1Chair of Embedded Intelligence for Health Care & Wellbeing, University of
Augsburg, Germany
2 Institute of Computational Perception, Johannes Kepler University Linz,
Austria
3GLAM – Group on Language, Audio, & Music, Imperial College London, UK

# A Temporal-oriented Broadcast ResNet for COVID-19 Detection

###### Abstract

Detecting COVID-19 from audio signals, such as breathing and coughing, can be
used as a fast and efficient pre-testing method to reduce the virus
transmission. Due to the promising results of deep learning networks in
modelling time sequences, and since applications to rapidly identify COVID in-
the-wild should require low computational effort, we present a temporal-
oriented broadcasting residual learning method that achieves efficient
computation and high accuracy with a small model size. Based on the
EfficientNet architecture, our novel network, named Temporal-oriented ResNet
(TorNet), constitutes of a broadcasting learning block, i. e., the Alternating
Broadcast (AB) Block, which contains several Broadcast Residual Blocks (BC
ResBlocks) and a convolution layer. With the AB Block, the network obtains
useful audio-temporal features and higher level embeddings effectively with
much less computation than Recurrent Neural Networks (RNNs), typically used to
model temporal information. TorNet achieves 72.2% Unweighted Average Recall
(UAR) on the INTERPSEECH 2021 Computational Paralinguistics Challenge COVID-19
cough Sub-Challenge, by this showing competitive results with a higher
computational efficiency than other state-of-the-art alternatives.

Index Terms: SARS-CoV2 detection, deep neural network, efficient neural
network, efficient CNN, residual learning

##  1 Introduction

COVID-19 cases are still rising in several countries, indicating that the
pandemic is still a main health challenge for our world[1]. Although there are
rapid testing methods, their efficiency is often limited by the capacity of
the testing equipment. In addition, as their production depends on the
materials’ availability, limited resources might yield crowds that in turn,
paradoxically, increase the infection rates. Indeed, ubiquitous low-cost
methods for detecting COVID-19 are still being explored. In the realm of
Artificial Intelligence, Deep Neural Networks (DNNs) have been growing in
popularity in recent years, setting the state-of-art in a variety of tasks,
including COVID-19 detection from audio signals, e. g., patients’ breathing
and coughing [2, 3].

The temporal component is an essential characteristic of audio signals. Thus,
learning discriminated representations containing temporal information is
crucial to achieve a better classification network when working with audio [4,
2]. To make full use of temporal information, Recurrent Neural Networks (RNNs)
and variants with Long-Short Term Memory (LSTM) [5, 6, 7] have been
successfully developed. However, RNNs are computationally more intensive and
require more storage compared to a typical Convolutional Neural Network (CNN).
Previous works have also shown that transformers can exploit the temporal
properties of audio to obtain higher detection results than RNNs [8, 9, 10].
Still, over-parametrised transformer-based deep networks might be prone to
overfitting, and similar to CNNs, computationally inefficient.

The successful application of RNNs and transformers to audio data illustrates
the importance of temporal features for audio tasks. Nevertheless, the
complexity of these network structures, unlike CNNs, increases the
computational complexity and reduces the training stability. In the present
work, we propose a temporal broadcast residual convolution block, i. e., the
Alternating Broadcast Block (AB Block), in which we average the 2D features in
the frequency dimension to guide the network’s focus on the temporal features.
Inspired by the EfficientNet [11] architecture (made up of repeated blocks and
based on the residual learning), we introduce a new deep learning network
named Temporal-oriented ResNet (TorNet) that contains several AB Blocks to
make full use of the temporal information in the audio segments. Furthermore,
we also adopt Instance Normalisation [12] to assist the network to find the
relevant feature areas of the Mel-spectrogram, by this improving the
classification results. We evaluate the efficiency of TorNet on the detection
of COVID-19 from coughing signals, using the audio dataset from the
INTERSPEECH 2021 Computational Paralinguistics Challenge’s COVID-19 cough sub-
challenge [13]. For reproducibility purposes, the source code of our work is
freely available111https://github.com/EIHW/compare21_tornet_covid.

The remainder of our paper is organised as follows: We summarise the related
research in Section 2. Then, we present our network architecture and describe
the experimental settings in Sections 3 and 4, respectively. In Section 5, we
discuss the results. Finally, in Section 6, we conclude with a brief summary
and outline future directions.

##  2 Related Works

Data representations such as Mel-Spectrograms can be seen from two different
perspectives: either as an image, or as an audio sequence. This duality leads
to the use of a variety of DNN architectures typical of both Computer Vision
(CV) and the audio domain [14, 15].

On the one side, previous work has shown that with Mel Frequency Cepstral
Coefficients (MFCC) and log Mel-Spectrograms, 1D audio data can be transformed
into 2D matrices [16]. This makes it possible to directly apply CNNs,
typically from CV, and which have become the mainstream in Computer Audition.
In the task of COVID-19 detection, Chang et al. [10] studied the performance
of classical CNNs pretrained on the FluSense database, collected to track
influenza-related indicators, such as cough and sneezes [17]. Similarly,
Casanova et al. [18] employed transfer learning from pretrained audio neural
networks with different data augmentation techniques.

