry
[9], as it quantifies the effective reduction in the sample size of flavour-
tagged ${B}^{0}$ candidates. It is the figure of merit used to optimize the
algorithms. Each algorithm provides a decision on the flavour of the ${B}^{0}$
candidate and an estimate of the probability $\eta$ that this decision is
incorrect. The probability is used to determine a weight applied to the
${B}^{0}$ candidate, in order to maximize the tagging power of a sample of
${B}^{0}$ mesons in a time-dependent analysis. The probabilities provided by
the two SS taggers are used to combine their decisions into the $\rm SScomb$
decision, which can be further combined with the decision of other taggers [2,
3].

Table 1: Expected correlation between the flavour of a $B$ meson and the hadronization products. $B$ meson | pion | proton | kaon
---|---|---|---
${B}^{0}$ | ${\pi}^{+}$ | $\overline{p}$ | ${\kern 1.99997pt\overline{\kern-1.99997ptK}{}}{}^{0}$
${{B}^{+}}$ | ${\pi}^{-}$ | $\overline{p}$ | ${K}^{-}$

The expected relationship between the flavour of charged and neutral $B$
mesons and the charge of the tagging particle is reported in Table 1. For a
${B}^{+}$ meson the same correlation as for a ${B}^{0}$ meson holds in the
case of protons, but with opposite charge in the case of pions. In addition,
the tagging kaons carry the same charge as pions, while they are neutral for a
${B}^{0}$. Since misidentified hadrons affect the tagging efficiency and the
mistag fraction of charged and neutral mesons in different ways, ${B}^{+}$
decays cannot be reliably used for the tuning and calibration of the SS
taggers. As a consequence, ${B}^{0}$ decays are used, and a time-dependent
analysis is required to determine the mistag fraction.

##  2 Detector

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 $4{\rm\,Tm}$, and three stations
of silicon-strip detectors and straw drift tubes placed downstream of the
magnet. Regular reversal of the magnet polarity allows a quantitative
assessment of detector-induced charge asymmetries. 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_{\rm T}$}){\,\upmu\rm m}$, where $p_{\rm T}$ is
the component of the momentum transverse to the beam, in
${\mathrm{\,Ge\kern-1.00006ptV\\!/}c}$.

Particularly relevant for this analysis is the identification of the different
species of charged hadrons, which mainly relies on the information of two
ring-imaging Cherenkov detectors. The first one covers the low and
intermediate momentum region 2 - 40${\mathrm{\,Ge\kern-1.00006ptV\\!/}c}$ over
the full spectrometer angular acceptance of 25 - 300$\rm\,mrad$. The second
Cherenkov detector covers the high momentum region 15 -
100${\mathrm{\,Ge\kern-1.00006ptV\\!/}c}$ over the angular range 15 -
120$\rm\,mrad$ [12].

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 online event selection is performed by a trigger [13], which consists of a
hardware stage, based on information from the calorimeter and muon systems,
followed by a software stage, which applies a full event reconstruction. At
the hardware trigger stage, events are required to have a muon with high
$p_{\rm T}$ or a hadron, photon or electron with high transverse energy in the
calorimeters. The software trigger requires a two-, three- or four-track
secondary vertex detached from the PV. A multivariate algorithm [14] is used
for the identification of secondary vertices consistent with the decay of a
$b$ hadron.

Samples of simulated events are used to model the signal mass and decay-time
distributions. In the simulation, $pp$ collisions are generated using Pythia
[15, *Sjostrand:2007gs] with a specific LHCb configuration [17]. Decays of
hadronic particles are described by EvtGen [18], in which final-state
radiation is generated using Photos [19]. The interaction of the generated
particles with the detector, and its response, are implemented using the
Geant4 toolkit [20, *Agostinelli:2002hh] as described in Ref. [22].

##  3 Development of the same-side taggers

The $\rm SS$$\pi$ and $\rm SS$$p$ algorithms are developed following similar
strategies. A sample of ${B}^{0}$ mesons decaying into the flavour-specific
final state $D^{-}\pi^{+}$, with ${D}^{-}$ candidates reconstructed in the
final state ${K}^{+}$ ${\pi}^{-}$ ${\pi}^{-}$, is selected using requirements
similar to those presented in Ref. [23]. The sample is collected from $pp$
collisions at $\sqrt{s}=8\mathrm{\,Te\kern-1.00006ptV}$, corresponding to an
integrated luminosity of 2$\mbox{\,fb}^{-1}$. Tagging pion or proton
candidates, with their charge correlated with the ${B}^{0}$ flavour, are
selected by means of a set of loose selection requirements and a multivariate
classifier, as described below. The ${{B}^{0}}\\!\rightarrow D^{-}\pi^{+}$
candidates are separated randomly into three disjoint subsamples of equal
size. The first sample is used for training the multivariate classifiers, the
second is used for determining the probability of an incorrect tagging
decision, and the third is used to evaluate the calibration of the mistag
probability.

The correctness of a tagging decision is evaluated by comparing the charge of
the tagging particle with the ${B}^{0}$ decay flavour as determined by the
reconstructed final state. Those ${B}^{0}$ candidates that have oscillated
before decaying enter the training process with an incorrectly assigned
production flavour. In the training phase the dilution is reduced by requiring
the decay time of the reconstructed ${B}^{0}$ mesons to be smaller than
2.2${\rm\,ps}$. This value was optimized with simulated events and reduces the
fraction of oscillated candidates to about 11%, keeping 66% of the original
sample.

The signal and background components of the ${B}^{0}$ sample are determined by
an unbinned maximum likelihood fit to the ${D}^{-}$ ${\pi}^{+}$ mass
distribution of the selected candidates in the region [5.2,
5.5]${\mathrm{\,Ge\kern-1.00006ptV\\!/}c^{2}}$. The signal is described by a
Johnson’s $S_{U}$ distribution [24], while the combinatorial background is
modelled by the sum of an exponential function and a constant. All parameters
are free to vary in the fit. A small component of ${{B}^{0}}\\!\rightarrow
D^{-}K^{+}$ decays ($\sim$1.2% as estimated from simulation), with the kaon
misidentified as a pion, is neglected in the fit. The number of signal
candidates in the full 2$\mbox{\,fb}^{-1}$ sample, estimated by the mass fit
and shown in Fig. 1, is 300 370 $\pm$ 674\. The fit to the mass distribution
is used to assign event-by-event weights (sWeights), using the sPlot technique
[25]. The weights are subsequently used to subtract the background
contribution when training the $\rm SS$$\pi$ and $\rm SS$$p$ classifiers and
in the fits to the ${B}^{0}$ decay-time distribution.

Figure 1: Mass distribution of ${{B}^{0}}\\!\rightarrow D^{-}\pi^{+}$
candidates with fit projections overlaid. Data points (black dots) correspond
to the ${B}^{0}$ candidates selected in the 2 $\mbox{\,fb}^{-1}$ data sample
collected at $\sqrt{s}=8\mathrm{\,Te\kern-1.00006ptV}$. The solid blue curve
represents the total fit function which is the sum of signal (red dashed) and
combinatorial background (green dash-dotted).

The loose selection requirements reduce the multiplicity of pion (proton)
candidates to 2.3 (1.7) per ${{B}^{0}}\\!\rightarrow D^{-}\pi^{+}$ signal
candidate, and are reported in Table 2. Only tracks with hits in all tracking
detectors are considered as tagging candidates. The following observables are
used: the $\chi^{2}/\mathrm{ndf}$ of the track fit, where ndf is the number of
degrees of freedom, the track transverse momentum
$p_{\mathrm{T}}^{\mathrm{track}}$, the ratio between the track impact
parameter with respect to the PV associated to the ${B}^{0}$ meson and the
error on this variable $\mathrm{IP}/\sigma_{\mathrm{IP}}$, the ratio between
the track impact parameter with respect to any other PV in the event and its
error $\mathrm{IP}_{\mathrm{PU}}/\sigma_{\mathrm{IP}_{\mathrm{PU}}}$, the
difference between the logarithms of the likelihood of the proton and pion
hypothesis $\log L_{p}-\log L_{\pi}$, or kaon and pion hypothesis $\log
L_{K}-\log L_{\pi}$. The likelihoods for the various mass hypothesis are
determined using the track and the Cherenkov angle information, as described
in Ref. [26]. For particles passing the loose selection criteria the
efficiency to identify a pion is 89% with a kaon misidentification probability
of 2%, while the efficiency to identify a proton is 92% with a pion
misidentification probability of 5%. Since mutually exclusive particle
identification criteria are imposed, a given tagging track is identified
either as a pion or as a proton. If more than one PV is reconstructed in the
event, the PV associated to the ${B}^{0}$ meson is the one which has the
smallest increase in the vertex-fit $\chi^{2}$ when adding the ${B}^{0}$ meson
to the PV.