On the other side, as audio data is inherently a type of temporal sequence
[19], RNNs [7] and LSTM[20] have been fully adopted to handle the temporal
information in several tasks. For instance, Hassan et al. [21] and Pahar et
al. [22] evaluated the role of different audio features as input for LSTM-
based classification of COVID-19. Similarly, Yan et al. [6] introduced the
Spatial Attentive ConvLSTM-RNN (SACRNN), able to identify the most valuable
features through an embedded temporal attention. Various efforts have also
explored more efficient CNNs using residual network approaches and ensembles
on audio data [23, 24, 25]. In particular, Byeonggeun et al. [26] used a
residual broadcast block to retrieve temporal features by averaging the
frequency features. Finally, Zhang et al. [27] proposed a hierarchical
structure called pyramidal temporal pooling (PTP), which can retrieve temporal
information by stacking a global PTP layer on multiple local ones.

##  3 Proposed Method

In this section, we propose Temporal-Oriented ResNet (TorNet), a modified
version of the Broadcasting-residual network [26] tailored to audio data,
which we present for COVID-19 recognition. In addition, we also propose an
Alternating Broadcast Block (AB Block), which contains several Broadcast
Residual Blocks (BC ResBlock) [26] and combines the temporal information to
the whole feature map and a convolution layer for a better overview of the
features. Finally, we use Frequency-wise Instance Normalisation for better
domain generalisation [28].

###  3.1 Broadcast Residual Block

The original ResNet [29] block is described by $y=x+f(x)$, with $f(x)$ being
the residual function, and $x$ and $y$ denoting the input and output features,
respectively. Normally, $f$ utilises 2D-spatiotemporal features (i. e., 2D
convolutions). To emphasise temporal features, we exploit 1D-temporal features
in addition to 2D ones. To highlight the frequency convolution over all
blocks, an auxiliary 2D residual connection is added from 2D features. To
summarise, the BC-ResBlock can be presented as:

$y=x+f_{2}(x)+BC(f_{1}(avgpool(f_{2}(x)))).$ (1)

In Equation 1, the 2D feature part $f_{2}$ consists of a 3x1 frequency depth-
wise convolution followed by SubSpectral normalisation [30], which splits the
input frequency into multiple groups and normalises them separately. To obtain
frequency-based temporal features, we apply SubSpectral normalisation instead
of Batch Norm. Finally, 2D features are averaged over the frequency dimension.

$f_{1}$ is a combination between a 1x3 temporal convolution with Batch Norm
and Swish activation [31] followed by a 1x1 point-wise convolution using a
channel dropout rate of $p=0.5$. Thus, the broadcasting operation expands the
feature map in $\mathbb{R}^{1\times w}$to $\mathbb{R}^{h\times w}$.

A normal BC ResBlock (cf. left in Figure 1) remaps the temporal information to
the original feature map, so it has the same input and output dimensions.
Meanwhile, a transition block (indicating that the number of input channel and
output channels is different) is used, with the following modifications:

  1. 1.

When channels do not have the same size, we add a transition block with Batch
Norm and ReLU activation;

  2. 2.

There is no identity shortcut.

Figure 1: Left: Normal BC ResBlock. A ResNet structure which firstly
compresses the frequency dimension of the feature map by average pooling, and
then broadcasts the temporal feature to the original feature map. Right:
Transition BC Block. It contains two additional $1\times 1$ convolution layers
to change the channel number without the identity shortcut.

###  3.2 Alternating Broadcast Block

With the BC ResBlock, it is possible to turn the features into a higher
dimensionality while broadcasting the temporal information to the whole
feature map. As shown in Figure 2, we propose a flexible structure of the
Alternating Broadcast Block (AB Block), which mainly contains a set of BC
ResBlocks and a convolution layer. The AB Block can be easily widened or
deepened, even when facing a large amount of data, by simply adding a larger
number of Normal BC ResBlocks. Note that the first BC ResBlock must be a
Transition BC ResBlock when the number of input and output channels is
different.

As shown in Figure 1 (left), average pooling is used before temporal depth-
wise (DW) convolution, which yields information loss in the frequency
dimension (an inevitable side effect of using the BC ResBlock). In order to
reduce the impact of information loss, the last layer of the AB Block is set
to a convolution layer, followed by a Batch Norm layer and a ReLU activation
layer. The main task of the convolution layer is to capture the global
information of the temporal-based feature map, while retaining the local
information learnt in the previous layer and projecting them to the higher
dimensions of the original inputs. Thus, by using the proposed block, we can
achieve enhanced frequency-aware temporal 2D features.

To achieve a better domain generalisation, we apply Instance Normalisation
[12] (IN), an approach that normalises across each channel in each training
example. Since IN does not rely on batch information, its implementation is
kept the same for both the training and testing phases. We apply IN on the
frequency dimension as formulated below:

$Freq\\_IN(x)=\frac{x-\mu_{nf}}{\sqrt{\sigma_{nf}+\epsilon}},$ (2)

where

$\begin{split}&\mu_{nf}=\frac{1}{CT}\sum^{C}_{c=1}\sum^{T}_{t=1}x_{ncft},\\\
&\sigma^{2}_{nf}=\frac{1}{CT}\sum^{C}_{c=1}\sum^{T}_{t=1}(x_{ncft}-\mu_{nf}^{2}),\end{split}$
(3)

where $\mu_{nf},\sigma_{nf}\in\mathbb{R}^{N\times F}$ are mean and standard
deviation of the input feature $x\in\mathbb{R}^{N\times C\times F\times T}$,
in which $N,C,F,T$ denote the batch size, number of input channel, frequency
dimension, and time dimension. $\epsilon$ is a value added to the denominator
for numerical stability.

###  3.3 Temporal-oriented ResNet (TorNet)

Figure 2: The Alternating Broadcast Block (AB Block).

After exploring several choices and combinations, we design the Temporal-
oriented ResNet (TorNet) for the COVID-19 detection task as shown in Figure 3.
Details on the TorNet structure are given in Table 1. As shown in Figure 3,
TorNet contains four main stages. The first stage has a $3\times 3$
convolution layer with a $2\times 2$ max-pooling layer on the front to
downsample both the time and frequency dimensions. The second stage is a
typical residual block with two AB Blocks, where every AB Block will double
the channel while halving the frequency dimension to get a higher-level
embedding. In the residual shortcut, we added a batch norm layer and used
maxpooling to control the size of the receptive field. This is followed by an
Instance Normalisation layer between stage 2 and stage 3. Stage 3 shares the
same structure as stage 2 with minor differences, i. e., the number of
channels is doubled, and the dimension of the feature map does not change.
After the second IN layer, the feature map is turned into a 3D tensor
$[batch\\_size,time,out\\_channel\times N\\_mel]$. Finally, two fully
connected layers are added as classification layers.

Figure 3: TorNet for COVID-19 detection.

##  4 Experiments

###  4.1 Dataset

In the INTERPSEECH 2021 Computational Paralinguistics Challenge [13], the
COVID-19 cough sub-challenge (CCS) was based on a subset of the crowd-sourced
Cambridge COVID-19 Sound database [32], whose goal is promoting the developing
of systems able to diagnose COVID-19 from audio data. The CCS database
consists of $929$ cough recordings ($1.63$ hours) from $397$ participants
presenting either a positive or negative COVID-19 test. Participants were
asked to provide one to three forced coughs in each
recording222https://www.COVID-19-sounds.org/; retrieved 12 March 2022. All
recordings in the CCS database were resampled and converted to $16$ kHz and
mono/16 bits.

The official training, validation, and test sets used in the ComparE challenge
are used in all our experiments.

Table 1: First 3 stages in TorNet. Each row is a sequence of one module with
an input shape of $channel\times frequency\times time$.

# | Input | Operator | Stride | Output
---|---|---|---|---
Stage 1 |  1 $\times$ 40 $\times$ 512 | conv2d 3x3 | 1 | 32
32 $\times$ 40 $\times$ 512 | maxpool 2x2 | 2 | 32
Stage 2 |  32 $\times$ 20 $\times$ 256 | AB Block |  (2, 1) | 64
64 $\times$ 10 $\times$ 256 | AB Block |  (2, 1) | 128
|  128 $\times$ 5 $\times$ 256 | IN | - | 128
Stage3 |  128 $\times$ 5 $\times$ 256 | AB Block | 1 | 256
256 $\times$ 5 $\times$ 256 | AB Block | 1 | 512
|  512 $\times$ 5 $\times$ 256 | IN | - | 512

Table 2: Unweighted Average Recall (UAR), networks’ parameters of the overall
results and 95% bootstrap confidence intervals (CI) using 1000 samples (with
replacement). The best result of our experiments is marked in bold, and the
second best one is underlined.

Method | # Param(M) | UAR (%) | CI on Test(%)
---|---|---|---
End2You[13] | - | 64.7 | 56.2 - 73.5
Fusion[13] (official baseline) | - | 73.9 | 66.0 - 82.6
CNN14 [18] | 79.67 | 75.9 | -
The Vision Transformer (ViT)[33] | - | 72.0 | -
ResNet-10 (baseline) | 5.12 | 66.9 | 57.7 - 71.8
ResNet-10+LSTM | 6.17 | 66.7 | 64.2 - 81.6
ResNet-10+LSTM+attention | 6.23 | 70.5 | 63.9 - 80.1
ResNet-10+Transformer | 58.90 | 68.0 | 63.3 - 79.6
TorNet (AB Block without last conv) | 1.32 | 65.5 | 60.1 - 77.6
TorNet (only Transition Block) | 4.09 | 69.4 | 58.5 - 72.6
TorNet without InstanceNorm | 4.46 | 70.2 | 59.4 - 72.8
TorNet with InstanceNorm | 4.46 | 72.2 | 71.5 - 88.6

###  4.2 Experiment settings

For data pre-processing, we standardise the length of the audio data to 10
seconds. The shorter samples are repeated until they match the target length.
As input features, we use 40-dimensional log Mel-Spectrograms with a 64 ms
window length and a 16 ms frame shift. We also extract deltas and delta-delta
of log Mel-Spectrograms and concatenate them as input features. At last, the
size of features for TorNet is $[batch\\_size,3,40,512]$. For all models, we
use the Adam optimiser with an epsilon value of $10^{-8}$, a mini-batch size
of $16$, and a learning rate of $10^{-5}$. As indicated by [30], the sub-bands
of SSN in AB Blocks were all set to $5$ and the dropout rate was always
$p=0.1$ except for the last layer, where it was set to $p=0.5$. We also tried
data augmentation (mixup, Spec augmentation) methods, but there was no
noticeable performance gain in our task, thus omitting them for brevity. All
the models were developed on Pytorch 1.8.1 and trained on a single Nvidia RTX
3090 GPU.

###  4.3 Proposed Experiments

To verify the efficiency of the proposed TorNet, as well as the effectiveness
of the temporal features, we developed four additional ResNet-based methods
for comparison. These four methods are as follows:

  * •

ResNet-10: has an identical structure as TorNet but uses a convolution layer
and maxpooling to control the size of the feature map. It is used as a
baseline model.