Additional requirements are introduced on the system formed by the tagging
particle and the reconstructed ${B}^{0}$ meson. They are applied to the total
transverse momentum of the system $p_{\mathrm{T}}^{\mathrm{tot}}$, the
difference between the pseudorapidity of the ${B}^{0}$ candidate and the
tagging particle $\Delta\eta$, the azimuthal angle $\Delta\phi$ between the
${B}^{0}$ candidate and the tagging particle, and the difference between the
invariant mass of the system and the mass of the ${B}^{0}$ and of the tagging
particle $\Delta Q=m({{B}^{0}}+h)-m({{B}^{0}})-m(h)$, where $h$ denotes the
hadron, $\pi$ or $p$. The vertex formed by the ${B}^{0}$ meson and the tagging
particle is required to have the $\chi^{2}$ of vertex fit
$\chi^{2}_{B^{0}-\mathrm{track}}$, less than 100.

Table 2: Loose selection requirements for the $\rm SS$$\pi$ and $\rm SS$$p$ algorithms. The variables used as input for the BDT classifiers are indicated by ✓. |  $\rm SS$$\pi$ |  $\rm SS$$p$
---|---|---
Variable | selection | BDT | selection | BDT
$\chi^{2}_{\mathrm{track}}/\mathrm{ndf}$ | $<3$ | ✓ | $<3$ | -
$p_{\mathrm{T}}^{\mathrm{track}}$ [GeV/c] | $>0.4$ | ✓ | $>0.4$ | ✓
$p^{\mathrm{track}}$ [GeV/c] | - | ✓ | - | ✓
$\mathrm{IP}/\sigma_{\mathrm{IP}}$ | $<4$ | ✓ | $<4$ | ✓
$\mathrm{IP}_{\mathrm{PU}}/\sigma_{\mathrm{IP}_{\mathrm{PU}}}$ | $>3$ | - | - | -
$\log L_{p}-\log L_{\pi}$ | $<5$ | - | $>5$ | ✓
$\log L_{K}-\log L_{\pi}$ | $<5$ | ✓ | - | -
$p_{\mathrm{T}}^{{{B}^{0}}}$ [GeV/c] | - | ✓ | - | -
$p_{\mathrm{T}}^{\mathrm{tot}}$ [GeV/c] | $>3$ | ✓ | $>3$ | ✓
$\chi^{2}_{B^{0}-\mathrm{track}}$ | $<100$ | - | $<100$ | -
$\Delta Q$ [GeV/c2] | $<1.2$ | ✓ | $<1.3$ | ✓
$\Delta\eta$ | $<1.2$ | ✓ | $<1.2$ | ✓
$\Delta\phi$ [rad] | $<1.1$ | ✓ | $<1.2$ | -
$\Delta R$ | - | ✓ | - | ✓
$PV_{\mathrm{tracks}}$ | - | ✓ | - | ✓

The multivariate classifiers used for the selection of the tagging particles
are boosted decision trees (BDT) [27] using the AdaBoost [28] method to
enhance and to increase the stability with respect to statistical
fluctuations. This choice has been shown to be optimal with respect to the
achievable tagging power. The classifiers take most of the above observables
as input, as specified in Table 2. In addition the BDTs use the following
variables: the momentum of the tagging particle $p^{\mathrm{track}}$, the
transverse momentum of the ${B}^{0}$ candidate $p_{\mathrm{T}}^{{{B}^{0}}}$,
the separation of tagging particle and the ${B}^{0}$ candidate $\Delta
R=\sqrt{\Delta\phi^{2}+\Delta\eta^{2}}$, and the number of tracks contributing
to the PV fit $PV_{\mathrm{tracks}}$. The sWeights are used to subtract the
contribution of background ${B}^{0}$ candidates in the training of the
classifiers. The charge of the tagging particle determines the flavour of the
${B}^{0}$ candidate. In case of multiple tagging particle candidates per
${B}^{0}$ candidate, the tagging particle with the highest BDT output value is
chosen. The BDT outputs, $\alpha_{\mathrm{BDT}}$, are shown in Fig. 2. The
global separation between signal and background is small, but enough to
provide useful information to determine the flavour of the ${B}^{0}$
candidate, as shown below.

Figure 2: Distribution of the BDT output of signal (correct-tag decision) and
background (wrong-tag decision) tagging particles, for (left) $\rm SS$$\pi$
and (right) $\rm SS$$p$ taggers. In case of multiple tagging candidates per
${B}^{0}$ candidate, only the candidate with the highest BDT output value is
shown.

##  4 Evaluation and calibration of mistag probability

###  4.1 The SS$\pi$ and SSp taggers

The BDT output is transformed into an estimate of the mistag probability
through linear regression. The decay-time distribution of all tagged ${B}^{0}$
candidates is considered and the dilution due to mixing is decoupled by means
of a full time-dependent analysis. Tagged ${B}^{0}$ candidates are divided
into eight bins of the BDT output and for each bin the probability of an
incorrect tagging decision is determined from an unbinned maximum likelihood
fit to the distribution of the measured decay time $t$ of the candidates,
using the sWeights. The probability density function (PDF) for the signal is
described as

${\cal S}(t,q)={\cal
N}~{}a(t)~{}e^{-t^{\prime}/\tau_{d}}(1+q(1-2\omega)\cos(\Delta
m_{d}~{}t^{\prime}))\otimes{\cal R}(t-t^{\prime}),$ (2)

where $t^{\prime}$ represents the true decay time, $\cal N$ is a normalization
factor, $\omega$ is the average mistag fraction in the bin, $q$ is the mixing
state ($q=+1$ when the flavour at production and the flavour at decay are the
same, $q=-1$ otherwise), ${\cal R}(t-t^{\prime})$ is the decay-time resolution
and $a(t)$ is the decay-time acceptance. The ${B}^{0}$ lifetime $\tau_{d}$,
and the mixing frequency $\Delta m_{d}$, are fixed in the fit to their known
values [29].

Equation 2 is obtained under the assumption of zero width difference
$\Delta\Gamma_{d}$ and neglecting the production and detection asymmetries
between ${B}^{0}$ and ${\kern 1.79993pt\overline{\kern-1.79993ptB}{}}{}^{0}$.
The decay-time resolution is modelled by a Gaussian function with a fixed
width of 50 $\rm\,fs$, as determined from simulation. The decay-time
acceptance $a(t)$, is described by a parametric function based on cubic
splines [30] whose nodes have fixed position and whose parameters are
determined from data. Figure 3 shows the measured average mistag rate per
subsample, interpolated with a third-order polynomial that represents $\eta$
as a function of $\alpha_{\mathrm{BDT}}$, for the $\rm SS$$\pi$ and $\rm
SS$$p$ taggers.

Figure 3: Measured average mistag fraction in bins of (left) $\rm SS$$\pi$ and
(right) $\rm SS$$p$ BDT output. The plots are obtained with the test sample of
background-subtracted ${{B}^{0}}\\!\rightarrow D^{-}\pi^{+}$ candidates. The
green-shaded area shows the confidence range within $\pm 1\sigma$.

This polynomial parametrization is then used to determine the mistag
probability $\eta(\alpha_{\mathrm{BDT}})$ of a ${B}^{0}$ candidate. Tagging
particles with $\eta(\alpha_{\mathrm{BDT}})>0.5$ are rejected. With the third
subsample of ${B}^{0}$ candidates, it is checked that the estimated mistag
probability corresponds to the true value by measuring the mistag fraction
$\omega$ with an unbinned likelihood fit to the decay-time distribution of the
${B}^{0}$ candidates. Possible differences between the mistag probability of
${B}^{0}$ and ${\kern 1.79993pt\overline{\kern-1.79993ptB}{}}{}^{0}$ mesons
may arise from the different interaction cross-sections of hadrons and
antihadrons in the detector material and from differences in detection
efficiencies of positive and negative hadrons. They are taken into account in
the decay-time fit by defining the variables

$\overline{\omega}=(\omega^{{{B}^{0}}}+\omega^{{{\kern
1.25995pt\overline{\kern-1.25995ptB}{}}{}^{0}}})/2,~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}\Delta\omega=\omega^{{{B}^{0}}}-\omega^{{{\kern
1.25995pt\overline{\kern-1.25995ptB}{}}{}^{0}}},\\\ $ (3)

where $\omega^{{{B}^{0}}}$ and $\omega^{{{\kern
1.25995pt\overline{\kern-1.25995ptB}{}}{}^{0}}}$ are the mistag fractions
related to ${B}^{0}$ and ${\kern
1.79993pt\overline{\kern-1.79993ptB}{}}{}^{0}$. Assuming a linear relation
between the measured and estimated mistag fractions, the calibration functions
are written as

$\begin{matrix}\omega^{{{B}^{0}}}(\eta)=p_{0}^{{{B}^{0}}}+p_{1}^{{{B}^{0}}}(\eta-\langle\eta\rangle),\\\
\omega^{{{\kern
1.25995pt\overline{\kern-1.25995ptB}{}}{}^{0}}}(\eta)=p_{0}^{{{\kern
1.25995pt\overline{\kern-1.25995ptB}{}}{}^{0}}}+p_{1}^{{{\kern
1.25995pt\overline{\kern-1.25995ptB}{}}{}^{0}}}(\eta-\langle\eta\rangle),\end{matrix}$
(4)

where $p_{i}^{{{B}^{0}}}$ and $p_{i}^{{{\kern
1.25995pt\overline{\kern-1.25995ptB}{}}{}^{0}}}$ (with $i=0,1$) are the
calibration parameters. The average calibration parameters and the differences
between the ${B}^{0}$ and ${\kern
1.79993pt\overline{\kern-1.79993ptB}{}}{}^{0}$ parameters are defined as

$\overline{p}_{i}=(p_{i}^{{{B}^{0}}}+p_{i}^{{{\kern
1.25995pt\overline{\kern-1.25995ptB}{}}{}^{0}}})/2,~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}\Delta
p_{i}=p_{i}^{{{B}^{0}}}-p_{i}^{{{\kern
1.25995pt\overline{\kern-1.25995ptB}{}}{}^{0}}}.$ (5)

The use of the arithmetic mean $\langle\eta\rangle$ of the $\eta$ distribution
aims at decorrelating $p_{0}$ and $p_{1}$. A perfect calibration corresponds
to $\overline{p}_{0}=\langle\eta\rangle$ and $\overline{p}_{1}=1$.

A difference in the number of reconstructed and tagged ${B}^{0}$ and ${\kern
1.79993pt\overline{\kern-1.79993ptB}{}}{}^{0}$ mesons arises from several
possible sources. Two of these sources are considered in the fit by
introducing an asymmetry in the detection efficiency of the final state
particles, defined as

$A_{\mathrm{det}}=\frac{\varepsilon_{\mathrm{det}}^{D^{+}\pi^{-}}-\varepsilon_{\mathrm{det}}^{D^{-}\pi^{+}}}{\varepsilon_{\mathrm{det}}^{D^{+}\pi^{-}}+\varepsilon_{\mathrm{det}}^{D^{-}\pi^{+}}},$
(6)

and an asymmetry of the tagging efficiencies, defined as

$A_{\mathrm{tag}}=\frac{\varepsilon_{\mathrm{tag}}^{{{\kern
1.25995pt\overline{\kern-1.25995ptB}{}}{}^{0}}}-\varepsilon_{\mathrm{tag}}^{{{B}^{0}}}}{\varepsilon_{\mathrm{tag}}^{{{\kern
1.25995pt\overline{\kern-1.25995ptB}{}}{}^{0}}}+\varepsilon_{\mathrm{tag}}^{{{B}^{0}}}}.$
(7)

With these additional inputs, the PDF becomes

${\cal S}(t,q)={\cal
N}~{}a(t)~{}e^{-t^{\prime}/\tau_{d}}(C_{\mathrm{cosh}}+C_{\mathrm{cos}}\cos(\Delta
m_{d}~{}t^{\prime}))\otimes{\cal R}(t-t^{\prime}).$ (8)

The coefficients $C_{\mathrm{cosh}}$ and $C_{\mathrm{cos}}$ are

$\displaystyle C_{\mathrm{cosh}}=$
$\displaystyle(1-r~{}A_{\mathrm{det}})\Bigl{(}1-\frac{a_{\mathrm{sl}}^{d}}{2}~{}\frac{1+r}{2}\Bigr{)}\Biggl{(}(1+A_{\mathrm{prod}}+A_{\mathrm{tag}})\Bigl{(}\frac{1-d}{2}+d(\omega+\Delta\omega)\Bigr{)}$
$\displaystyle+(1-A_{\mathrm{prod}}-A_{\mathrm{tag}})\Bigl{(}\frac{1+d}{2}-d(\omega-\Delta\omega)\Bigr{)}\Bigl{(}1+\frac{a_{sl}^{d}}{2}\Bigr{)}\Biggr{)},$
$\displaystyle C_{\mathrm{cos}}=$
$\displaystyle-r(1-r~{}A_{\mathrm{det}})\Bigl{(}1-\frac{a_{\mathrm{sl}}^{d}}{2}~{}\frac{1+r}{2}\Bigr{)}\Biggl{(}(1+A_{\mathrm{prod}}+A_{\mathrm{tag}})\Bigl{(}\frac{1-d}{2}+d(\omega+\Delta\omega)\Bigr{)}$
$\displaystyle-(1-A_{\mathrm{prod}}-A_{\mathrm{tag}})\Bigl{(}\frac{1+d}{2}-d(\omega-\Delta\omega)\Bigr{)}\Bigl{(}1+\frac{a_{\mathrm{sl}}^{d}}{2}\Bigr{)}\Biggr{)},$
(9)

where $r$ is the $B$ meson flavour at decay ($r=+1$ for ${{B}^{0}}\rightarrow
D^{-}\pi^{+}$, $r=-1$ for ${{\kern
1.79993pt\overline{\kern-1.79993ptB}{}}{}^{0}}\rightarrow D^{+}\pi^{-}$) and
$d$ is the tagging decision ($d=+1$ for $\pi^{+}$ ($\overline{p}$), $d=-1$ for
$\pi^{-}$ ($p$)). These coefficients also take into account the production
asymmetry, $A_{\mathrm{prod}}=\frac{N_{{{\kern
0.89996pt\overline{\kern-0.89996ptB}{}}{}^{0}}}-N_{{{B}^{0}}}}{N_{{{\kern
0.89996pt\overline{\kern-0.89996ptB}{}}{}^{0}}}+N_{{{B}^{0}}}}$, and the
asymmetry in mixing, or flavour-specific asymmetry, $a_{\mathrm{sl}}^{d}$.
These two asymmetries cannot be distinguished from the tagging and detection
asymmetries and are fixed in the fit. The production asymmetry is fixed to the
value measured in Ref. [31], $A_{\mathrm{prod}}=(-0.58\pm 0.70)\%$, while
$a_{\mathrm{sl}}^{d}$ is fixed to the world average
$a_{\mathrm{sl}}^{d}=(-0.15\pm 0.17)\%$ [32]. The effect of their
uncertainties on the calibration parameters is included in the systematic
uncertainty.

The calibration parameters for the two taggers obtained in the fit to the
calibration sample of ${{B}^{0}}\\!\rightarrow D^{-}\pi^{+}$ decays are
reported in Table 3. The correlations between the calibration parameters are
below 10%, except for the asymmetry of the tagging efficiencies, which has a
correlation of about 16% with $\Delta p_{0}$ and $\Delta p_{1}$ and about 64%
with $A_{\mathrm{det}}$. For the $\rm SS$$\pi$ tagger, $A_{\mathrm{tag}}$,
$\Delta p_{0}$ and $\Delta p_{1}$ are zero within one standard deviation,
showing no significant difference in tagging behaviour between ${B}^{0}$ and
${\kern 1.79993pt\overline{\kern-1.79993ptB}{}}{}^{0}$ decays. For the $\rm
SS$$p$ tagger, it is found that $\Delta p_{0}<0$, as a consequence of the
higher interaction cross-section of anti-protons with matter compared to
protons. A similar effect is reported for kaon taggers [5]. The fit result of
the detection asymmetry is comparable for the two taggers
($A_{\mathrm{det}}^{\rm SS{\pi}}=(-0.87\pm 0.48)\%$,
$A_{\mathrm{det}}^{{\mathrm{SS}}{\mathit{p}}}=(-0.66\pm 0.62)\%$) and in
agreement with that found in Ref. [33]. The systematic uncertainties on the
parameters will be described in Section 5.