  * •

ResNet-10 + LSTM: introduces a layer of a standard LSTM network at the output
of the ResNet-10.

  * •

ResNet-10 + LSTM + Attention: adds a 4-head multihead attention module to
extract more centralised temporal-frequency feature maps.

  * •

ResNet-10 + Transformer: adds 2 transformer encoder layers for locating and
re-extracting the most relevant features of the audio segments.

##  5 Results and Discussion

Our experimental results obtained on the binary task of COVID-19 detection are
presented in Table 2. The upper part of Table 2 displays the results from
previous works on the CSS dataset. The middle part shows the results obtained
from the four additional ResNet-based methods presented for comparison.
Finally, the results for the series of experiments with same network
hyperparameters and different modules are given in the lower part. Unweighted
Average Recall (UAR) is reported as the evaluation metric. For each method,
the results on the test set are obtained by using the model achieving the
highest UAR on the validation set.

Our proposed TorNet, based on the combination of AB Block and residual
learning’s results, reached up to 72.2% UAR. The results show that all
experiment results on the Tornet outperform the official baseline (End2You
64.7%) but still lag behind other approaches. Unlike the herein presented one,
[18] achieved 75.9% UAR based on a large-scale transfer learning model. Their
CNN14 model is pre-trained on Audioset, which means a longer training time and
higher computational effort. Indeed, the parameters of CNN14 (79.67 millions)
are almost 18 times more than the number of parameters compared to TorNet
(4.46 millions) – thus showing that TorNet has a higher computational
efficiency. Similarly, the baseline fusion framework for the CCS Sub-Challenge
fuses multiple best models to obtain the final results (73.9% UAR), which also
results in a far higher computational complexity than our TorNet. Overall,
this shows that TorNet can achieve competitive performance without pre-
training or fusion while also using far lower computational resources.

In comparison to the official ‘standard’ baseline (End2You), our baseline
results show that the ResNet structure still has good robustness for audio
data (ResNet-10 66.9%). Meanwhile, we added LSTM and transformer structures
for extracting temporal information after ResNet-10. The results show that the
extraction of temporal information can improve the final UAR results, as shown
by the combination of ResNet + LSTM (cf. 70.5%, in the bold, in the middle
part of Table 2); by this, outperforming all other ResNet-based methods.

These experiments demonstrate the importance of utilising temporal context.
However, these architectures dramatically increase the computational
complexity while still not reaching the performance of TorNet. This
demonstrates how accounting for the temporal nature of audio inside the
intermediate layers of a DNN –like TorNet does– is superior to doing it only
towards the deeper part of the network.

Since our goal is to investigate to which extent it is possible to model
temporal information while improving computational efficiency with DNNs, we
also set up four comparison experiments based on TorNet. In these, we keep all
training parameters consistent in order to assess the impact of different
modules, i. e., the use of a convolution layer in the AB Block, the Normal BC
ResBlock, and Instance Normalisation on the overall performance of TorNet.

The lower part of table 2 contains an ablation study of the components
introduced in this work. TorNet without the last convolution layer in the AB
block achieves only 65.5% UAR, while when convolution layers are introduced,
there is a performance improvement of nearly 5.0% (cf. 70.2%, underlined in
the lower part of Table 2). This is because in each AB Block, the BC ResBlock
has the ability to broadcast the temporal features to the original feature
map, but loses a portion of the frequency features. By introducing an extra
convolution layer, we eliminate the influence that this loss of granular
details entails, thus obtaining a better overview for the feature map, which
results in a sizable performance increase. At the same time, the IN layer is
introduced in the frequency dimension for better domain generalisation,
leading to a performance improvement of approximately 2% in the same training
environment: from 70.2% (without IN) to 72.2% (with IN).

##  6 Conclusion

In this work, we proposed an AB Block that can efficiently exploit the
temporal information in audio sequences. It contains multiple BC ResBlock as
well as a convolution layer to capture the temporal-enhanced feature. Based on
the AB Block with residual learning, we proposed a flexible, lightweight, and
time-oriented network – TorNet. TorNet has a typical ResNet structure, but we
replace the convolution module with the AB Block. Competitive results
highlight the high computational efficiency and robustness of TorNet, a
promising architecture that offers new insights for the detection of COVID-19.

Future work could be targeted towards the application of TorNet in other
domains, such as speech emotion recognition or acoustic scene classification.