Table 3: Calibration parameters for the $\rm SS$$\pi$, $\rm SS$$p$ and $\rm
SScomb$ taggers where the first uncertainties are statistical and the second
are systematic.

|  $\rm SS$$\pi$ |  $\rm SS$$p$ | $\rm SScomb$
---|---|---|---
$\langle\eta\rangle$ | $\phantom{0}0.444$ | $\phantom{0}0.461$ | $\phantom{0}0.439$
$\overline{p}_{0}$ | $\phantom{+}0.446\phantom{0}\pm 0.003\phantom{0}\pm 0.001\phantom{0}$ | $\phantom{+}0.468\phantom{0}\pm 0.004\phantom{0}\pm 0.001\phantom{0}$ | $\phantom{+}0.441\phantom{0}\pm 0.003\phantom{0}\pm 0.002\phantom{0}$
$\overline{p}_{1}$ | $\phantom{+}1.05\phantom{00}\pm 0.05\phantom{00}\pm 0.01\phantom{00}$ | $\phantom{+}1.04\phantom{00}\pm 0.08\phantom{00}\pm 0.02\phantom{00}$ | $\phantom{+}0.99\phantom{00}\pm 0.04\phantom{00}\pm 0.02\phantom{00}$
$\Delta p_{0}$ | $-0.0028\pm 0.0036\pm 0.0016$ | $-0.0218\pm 0.0048\pm 0.0016$ | $-0.0056\pm 0.0036\pm 0.0018$
$\Delta p_{1}$ | $\phantom{+}0.015\phantom{0}\pm 0.074\phantom{0}\pm 0.014\phantom{0}$ | $\phantom{+}0.140\phantom{0}\pm 0.112\phantom{0}\pm 0.019\phantom{0}$ | $\phantom{+}0.052\phantom{0}\pm 0.060\phantom{0}\pm 0.017\phantom{0}$
$A_{\mathrm{tag}}$ | $-0.001\phantom{0}\pm 0.007\phantom{0}\pm 0.007\phantom{0}$ | $\phantom{+}0.008\phantom{0}\pm 0.009\phantom{0}\pm 0.007\phantom{0}$ | $\phantom{+}0.002\phantom{0}\pm 0.007\phantom{0}\pm 0.007\phantom{0}$

After calibration, the total tagging power of the sample is calculated as

$\varepsilon_{\mathrm{eff}}=\frac{\sum_{i=1}^{N_{\mathrm{tag}}}(1-2{\overline{\omega}}(\eta_{i}))^{2}s_{i}}{\sum_{j=1}^{N}s_{j}}$
(10)

where $s_{i}$ is the sWeight of the candidate $i$, $N$ and $N_{\mathrm{tag}}$
are the numbers of total and tagged candidates, having mistag probability
$\eta_{i}$, and the average mistag fraction ${\overline{\omega}}(\eta_{i})$ is
calculated using Eqs. 3 and 4. Candidates with a mistag probability larger
than 0.5 are considered untagged and are removed from the sum in the
numerator, effectively setting $\omega(\eta_{i})=0.5$. The tagging
performances for the $\rm SS$$\pi$ and $\rm SS$$p$ taggers are reported in
Table 4.

Table 4: Tagging efficiencies and tagging power of the $\rm SS$$\pi$, $\rm SS$$p$ and $\rm SScomb$ algorithms. The $\rm SScomb$ efficiencies are shown splitting the sample in candidates tagged exclusively by $\rm SS$$\pi$ or $\rm SS$$p$, or by both. As explained in the text, there is a large overlap between the $\rm SS$$\pi$ and $\rm SS$$p$ taggers. Tagger | Sample |  $\varepsilon_{\mathrm{tag}}$ [$\%$] |  $\varepsilon_{\mathrm{eff}}$ [$\%$]
---|---|---|---
$\rm SS$$\pi$ |  | $71.96\pm 0.23$ | $1.69\pm 0.10$
$\rm SS$$p$ |  | $38.56\pm 0.15$ | $0.53\pm 0.05$
$\rm SScomb$ |  $\rm SS$$\pi$ only | $35.91\pm 0.14$ | $0.95\pm 0.08$
$\rm SS$$p$ only | $\phantom{0}8.75\pm 0.10$ | $0.12\pm 0.02$
$\rm SS$$\pi$ & $\rm SS$$p$ | $34.74\pm 0.15$ | $1.04\pm 0.07$
total | $79.40\pm 0.23$ | $2.11\pm 0.11$

The fit of the decay-time distribution is repeated after dividing events into
bins of predicted mistag probability. The distribution of $\eta$ and the
dependence of the measured mistag fraction on $\eta$ are shown in Fig. 4 with
the linear fits superimposed, demonstrating the expected linearity. In Figs. 5
and 6 the time-dependent mixing asymmetries
$A=\frac{N^{\mathrm{unmix}}-N^{\mathrm{mix}}}{N^{\mathrm{unmix}}+N^{\mathrm{mix}}}$
are shown for each of the five bins.

Figure 4: (top left) Distribution of the mistag probability $\eta^{\rm
SS{\pi}}$ and (top right) measured mistag fraction $\omega$ as a function of
$\eta^{\rm SS{\pi}}$. (bottom left) Distribution of the mistag probability
$\eta^{\rm SS{p}}$ and (bottom right) measured mistag fraction $\omega$ as a
function of $\eta^{\rm SS{p}}$. The green-shaded area shows the 68% confidence
range.

Figure 5: Mixing asymmetry in bins of mistag probability using the $\rm
SS$$\pi$ tagger.

Figure 6: Mixing asymmetry in bins of mistag probability using the $\rm SS$$p$
tagger.

###  4.2 The SScomb tagger

Even though a given tagging particle can be selected by only one of the $\rm
SS$$\pi$ or the $\rm SS$$p$ taggers, both taggers may find a candidate track
in the same event. About 50% of the candidates tagged by $\rm SS$$\pi$ are
also tagged by $\rm SS$$p$, and 80% of the candidates tagged by $\rm SS$$p$
are also tagged by $\rm SS$$\pi$. When both taggers provide a decision, they
are combined into a single decision. Since the correlation between the $\rm
SS$$\pi$ and $\rm SS$$p$ decisions, and between their mistag probabilities, is
found to be small, it is neglected when combining them using the following
formulae

$p(b)=\prod_{i}\bigg{(}\frac{1+d_{i}}{2}-d_{i}(1-\eta_{i})\bigg{)},\qquad\qquad
p(\overline{b})=\prod_{i}\bigg{(}\frac{1-d_{i}}{2}+d_{i}(1-\eta_{i})\bigg{)},$
(11)

where p($b$) and p($\overline{b}$) are the probabilities that the signal $B$
meson contains a $b$ or a $\overline{b}$ quark respectively, and $d_{i}$ is
the tagging decision of the tagger $i=$$\rm SS$$\pi$, $\rm SS$$p$. The
normalized probabilities are

$P(\overline{b})=\frac{p(\overline{b})}{p(\overline{b})+p(b)},\qquad\qquad
P(b)=1-P(\overline{b}).$ (12)

For $P(\overline{b})>P(b)$ the combined tagging decision is $d=+1$ and the
final mistag probability is $\eta=P(b)$. Otherwise, the combined tagging
decision and the mistag probability are $d=-1$ and $\eta=P(\overline{b})$.

The combination procedure, which assumes no correlation, is validated by
checking the combined mistag probability a posteriori. Assuming a linear
relation between the predicted mistag probability and the true mistag
fraction, the calibration parameters in the overlapping sample give
$(\overline{p}_{0}-\langle\eta\rangle)=0.010\pm 0.005$ and
$(\overline{p}_{1}-1)=0.01\pm 0.08$. The calibration is repeated on the sample
of all ${B}^{0}$ candidates tagged by the $\rm SScomb$ tagger, and the
calibration parameters derived from the unbinned likelihood fit with the PDF
of Eq. 8, reported in Table 3, demonstrate its validity. The performance of
$\rm SScomb$ is reported in Table 4. The total tagging power obtained by the
combined algorithm is $(2.11\pm 0.11)\%$, a relative increase of 25% compared
to that provided by the $\rm SS$$\pi$ tagger alone.

A higher tagging power can be obtained from the combination of the SScomb
tagger with the OS tagger. The OS tagger is the combination of various OS
tagging algorithms using electrons and muons from semileptonic decays of $b$
hadrons, kaons from $b\rightarrow c\rightarrow s$ decay chains and the
inclusive reconstruction of a secondary vertex of the decay products of the
opposite side $b$ hadron. The SS and OS taggers are found to be uncorrelated,
so their combination follows the same procedure as the combination of $\rm
SS$$\pi$ and $\rm SS$$p$ into $\rm SScomb$. The calibration of the combined
mistag probability is verified a posteriori with a fit of the decay-time
distribution of the ${B}^{0}$ candidates. For ${{B}^{0}}\\!\rightarrow
D^{-}\pi^{+}$ decays, the total tagging efficiency and the total tagging power
are $(84.48\pm 0.26)\%$ and $(5.14\pm 0.15)\%$, respectively. On the same
sample, the use of the OS tagger only provides a tagging efficiency and a
tagging power of $(37.95\pm 0.15)\%$ and $(3.52\pm 0.17)\%$, respectively.

##  5 Validation and systematic uncertainties

A possible dependence of the calibration parameters of the SS taggers on
properties of the event sample is checked by repeating the calibration after
splitting the data according to the data-taking conditions (magnet polarity),
global event properties (total number of reconstructed tracks, number of
primary vertices) or according to the kinematic properties of the ${B}^{0}$
meson (transverse momentum, pseudorapidity and azimuthal angle). The average
mistag probability has a weak dependence on the number of tracks in the event.
On the other hand, it decreases as a function of the transverse momentum since
the number of random tracks decreases at high $p_{T}^{B}$. The tagging
efficiency is nearly constant for pions, while the requirement on proton
identification reduces the number of proton candidates at high $p_{T}^{B}$. A
similar dependence is present versus the pseudorapidity of the ${B}^{0}$
meson. Since the average mistag fraction and the $p_{0}$ parameter decrease
with increasing $p_{\rm T}^{{B}^{0}}$, the calibration remains valid in all
subsamples, with variations below two standard deviations.

The portability of the mistag calibration, from the training data sample to
other data samples and other ${B}^{0}$ decay modes, is validated using an
independent sample of ${{B}^{0}}\\!\rightarrow D^{-}\pi^{+}$ decays collected
at $\sqrt{s}$ = 7$\mathrm{\,Te\kern-1.00006ptV}$ (corresponding to an
integrated luminosity of 1$\mbox{\,fb}^{-1}$) and a sample of
${{B}^{0}}\\!\rightarrow{{K}^{+}}{{\pi}^{-}}$ decays collected at $\sqrt{s}$ =
8$\mathrm{\,Te\kern-1.00006ptV}$ (correspoding to an integrated luminosity of
2$\mbox{\,fb}^{-1}$). The same selection criteria and fitting procedure as
described above are used for the ${{B}^{0}}\\!\rightarrow D^{-}\pi^{+}$
validation sample at $\sqrt{s}$ = 7$\mathrm{\,Te\kern-1.00006ptV}$. The
calibration parameters for the $\rm SS$$\pi$, $\rm SS$$p$, and $\rm SScomb$
taggers determined from an unbinned maximum likelihood fit to the decay-time
distribution are compatible with those derived in the
8$\mathrm{\,Te\kern-1.00006ptV}$ sample. Consistent values of tagging power
are found for all taggers.

The selection criteria and the mass model for the
${{B}^{0}}\\!\rightarrow{{K}^{+}}{{\pi}^{-}}$ candidates are described in Ref.
[34]. The decay-time acceptance is parametrized using cubic splines with six
nodes, whose positions are fixed and whose coefficients are free in the fit.
The decay-time resolution is described by a Gaussian function with parameters
determined from simulation. The parameters shown in Table 5 demonstrate a good
portability of the mistag calibration, with
$\overline{p}_{0}-\langle\eta\rangle\approx 0$ and $\overline{p}_{1}-1\approx
0$ as expected. A lower tagging power is measured in this channel, giving
$(1.06\pm 0.09)$%, $(0.42\pm 0.06)$%, and $(1.37\pm 0.13)$% for $\rm SS$$\pi$,
$\rm SS$$p$ and $\rm SScomb$, respectively, as expected from the lower average
$p_{\rm T}$ of the selected ${B}^{0}$ candidates.

Table 5: Calibration parameters for the
${{B}^{0}}\\!\rightarrow{{K}^{+}}{{\pi}^{-}}$ decay sample. Uncertainties are
statistical only.

Tagger | $\langle\eta\rangle$ | $\overline{p}_{0}$ | $\overline{p}_{1}$ | $\Delta p_{0}$ | $\Delta p_{1}$ | $A_{\mathrm{tag}}$
---|---|---|---|---|---|---
$\rm SS$$\pi$ | $0.456$ | $0.452\pm 0.003$ | $1.06\pm 0.09$ | $\phantom{+}0.0053\pm 0.0042$ | $0.047\pm 0.115$ | $-0.009\pm 0.008$
$\rm SS$$p$ | $0.467$ | $0.459\pm 0.004$ | $0.80\pm 0.14$ | $-0.0138\pm 0.0051$ | $0.025\pm 0.141$ | $\phantom{+}0.008\pm 0.009$
$\rm SScomb$ | $0.452$ | $0.457\pm 0.003$ | $0.94\pm 0.07$ | $-0.0034\pm 0.0040$ | $0.079\pm 0.086$ | $\phantom{+}0.007\pm 0.007$

Several sources of systematic uncertainties on the calibration parameters are
studied and the associated uncertainties are reported in Table 6.
Uncertainties related to the mass model and background unfolding procedure are
assessed by repeating the calibration replacing the sWeights derived in the
fit to the mass distribution of all ${B}^{0}$ candidates by the sWeights
derived after restricting the sample to tagged ${B}^{0}$ candidates. In a
second test, the signal mass model is replaced by a Hypatia function [35]
convolved with a Gaussian function. The sum in quadrature of the variations of
the calibration parameters observed in the two tests is taken as uncertainty
on the mass model.

Uncertainties related to the decay-time acceptance model are assessed by
changing the number of nodes in the cubic splines from six to nine and are
found to be negligible. A negligible uncertainty is associated to the decay-
time resolution model. The mistag model uncertainties are assessed by
comparing the calibration parameters derived in the nominal fit and those
derived in fits with the mistag probability binned in categories. Five, seven
and nine bins are tested and the largest observed variation of the parameters
is taken as a systematic uncertainty. Differences between the results of the
two implementations of the time-dependent fit are due to the dependence of the
mistag probability on the decay time. Pseudoexperiments are generated where
the mistag probability has the same dependence on time as in data and are
fitted with the two approaches. The difference in parameters is similar to or
smaller than that observed in data.

Uncertainties related to neglecting $\Delta\Gamma_{d}$ and possible $C\\!P$
violation in the ${{B}^{0}}\\!\rightarrow D^{-}\pi^{+}$ decays in the decay-
time fit, are studied by performing pseudoexperiments in which changes
associated with the parameter under study are incorporated in the generation
and neglected in the subsequent fit. Terms proportional to the relevant
$C\\!P$ parameters are added to the PDF in Eq. 8 and the values of the
parameters are taken from Ref. [32]. The associated systematic uncertainties
are taken to be the changes in the calibration parameters with respect to
perfect calibration ($\overline{p}_{0}=\langle\eta\rangle$,
$\overline{p}_{1}$=1), used in the generation. Uncertainties related to the
variation of $A_{\mathrm{prod}}$ and $a_{\mathrm{sl}}^{d}$, which are fixed in
the decay-time fit, are evaluated with pseudoexperiments where the parameters
are varied within their uncertainties. The uncertainties are determined in the
$\rm SS$$\pi$ configuration and attributed to both taggers. A breakdown of the
systematic uncertainties related to the decay-time model is shown in Table 7.