##  7 References

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LHCb-PAPER-2017-050 [LHCb-PAPER-2017-050] |  LHCb-PAPER-2017-049 [LHCb-PAPER-2017-049] |  LHCb-PAPER-2017-048 [LHCb-PAPER-2017-048] |  LHCb-PAPER-2017-047 [LHCb-PAPER-2017-047] |  LHCb-PAPER-2017-046 [LHCb-PAPER-2017-046]
LHCb-PAPER-2017-045 [LHCb-PAPER-2017-045] |  LHCb-PAPER-2017-044 [LHCb-PAPER-2017-044] |  LHCb-PAPER-2017-043 [LHCb-PAPER-2017-043] |  LHCb-PAPER-2017-042 [LHCb-PAPER-2017-042] |  LHCb-PAPER-2017-041 [LHCb-PAPER-2017-041]
LHCb-PAPER-2017-040 [LHCb-PAPER-2017-040] |  LHCb-PAPER-2017-039 [LHCb-PAPER-2017-039] |  LHCb-PAPER-2017-038 [LHCb-PAPER-2017-038] |  LHCb-PAPER-2017-037 [LHCb-PAPER-2017-037] |  LHCb-PAPER-2017-036 [LHCb-PAPER-2017-036]
LHCb-PAPER-2017-035 [LHCb-PAPER-2017-035] |  LHCb-PAPER-2017-034 [LHCb-PAPER-2017-034] |  LHCb-PAPER-2017-033 [LHCb-PAPER-2017-033] |  LHCb-PAPER-2017-032 [LHCb-PAPER-2017-032] |  LHCb-PAPER-2017-031 [LHCb-PAPER-2017-031]
LHCb-PAPER-2017-030 [LHCb-PAPER-2017-030] |  LHCb-PAPER-2017-029 [LHCb-PAPER-2017-029] |  LHCb-PAPER-2017-028 [LHCb-PAPER-2017-028] |  LHCb-PAPER-2017-027 [LHCb-PAPER-2017-027] |  LHCb-PAPER-2017-026 [LHCb-PAPER-2017-026]
LHCb-PAPER-2017-025 [LHCb-PAPER-2017-025] |  LHCb-PAPER-2017-024 [LHCb-PAPER-2017-024] |  LHCb-PAPER-2017-023 [LHCb-PAPER-2017-023] |  LHCb-PAPER-2017-022 [LHCb-PAPER-2017-022] |  LHCb-PAPER-2017-021 [LHCb-PAPER-2017-021]
LHCb-PAPER-2017-020 [LHCb-PAPER-2017-020] |  LHCb-PAPER-2017-019 [LHCb-PAPER-2017-019] |  LHCb-PAPER-2017-018 [LHCb-PAPER-2017-018] |  LHCb-PAPER-2017-017 [LHCb-PAPER-2017-017] |  LHCb-PAPER-2017-016 [LHCb-PAPER-2017-016]
LHCb-PAPER-2017-015 [LHCb-PAPER-2017-015] |  LHCb-PAPER-2017-014 [LHCb-PAPER-2017-014] |  LHCb-PAPER-2017-013 [LHCb-PAPER-2017-013] |  LHCb-PAPER-2017-012 [LHCb-PAPER-2017-012] |  LHCb-PAPER-2017-011 [LHCb-PAPER-2017-011]
LHCb-PAPER-2017-010 [LHCb-PAPER-2017-010] |  LHCb-PAPER-2017-009 [LHCb-PAPER-2017-009] |  LHCb-PAPER-2017-008 [LHCb-PAPER-2017-008] |  LHCb-PAPER-2017-007 [LHCb-PAPER-2017-007] |  LHCb-PAPER-2017-006 [LHCb-PAPER-2017-006]
LHCb-PAPER-2017-005 [LHCb-PAPER-2017-005] |  LHCb-PAPER-2017-004 [LHCb-PAPER-2017-004] |  LHCb-PAPER-2017-003 [LHCb-PAPER-2017-003] |  LHCb-PAPER-2017-002 [LHCb-PAPER-2017-002] |  LHCb-PAPER-2017-001 [LHCb-PAPER-2017-001]
LHCb-PAPER-2016-065 [LHCb-PAPER-2016-065] |  LHCb-PAPER-2016-064 [LHCb-PAPER-2016-064] |  LHCb-PAPER-2016-063 [LHCb-PAPER-2016-063] |  LHCb-PAPER-2016-062 [LHCb-PAPER-2016-062] |  LHCb-PAPER-2016-061 [LHCb-PAPER-2016-061]
LHCb-PAPER-2016-060 [LHCb-PAPER-2016-060] |  LHCb-PAPER-2016-059 [LHCb-PAPER-2016-059] |  LHCb-PAPER-2016-058 [LHCb-PAPER-2016-058] |  LHCb-PAPER-2016-057 [LHCb-PAPER-2016-057] |  LHCb-PAPER-2016-056 [LHCb-PAPER-2016-056]
LHCb-PAPER-2016-055 [LHCb-PAPER-2016-055] |  LHCb-PAPER-2016-054 [LHCb-PAPER-2016-054] |  LHCb-PAPER-2016-053 [LHCb-PAPER-2016-053] |  LHCb-PAPER-2016-052 [LHCb-PAPER-2016-052] |  LHCb-PAPER-2016-051 [LHCb-PAPER-2016-051]
LHCb-PAPER-2016-050 [LHCb-PAPER-2016-050] |  LHCb-PAPER-2016-049 [LHCb-PAPER-2016-049] |  LHCb-PAPER-2016-048 [LHCb-PAPER-2016-048] |  LHCb-PAPER-2016-047 [LHCb-PAPER-2016-047] |  LHCb-PAPER-2016-046 [LHCb-PAPER-2016-046]