Table 6: Systematic uncertainties on the calibration parameters of $\rm SS$$\pi$, $\rm SS$$p$ and $\rm SScomb$ taggers. The total systematic uncertainty is the squared sum of all contributions. A dash indicates a value negligible with respect to the quoted precision. Tagger | Source | $\sigma(\overline{p}_{0})$ | $\sigma(\overline{p}_{1})$ | $\sigma(\Delta p_{0})$ | $\sigma(\Delta p_{1})$ | $\sigma(A_{\mathrm{tag}})$
---|---|---|---|---|---|---
$\rm SS$$\pi$ | mass model | – | – | – | $0.001$ | –
mistag model | $0.001$ | $0.01$ | $0.0002$ | $0.007$ | –
decay model | 0.001 | 0.01 | 0.0016 | 0.012 | 0.007
total | $0.001$ | $0.01$ | $0.0016$ | $0.014$ | $0.007$
$\rm SS$$p$ | mass model | – | – | $0.0002$ | $0.004$ | –
mistag model | 0.001 | 0.02 | – | 0.014 | 0.001
decay model | 0.001 | 0.01 | 0.0016 | 0.012 | 0.007
total | $0.001$ | $0.02$ | $0.0016$ | $0.019$ | $0.007$
$\rm SScomb$ | mass model | – | – | $0.0008$ | $0.005$ | –
mistag model | 0.002 | 0.02 | 0.0004 | 0.010 | 0.001
decay model | 0.001 | 0.01 | 0.0016 | 0.012 | 0.007
total | $0.002$ | $0.02$ | $0.0018$ | $0.017$ | $0.007$
Table 7: Systematic uncertainties related to the decay-time model. A dash indicates a value negligible with respect to the quoted precision. Source | $\sigma(\overline{p}_{0})$ | $\sigma(\overline{p}_{1})$ | $\sigma(\Delta p_{0})$ | $\sigma(\Delta p_{1})$ | $\sigma(A_{\mathrm{tag}})$
---|---|---|---|---|---
$\Delta\Gamma$ | 0.00013 | – | – | – | 0.001
$A_{\mathrm{prod}}$ | 0.00002 | – | – | – | 0.007
$a_{\mathrm{sl}}^{\mathrm{d}}$ | – | – | – | – | –
$C\\!P$ violation | 0.00124 | 0.01 | 0.0016 | 0.012 | 0.002
total | 0.001 | 0.01 | 0.0016 | 0.012 | 0.007

##  6 Conclusion

Two new same-side algorithms are developed to determine the production flavour
of ${B}^{0}$ mesons using pions and protons from the hadronization process.
This is the first time that protons are used to identify the flavour of a
${B}^{0}$ meson. The algorithms are optimized and calibrated on data using
${{B}^{0}}\\!\rightarrow D^{-}\pi^{+}$ decays. The calibration parameters of
the taggers are reported in Table 3. The efficiency and mistag probability of
the taggers depend on the kinematic properties of the ${B}^{0}$ decay mode
under study. Estimated mistag probabilities match the true mistag fraction
throughout the phase space. The new $\rm SS$$\pi$ tagger provides a tagging
power that is greater by 60% relative to the previous algorithm using pions,
employed in Ref. [4]. Adding the combination of the two new algorithms to the
existing OS taggers provides a relative increase of the total tagging power of
about 40%.

## 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).

## References

  * [1] Particle Data Group, K. A. Olive et al., _Review of particle physics, ” $C\\!P$ violation in the quark sector” (pages 223-234)_, Chin. Phys. C38 (2014) 090001
  * [2] LHCb collaboration, R. Aaij et al., _Opposite-side flavour tagging of $B$ mesons at the LHCb experiment_, Eur. Phys. J. C72 (2012) 2022, arXiv:1202.4979
  * [3] LHCb collaboration, R. Aaij et al., _$B$ flavour tagging using charm decays at the LHCb experiment_, JINST 10 (2015) P10005, arXiv:1507.07892
  * [4] LHCb collaboration, R. Aaij et al., _Measurement of $C\\!P$ violation in $B^{0}\rightarrow J/\psi K^{0}_{S}$ decays_, Phys. Rev. Lett. 115 (2015) 031601, arXiv:1503.07089
  * [5] LHCb collaboration, R. Aaij et al., _Neural-network-based same side kaon tagging algorithm calibrated with $B_{s}^{0}\rightarrow D_{s}^{-}\pi^{+}$ and $B_{s2}^{*}(5840)^{0}\rightarrow B^{+}K^{-}$ decays_, JINST 11 (2016) P05010, arXiv:1602.07252
  * [6] LHCb collaboration, R. Aaij et al., _Precision measurement of $C\\!P$ violation in $B_{s}^{0}\rightarrow J/\psi K^{+}K^{-}$ decays_, Phys. Rev. Lett. 114 (2015) 041801, arXiv:1411.3104
  * [7] LHCb collaboration, R. Aaij et al., _Measurement of the $C\\!P$-violating phase $\phi_{s}$ in ${{\kern 1.79993pt\overline{\kern-1.79993ptB}{}}{}^{0}_{s}}\rightarrow J/\psi\pi^{+}\pi^{-}$ decays_, Phys. Lett. B736 (2014) 186, arXiv:1405.4140
  * [8] LHCb collaboration, R. Aaij et al., _Measurement of the $C\\!P$-violating phase $\phi_{s}$ in ${{\kern 1.79993pt\overline{\kern-1.79993ptB}{}}{}^{0}_{s}}\rightarrow D_{s}^{+}D_{s}^{-}$ decays_, Phys. Rev. Lett. 113 (2014) 211801, arXiv:1409.4619
  * [9] Ed. A. J. Bevan, B. Golob, Th. Mannel, S. Prell and B. D. Yabsley, _The Physics of the B Factories_ , Eur. Phys. J. C74 (2014) 3026, arXiv:1406.6311
  * [10] LHCb collaboration, A. A. Alves Jr. et al., _The LHCb detector at the LHC_, JINST 3 (2008) S08005
  * [11] LHCb collaboration, R. Aaij et al., _LHCb detector performance_ , Int. J. Mod. Phys. A30 (2015) 1530022, arXiv:1412.6352
  * [12] M. Adinolfi et al., _Performance of the LHCb RICH detector at the LHC_, Eur. Phys. J. C73 (2013) 2431, arXiv:1211.6759
  * [13] R. Aaij et al., _The LHCb trigger and its performance in 2011_, JINST 8 (2013) P04022, arXiv:1211.3055
  * [14] V. V. Gligorov and M. Williams, _Efficient, reliable and fast high-level triggering using a bonsai boosted decision tree_ , JINST 8 (2013) P02013, arXiv:1210.6861
  * [15] T. Sjöstrand, S. Mrenna, and P. Skands, _PYTHIA 6.4 physics and manual_ , JHEP 05 (2006) 026, arXiv:hep-ph/0603175
  * [16] T. Sjöstrand, S. Mrenna, and P. Skands, _A brief introduction to PYTHIA 8.1_ , Comput. Phys. Commun. 178 (2008) 852, arXiv:0710.3820
  * [17] I. Belyaev et al., _Handling of the generation of primary events in Gauss, the LHCb simulation framework_ , J. Phys. Conf. Ser. 331 (2011) 032047
  * [18] D. J. Lange, _The EvtGen particle decay simulation package_ , Nucl. Instrum. Meth. A462 (2001) 152
  * [19] P. Golonka and Z. Was, _PHOTOS Monte Carlo: A precision tool for QED corrections in $Z$ and $W$ decays_, Eur. Phys. J. C45 (2006) 97, arXiv:hep-ph/0506026
  * [20] Geant4 collaboration, J. Allison et al., _Geant4 developments and applications_ , IEEE Trans. Nucl. Sci. 53 (2006) 270
  * [21] Geant4 collaboration, S. Agostinelli et al., _Geant4: A simulation toolkit_ , Nucl. Instrum. Meth. A506 (2003) 250
  * [22] M. Clemencic et al., _The LHCb simulation application, Gauss: Design, evolution and experience_, J. Phys. Conf. Ser. 331 (2011) 032023
  * [23] LHCb collaboration, R. Aaij et al., _Measurement of the ${{\kern 1.79993pt\overline{\kern-1.79993ptB}{}}{}^{0}_{s}}$ meson lifetime in $D_{s}^{+}\pi^{-}$ decays_, Phys. Rev. Lett. 113 (2014) 172001, arXiv:1407.5873
  * [24] N. L. Johnson, _Systems of frequency curves generated by methods of translation_ , Biometrika 36 (1949) 149
  * [25] M. Pivk and F. R. Le Diberder, _sPlot: A statistical tool to unfold data distributions_ , Nucl. Instrum. Meth. A555 (2005) 356, arXiv:physics/0402083
  * [26] A. A. Alves Jr. et al., _Performance of the LHCb muon system_ , JINST 8 (2013) P02022, arXiv:1211.1346
  * [27] L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification and regression trees, Wadsworth international group, Belmont, California, USA, 1984
  * [28] R. E. Schapire and Y. Freund, _A decision-theoretic generalization of on-line learning and an application to boosting_ , J. Comput. Syst. Sci. 55 (1997) 119
  * [29] Particle Data Group, K. A. Olive et al., _Review of particle physics_ , Chin. Phys. C38 (2014) 090001, and 2015 update
  * [30] C. de Boor, A Practical Guide to Splines (revised edition), Springer, New York, NY, USA, 2001
  * [31] LHCb collaboration, R. Aaij et al., _Measurement of the ${{\kern 1.79993pt\overline{\kern-1.79993ptB}{}}{}^{0}}$–${{B}^{0}}$ and ${{\kern 1.79993pt\overline{\kern-1.79993ptB}{}}{}^{0}_{s}}$–${{B}^{0}_{s}}$ production asymmetries in $pp$ collisions at $\sqrt{s}=7$ TeV_, Phys. Lett. B739 (2014) 218, arXiv:1408.0275
  * [32] Heavy Flavor Averaging Group, Y. Amhis et al., _Averages of $b$-hadron, $c$-hadron, and $\tau$-lepton properties as of summer 2014_, arXiv:1412.7515, updated results and plots available at http://www.slac.stanford.edu/xorg/hfag/
  * [33] LHCb collaboration, R. Aaij et al., _Measurement of the semileptonic $C\\!P$ asymmetry in ${{B}^{0}}$–${{\kern 1.79993pt\overline{\kern-1.79993ptB}{}}{}^{0}}$ mixing_, Phys. Rev. Lett. 114 (2015) 041601, arXiv:1409.8586
  * [34] LHCb collaboration, R. Aaij et al., _First measurement of time-dependent $C\\!P$ violation in $B_{s}^{0}\rightarrow K^{+}K^{-}$ decays_, JHEP 10 (2013) 183, arXiv:1308.1428
  * [35] D. Martinez-Santos and F. Dupertuis, _Mass distributions marginalized over per-event errors_ , Nucl. Instrum. Meth. A764 (2014) 150, arXiv:1312.5000

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.
Archilli43, P. d’Argent12, J. Arnau Romeu6, A. Artamonov37, M. Artuso61, E.
Aslanides6, G. Auriemma26, M. Baalouch5, I. Babuschkin56, S. Bachmann12, J.J.
Back50, A. Badalov38, C. Baesso62, S. Baker55, W. Baldini17, R.J. Barlow56, C.
Barschel40, S. Barsuk7, W. Barter40, M. Baszczyk27, V. Batozskaya29, B.
Batsukh61, V. Battista41, A. Bay41, L. Beaucourt4, J. Beddow53, F. Bedeschi24,
I. Bediaga1, L.J. Bel43, V. Bellee41, N. Belloli21,i, K. Belous37, I.
Belyaev32, E. Ben-Haim8, G. Bencivenni19, S. Benson43, J. Benton48, A.
Berezhnoy33, R. Bernet42, A. Bertolin23, F. Betti15, M.-O. Bettler40, M. van
Beuzekom43, Ia. Bezshyiko42, S. Bifani47, P. Billoir8, T. 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.
Bowen42, C. Bozzi17,40, S. Braun12, M. Britsch12, T. Britton61, J.
Brodzicka56, E. Buchanan48, C. Burr56, A. Bursche2, J. Buytaert40, S.
Cadeddu16, R. Calabrese17,g, M. Calvi21,i, M. Calvo Gomez38,m, A. Camboni38,
P. Campana19, D. Campora Perez40, D.H. Campora Perez40, L. Capriotti56, A.
Carbone15,e, G. Carboni25,j, R. Cardinale20,h, A. Cardini16, P. Carniti21,i,
L. Carson52, K. Carvalho Akiba2, G. Casse54, L. Cassina21,i, L. Castillo
Garcia41, M. Cattaneo40, Ch. Cauet10, G. Cavallero20, R. Cenci24,t, M.
Charles8, Ph. Charpentier40, G. Chatzikonstantinidis47, M. Chefdeville4, S.
Chen56, S.-F. Cheung57, V. Chobanova39, M. Chrzaszcz42,27, X. Cid Vidal39, G.
Ciezarek43, P.E.L. Clarke52, M. Clemencic40, H.V. Cliff49, J. Closier40, V.
Coco59, J. Cogan6, E. Cogneras5, V. Cogoni16,40,f, L. Cojocariu30, G.
Collazuol23,o, P. Collins40, A. Comerma-Montells12, A. Contu40, A. Cook48, G.
Coombs40, S. Coquereau38, G. Corti40, M. Corvo17,g, C.M. Costa Sobral50, B.
Couturier40, G.A. Cowan52, D.C. Craik52, A. Crocombe50, M. Cruz Torres62, S.
Cunliffe55, R. Currie55, C. D’Ambrosio40, F. Da Cunha Marinho2, E.
Dall’Occo43, J. Dalseno48, P.N.Y. David43, A. Davis59, O. De Aguiar
Francisco2, K. De Bruyn6, S. De Capua56, M. De Cian12, J.M. De Miranda1, L. De
Paula2, M. De Serio14,d, P. De Simone19, C.-T. Dean53, D. Decamp4, M.
Deckenhoff10, L. Del Buono8, M. Demmer10, A. Dendek28, D. Derkach35, O.
Deschamps5, F. Dettori40, B. Dey22, A. Di Canto40, H. Dijkstra40, F. Dordei40,
M. Dorigo41, A. Dosil Suárez39, A. Dovbnya45, K. Dreimanis54, L. Dufour43, G.
Dujany56, K. Dungs40, P. Durante40, R. Dzhelyadin37, A. Dziurda40, A.
Dzyuba31, N. Déléage4, S. Easo51, M. Ebert52, U. Egede55, V. Egorychev32, S.
Eidelman36,w, S. Eisenhardt52, U. Eitschberger10, R. Ekelhof10, L. Eklund53,
Ch. Elsasser42, S. Ely61, S. Esen12, H.M. Evans49, T. Evans57, A. Falabella15,
N. Farley47, S. Farry54, R. Fay54, D. Fazzini21,i, D. Ferguson52, A. Fernandez
Prieto39, F. Ferrari15,40, F. Ferreira Rodrigues1, M. Ferro-Luzzi40, S.
Filippov34, R.A. Fini14, M. Fiore17,g, M. Fiorini17,g, M. Firlej28, C.
Fitzpatrick41, T. Fiutowski28, F. Fleuret7,b, K. Fohl40, M. Fontana16,40, F.
Fontanelli20,h, D.C. Forshaw61, R. Forty40, V. Franco Lima54, M. Frank40, C.
Frei40, J. Fu22,q, E. Furfaro25,j, C. Färber40, A. Gallas Torreira39, D.
Galli15,e, S. Gallorini23, S. Gambetta52, M. Gandelman2, P. Gandini57, Y.
Gao3, L.M. Garcia Martin68, J. García Pardiñas39, J. Garra Tico49, L.
Garrido38, P.J. Garsed49, D. Gascon38, C. Gaspar40, L. Gavardi10, G. Gazzoni5,
D. Gerick12, E. Gersabeck12, M. Gersabeck56, T. Gershon50, Ph. Ghez4, S.
Gianì41, V. Gibson49, O.G. Girard41, L. Giubega30, K. Gizdov52, V.V.
Gligorov8, D. Golubkov32, A. Golutvin55,40, A. Gomes1,a, I.V. Gorelov33, C.
Gotti21,i, M. Grabalosa Gándara5, R. Graciani Diaz38, L.A. Granado Cardoso40,
E. Graugés38, E. Graverini42, G. Graziani18, A. Grecu30, P. Griffith47, L.
Grillo21,40,i, B.R. Gruberg Cazon57, O. Grünberg66, E. Gushchin34, Yu. Guz37,
T. Gys40, C. Göbel62, T. Hadavizadeh57, C. Hadjivasiliou5, G. Haefeli41, C.
Haen40, S.C. Haines49, S. Hall55, B. Hamilton60, X. Han12, S. Hansmann-
Menzemer12, N. Harnew57, S.T. Harnew48, J. Harrison56, M. Hatch40, J. He63, T.
Head41, A. Heister9, K. Hennessy54, P. Henrard5, L. Henry8, J.A. Hernando
Morata39, E. van Herwijnen40, M. Heß66, A. Hicheur2, D. Hill57, C. Hombach56,
H. Hopchev41, W. Hulsbergen43, T. Humair55, M. Hushchyn35, N. Hussain57, D.
Hutchcroft54, M. Idzik28, P. Ilten58, R. Jacobsson40, A. Jaeger12, J.
Jalocha57, E. Jans43, A. Jawahery60, F. Jiang3, M. John57, D. Johnson40, C.R.
Jones49, C. Joram40, B. Jost40, N. Jurik61, S. Kandybei45, W. Kanso6, M.
Karacson40, J.M. Kariuki48, S. Karodia53, M. Kecke12, M. Kelsey61, I.R.
Kenyon47, M. Kenzie49, T. Ketel44, E. Khairullin35, B. Khanji21,40,i, C.
Khurewathanakul41, T. Kirn9, S. Klaver56, K. Klimaszewski29, S. Koliiev46, M.
Kolpin12, I. Komarov41, R.F. Koopman44, P. Koppenburg43, A. Kosmyntseva32, A.
Kozachuk33, M. Kozeiha5, L. Kravchuk34, K. Kreplin12, M. Kreps50, P.
Krokovny36,w, F. Kruse10, W. Krzemien29, W. Kucewicz27,l, M. Kucharczyk27, V.