LHCb-PAPER-2016-045 [LHCb-PAPER-2016-045] |  LHCb-PAPER-2016-044 [LHCb-PAPER-2016-044] |  LHCb-PAPER-2016-043 [LHCb-PAPER-2016-043] |  LHCb-PAPER-2016-042 [LHCb-PAPER-2016-042] |  LHCb-PAPER-2016-041 [LHCb-PAPER-2016-041]
LHCb-PAPER-2016-040 [LHCb-PAPER-2016-040] |  LHCb-PAPER-2016-039 [LHCb-PAPER-2016-039] |  LHCb-PAPER-2016-038 [LHCb-PAPER-2016-038] |  LHCb-PAPER-2016-037 [LHCb-PAPER-2016-037] |  LHCb-PAPER-2016-036 [LHCb-PAPER-2016-036]
LHCb-PAPER-2016-035 [LHCb-PAPER-2016-035] |  LHCb-PAPER-2016-034 [LHCb-PAPER-2016-034] |  LHCb-PAPER-2016-033 [LHCb-PAPER-2016-033] |  LHCb-PAPER-2016-032 [LHCb-PAPER-2016-032] |  LHCb-PAPER-2016-031 [LHCb-PAPER-2016-031]
LHCb-PAPER-2016-030 [LHCb-PAPER-2016-030] |  LHCb-PAPER-2016-029 [LHCb-PAPER-2016-029] |  LHCb-PAPER-2016-028 [LHCb-PAPER-2016-028] |  LHCb-PAPER-2016-027 [LHCb-PAPER-2016-027] |  LHCb-PAPER-2016-026 [LHCb-PAPER-2016-026]
LHCb-PAPER-2016-025 [LHCb-PAPER-2016-025] |  LHCb-PAPER-2016-024 [LHCb-PAPER-2016-024] |  LHCb-PAPER-2016-023 [LHCb-PAPER-2016-023] |  LHCb-PAPER-2016-022 [LHCb-PAPER-2016-022] |  LHCb-PAPER-2016-021 [LHCb-PAPER-2016-021]
LHCb-PAPER-2016-020 [LHCb-PAPER-2016-020] |  LHCb-PAPER-2016-019 [LHCb-PAPER-2016-019] |  LHCb-PAPER-2016-018 [LHCb-PAPER-2016-018] |  LHCb-PAPER-2016-017 [LHCb-PAPER-2016-017] |  LHCb-PAPER-2016-016 [LHCb-PAPER-2016-016]
LHCb-PAPER-2016-015 [LHCb-PAPER-2016-015] |  LHCb-PAPER-2016-014 [LHCb-PAPER-2016-014] |  LHCb-PAPER-2016-013 [LHCb-PAPER-2016-013] |  LHCb-PAPER-2016-012 [LHCb-PAPER-2016-012] |  LHCb-PAPER-2016-011 [LHCb-PAPER-2016-011]
LHCb-PAPER-2016-010 [LHCb-PAPER-2016-010] |  LHCb-PAPER-2016-009 [LHCb-PAPER-2016-009] |  LHCb-PAPER-2016-008 [LHCb-PAPER-2016-008] |  LHCb-PAPER-2016-007 [LHCb-PAPER-2016-007] |  LHCb-PAPER-2016-006 [LHCb-PAPER-2016-006]
LHCb-PAPER-2016-005 [LHCb-PAPER-2016-005] |  LHCb-PAPER-2016-004 [LHCb-PAPER-2016-004] |  LHCb-PAPER-2016-003 [LHCb-PAPER-2016-003] |  LHCb-PAPER-2016-002 [LHCb-PAPER-2016-002] |  LHCb-PAPER-2016-001 [LHCb-PAPER-2016-001]
LHCb-PAPER-2015-060 [LHCb-PAPER-2015-060] |  LHCb-PAPER-2015-059 [LHCb-PAPER-2015-059] |  LHCb-PAPER-2015-058 [LHCb-PAPER-2015-058] |  LHCb-PAPER-2015-057 [LHCb-PAPER-2015-057] |  LHCb-PAPER-2015-056 [LHCb-PAPER-2015-056]
LHCb-PAPER-2015-055 [LHCb-PAPER-2015-055] |  LHCb-PAPER-2015-054 [LHCb-PAPER-2015-054] |  LHCb-PAPER-2015-053 [LHCb-PAPER-2015-053] |  LHCb-PAPER-2015-052 [LHCb-PAPER-2015-052] |  LHCb-PAPER-2015-051 [LHCb-PAPER-2015-051]
LHCb-PAPER-2015-050 [LHCb-PAPER-2015-050] |  LHCb-PAPER-2015-049 [LHCb-PAPER-2015-049] |  LHCb-PAPER-2015-048 [LHCb-PAPER-2015-048] |  LHCb-PAPER-2015-047 [LHCb-PAPER-2015-047] |  LHCb-PAPER-2015-046 [LHCb-PAPER-2015-046]
LHCb-PAPER-2015-045 [LHCb-PAPER-2015-045] |  LHCb-PAPER-2015-044 [LHCb-PAPER-2015-044] |  LHCb-PAPER-2015-043 [LHCb-PAPER-2015-043] |  LHCb-PAPER-2015-042 [LHCb-PAPER-2015-042] |  LHCb-PAPER-2015-041 [LHCb-PAPER-2015-041]
LHCb-PAPER-2015-040 [LHCb-PAPER-2015-040] |  LHCb-PAPER-2015-039 [LHCb-PAPER-2015-039] |  LHCb-PAPER-2015-038 [LHCb-PAPER-2015-038] |  LHCb-PAPER-2015-037 [LHCb-PAPER-2015-037] |  LHCb-PAPER-2015-036 [LHCb-PAPER-2015-036]
LHCb-PAPER-2015-035 [LHCb-PAPER-2015-035] |  LHCb-PAPER-2015-034 [LHCb-PAPER-2015-034] |  LHCb-PAPER-2015-033 [LHCb-PAPER-2015-033] |  LHCb-PAPER-2015-032 [LHCb-PAPER-2015-032] |  LHCb-PAPER-2015-031 [LHCb-PAPER-2015-031]
LHCb-PAPER-2015-030 [LHCb-PAPER-2015-030] |  LHCb-PAPER-2015-029 [LHCb-PAPER-2015-029] |  LHCb-PAPER-2015-028 [LHCb-PAPER-2015-028] |  LHCb-PAPER-2015-027 [LHCb-PAPER-2015-027] |  LHCb-PAPER-2015-026 [LHCb-PAPER-2015-026]
LHCb-PAPER-2015-025 [LHCb-PAPER-2015-025] |  LHCb-PAPER-2015-024 [LHCb-PAPER-2015-024] |  LHCb-PAPER-2015-023 [LHCb-PAPER-2015-023] |  LHCb-PAPER-2015-022 [LHCb-PAPER-2015-022] |  LHCb-PAPER-2015-021 [LHCb-PAPER-2015-021]
LHCb-PAPER-2015-020 [LHCb-PAPER-2015-020] |  LHCb-PAPER-2015-019 [LHCb-PAPER-2015-019] |  LHCb-PAPER-2015-018 [LHCb-PAPER-2015-018] |  LHCb-PAPER-2015-017 [LHCb-PAPER-2015-017] |  LHCb-PAPER-2015-016 [LHCb-PAPER-2015-016]
LHCb-PAPER-2015-015 [LHCb-PAPER-2015-015] |  LHCb-PAPER-2015-014 [LHCb-PAPER-2015-014] |  LHCb-PAPER-2015-013 [LHCb-PAPER-2015-013] |  LHCb-PAPER-2015-012 [LHCb-PAPER-2015-012] |  LHCb-PAPER-2015-011 [LHCb-PAPER-2015-011]
LHCb-PAPER-2015-010 [LHCb-PAPER-2015-010] |  LHCb-PAPER-2015-009 [LHCb-PAPER-2015-009] |  LHCb-PAPER-2015-008 [LHCb-PAPER-2015-008] |  LHCb-PAPER-2015-007 [LHCb-PAPER-2015-007] |  LHCb-PAPER-2015-006 [LHCb-PAPER-2015-006]
LHCb-PAPER-2015-005 [LHCb-PAPER-2015-005] |  LHCb-PAPER-2015-004 [LHCb-PAPER-2015-004] |  LHCb-PAPER-2015-003 [LHCb-PAPER-2015-003] |  LHCb-PAPER-2015-002 [LHCb-PAPER-2015-002] |  LHCb-PAPER-2015-001 [LHCb-PAPER-2015-001]
LHCb-PAPER-2014-070 [LHCb-PAPER-2014-070] |  LHCb-PAPER-2014-069 [LHCb-PAPER-2014-069] |  LHCb-PAPER-2014-068 [LHCb-PAPER-2014-068] |  LHCb-PAPER-2014-067 [LHCb-PAPER-2014-067] |  LHCb-PAPER-2014-066 [LHCb-PAPER-2014-066]
LHCb-PAPER-2014-065 [LHCb-PAPER-2014-065] |  LHCb-PAPER-2014-064 [LHCb-PAPER-2014-064] |  LHCb-PAPER-2014-063 [LHCb-PAPER-2014-063] |  LHCb-PAPER-2014-062 [LHCb-PAPER-2014-062] |  LHCb-PAPER-2014-061 [LHCb-PAPER-2014-061]
LHCb-PAPER-2014-060 [LHCb-PAPER-2014-060] |  LHCb-PAPER-2014-059 [LHCb-PAPER-2014-059] |  LHCb-PAPER-2014-058 [LHCb-PAPER-2014-058] |  LHCb-PAPER-2014-057 [LHCb-PAPER-2014-057] |  LHCb-PAPER-2014-056 [LHCb-PAPER-2014-056]
LHCb-PAPER-2014-055 [LHCb-PAPER-2014-055] |  LHCb-PAPER-2014-054 [LHCb-PAPER-2014-054] |  LHCb-PAPER-2014-053 [LHCb-PAPER-2014-053] |  LHCb-PAPER-2014-052 [LHCb-PAPER-2014-052] |  LHCb-PAPER-2014-051 [LHCb-PAPER-2014-051]
LHCb-PAPER-2014-050 [LHCb-PAPER-2014-050] |  LHCb-PAPER-2014-049 [LHCb-PAPER-2014-049] |  LHCb-PAPER-2014-048 [LHCb-PAPER-2014-048] |  LHCb-PAPER-2014-047 [LHCb-PAPER-2014-047] |  LHCb-PAPER-2014-046 [LHCb-PAPER-2014-046]
LHCb-PAPER-2014-045 [LHCb-PAPER-2014-045] |  LHCb-PAPER-2014-044 [LHCb-PAPER-2014-044] |  LHCb-PAPER-2014-043 [LHCb-PAPER-2014-043] |  LHCb-PAPER-2014-042 [LHCb-PAPER-2014-042] |  LHCb-PAPER-2014-041 [LHCb-PAPER-2014-041]