Kudryavtsev36,w, A.K. Kuonen41, K. Kurek29, T. Kvaratskheliya32,40, D.
Lacarrere40, G. Lafferty56, A. Lai16, D. Lambert52, G. Lanfranchi19, C.
Langenbruch9, T. Latham50, C. Lazzeroni47, R. Le Gac6, J. van Leerdam43, J.-P.
Lees4, A. Leflat33,40, J. Lefrançois7, R. Lefèvre5, F. Lemaitre40, E. Lemos
Cid39, O. Leroy6, T. Lesiak27, B. Leverington12, Y. Li7, T. Likhomanenko35,67,
R. Lindner40, C. Linn40, F. Lionetto42, B. Liu16, X. Liu3, D. Loh50, I.
Longstaff53, J.H. Lopes2, D. Lucchesi23,o, M. Lucio Martinez39, H. Luo52, A.
Lupato23, E. Luppi17,g, O. Lupton57, A. Lusiani24, X. Lyu63, F. Machefert7, F.
Maciuc30, O. Maev31, K. Maguire56, S. Malde57, A. Malinin67, T. Maltsev36, G.
Manca7, G. Mancinelli6, P. Manning61, J. Maratas5,v, J.F. Marchand4, U.
Marconi15, C. Marin Benito38, P. Marino24,t, J. Marks12, G. Martellotti26, M.
Martin6, M. Martinelli41, D. Martinez Santos39, F. Martinez Vidal68, D.
Martins Tostes2, L.M. Massacrier7, A. Massafferri1, R. Matev40, A. Mathad50,
Z. Mathe40, C. Matteuzzi21, A. Mauri42, B. Maurin41, A. Mazurov47, M.
McCann55, J. McCarthy47, A. McNab56, R. McNulty13, B. Meadows59, F. Meier10,
M. Meissner12, D. Melnychuk29, M. Merk43, A. Merli22,q, E. Michielin23, D.A.
Milanes65, M.-N. Minard4, D.S. Mitzel12, A. Mogini8, J. Molina Rodriguez1,
I.A. Monroy65, S. Monteil5, M. Morandin23, P. Morawski28, A. Mordà6, M.J.
Morello24,t, J. Moron28, A.B. Morris52, R. Mountain61, F. 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, A.D. Nguyen41, 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. Oldeman49, C.J.G. Onderwater69, J.M. Otalora
Goicochea2, A. Otto40, P. Owen42, A. Oyanguren68, 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. Petrov67, M. Petruzzo22,q, E. Picatoste
Olloqui38, B. Pietrzyk4, M. Pikies27, D. Pinci26, A. Pistone20, A. Piucci12,
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, A. Pritchard54, C. Prouve48, V. Pugatch46, A. Puig Navarro41,
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 Alepuz68, V.
Renaudin7, S. Ricciardi51, S. Richards48, M. Rihl40, K. Rinnert54, V. Rives
Molina38, P. Robbe7,40, A.B. Rodrigues1, E. Rodrigues59, J.A. Rodriguez
Lopez65, 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 Valls68, J.J. Saborido Silva39, E. Sadykhov32, N.
Sagidova31, B. Saitta16,f, V. Salustino Guimaraes2, C. Sanchez Mayordomo68, 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. Schiller40, 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. Shevchenko67, A. Shires10, 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, 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. Stone61, B. Storaci42, S.
Stracka24,p, M. Straticiuc30, U. Straumann42, L. Sun59, W. Sutcliffe55, K.
Swientek28, V. Syropoulos44, M. Szczekowski29, T. Szumlak28, S. T’Jampens4, A.
Tayduganov6, T. Tekampe10, G. Tellarini17,g, F. Teubert40, E. Thomas40, J. van
Tilburg43, M.J. Tilley55, V. Tisserand4, M. Tobin41, S. Tolk49, L.
Tomassetti17,g, D. Tonelli40, S. Topp-Joergensen57, F. Toriello61, E.
Tournefier4, S. Tourneur41, K. Trabelsi41, M. Traill53, M.T. Tran41, M.
Tresch42, A. Trisovic40, A. Tsaregorodtsev6, P. Tsopelas43, A. Tully49, N.
Tuning43, A. Ukleja29, A. Ustyuzhanin35, U. Uwer12, C. Vacca16,f, V.
Vagnoni15,40, A. Valassi40, S. Valat40, G. Valenti15, A. Vallier7, R. Vazquez
Gomez19, P. Vazquez Regueiro39, S. Vecchi17, M. van Veghel43, J.J. Velthuis48,
M. Veltri18,r, G. Veneziano41, A. Venkateswaran61, M. Vernet5, M.
Vesterinen12, B. Viaud7, D. Vieira1, M. Vieites Diaz39, X. Vilasis-
Cardona38,m, V. Volkov33, A. Vollhardt42, B. Voneki40, A. Vorobyev31, V.
Vorobyev36,w, C. Voß66, J.A. de Vries43, C. Vázquez Sierra39, R. Waldi66, C.
Wallace50, R. Wallace13, J. Walsh24, J. Wang61, D.R. Ward49, H.M. Wark54, N.K.
Watson47, D. Websdale55, A. Weiden42, M. Whitehead40, J. Wicht50, G.
Wilkinson57,40, M. Wilkinson61, M. Williams40, M.P. Williams47, M. Williams58,
T. Williams47, F.F. Wilson51, J. Wimberley60, J. Wishahi10, W. Wislicki29, M.
Witek27, G. Wormser7, S.A. Wotton49, K. Wraight53, K. Wyllie40, Y. Xie64, Z.
Xing61, Z. Xu41, Z. Yang3, H. Yin64, J. Yu64, X. Yuan36,w, O. Yushchenko37,
K.A. Zarebski47, M. Zavertyaev11,c, L. Zhang3, Y. Zhang7, Y. Zhang63, A.
Zhelezov12, Y. Zheng63, A. Zhokhov32, X. Zhu3, V. Zhukov9, S. 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
64Institute of Particle Physics, Central China Normal University, Wuhan,
Hubei, China, associated to 3
65Departamento de Fisica , Universidad Nacional de Colombia, Bogota, Colombia,
associated to 8
66Institut für Physik, Universität Rostock, Rostock, Germany, associated to 12
67National Research Centre Kurchatov Institute, Moscow, Russia, associated to
32
68Instituto de Fisica Corpuscular (IFIC), Universitat de Valencia-CSIC,
Valencia, Spain, associated to 38
69Van 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