LHCb-PAPER-2014-040 [LHCb-PAPER-2014-040] |  LHCb-PAPER-2014-039 [LHCb-PAPER-2014-039] |  LHCb-PAPER-2014-038 [LHCb-PAPER-2014-038] |  LHCb-PAPER-2014-037 [LHCb-PAPER-2014-037] |  LHCb-PAPER-2014-036 [LHCb-PAPER-2014-036]
LHCb-PAPER-2014-035 [LHCb-PAPER-2014-035] |  LHCb-PAPER-2014-034 [LHCb-PAPER-2014-034] |  LHCb-PAPER-2014-033 [LHCb-PAPER-2014-033] |  LHCb-PAPER-2014-032 [LHCb-PAPER-2014-032] |  LHCb-PAPER-2014-031 [LHCb-PAPER-2014-031]
LHCb-PAPER-2014-030 [LHCb-PAPER-2014-030] |  LHCb-PAPER-2014-029 [LHCb-PAPER-2014-029] |  LHCb-PAPER-2014-028 [LHCb-PAPER-2014-028] |  LHCb-PAPER-2014-027 [LHCb-PAPER-2014-027] |  LHCb-PAPER-2014-026 [LHCb-PAPER-2014-026]
LHCb-PAPER-2014-025 [LHCb-PAPER-2014-025] |  LHCb-PAPER-2014-024 [LHCb-PAPER-2014-024] |  LHCb-PAPER-2014-023 [LHCb-PAPER-2014-023] |  LHCb-PAPER-2014-022 [LHCb-PAPER-2014-022] |  LHCb-PAPER-2014-021 [LHCb-PAPER-2014-021]
LHCb-PAPER-2014-020 [LHCb-PAPER-2014-020] |  LHCb-PAPER-2014-019 [LHCb-PAPER-2014-019] |  LHCb-PAPER-2014-018 [LHCb-PAPER-2014-018] |  LHCb-PAPER-2014-017 [LHCb-PAPER-2014-017] |  LHCb-PAPER-2014-016 [LHCb-PAPER-2014-016]
LHCb-PAPER-2014-015 [LHCb-PAPER-2014-015] |  LHCb-PAPER-2014-014 [LHCb-PAPER-2014-014] |  LHCb-PAPER-2014-013 [LHCb-PAPER-2014-013] |  LHCb-PAPER-2014-012 [LHCb-PAPER-2014-012] |  LHCb-PAPER-2014-011 [LHCb-PAPER-2014-011]
LHCb-PAPER-2014-010 [LHCb-PAPER-2014-010] |  LHCb-PAPER-2014-009 [LHCb-PAPER-2014-009] |  LHCb-PAPER-2014-008 [LHCb-PAPER-2014-008] |  LHCb-PAPER-2014-007 [LHCb-PAPER-2014-007] |  LHCb-PAPER-2014-006 [LHCb-PAPER-2014-006]
LHCb-PAPER-2014-005 [LHCb-PAPER-2014-005] |  LHCb-PAPER-2014-004 [LHCb-PAPER-2014-004] |  LHCb-PAPER-2014-003 [LHCb-PAPER-2014-003] |  LHCb-PAPER-2014-002 [LHCb-PAPER-2014-002] |  LHCb-PAPER-2014-001 [LHCb-PAPER-2014-001]
LHCb-PAPER-2013-070 [LHCb-PAPER-2013-070] |  LHCb-PAPER-2013-069 [LHCb-PAPER-2013-069] |  LHCb-PAPER-2013-068 [LHCb-PAPER-2013-068] |  LHCb-PAPER-2013-067 [LHCb-PAPER-2013-067] |  LHCb-PAPER-2013-066 [LHCb-PAPER-2013-066]
LHCb-PAPER-2013-065 [LHCb-PAPER-2013-065] |  LHCb-PAPER-2013-064 [LHCb-PAPER-2013-064] |  LHCb-PAPER-2013-063 [LHCb-PAPER-2013-063] |  LHCb-PAPER-2013-062 [LHCb-PAPER-2013-062] |  LHCb-PAPER-2013-061 [LHCb-PAPER-2013-061]
LHCb-PAPER-2013-060 [LHCb-PAPER-2013-060] |  LHCb-PAPER-2013-059 [LHCb-PAPER-2013-059] |  LHCb-PAPER-2013-058 [LHCb-PAPER-2013-058] |  LHCb-PAPER-2013-057 [LHCb-PAPER-2013-057] |  LHCb-PAPER-2013-056 [LHCb-PAPER-2013-056]
LHCb-PAPER-2013-055 [LHCb-PAPER-2013-055] |  LHCb-PAPER-2013-054 [LHCb-PAPER-2013-054] |  LHCb-PAPER-2013-053 [LHCb-PAPER-2013-053] |  LHCb-PAPER-2013-052 [LHCb-PAPER-2013-052] |  LHCb-PAPER-2013-051 [LHCb-PAPER-2013-051]
LHCb-PAPER-2013-050 [LHCb-PAPER-2013-050] |  LHCb-PAPER-2013-049 [LHCb-PAPER-2013-049] |  LHCb-PAPER-2013-048 [LHCb-PAPER-2013-048] |  LHCb-PAPER-2013-047 [LHCb-PAPER-2013-047] |  LHCb-PAPER-2013-046 [LHCb-PAPER-2013-046]
LHCb-PAPER-2013-045 [LHCb-PAPER-2013-045] |  LHCb-PAPER-2013-044 [LHCb-PAPER-2013-044] |  LHCb-PAPER-2013-043 [LHCb-PAPER-2013-043] |  LHCb-PAPER-2013-042 [LHCb-PAPER-2013-042] |  LHCb-PAPER-2013-041 [LHCb-PAPER-2013-041]
LHCb-PAPER-2013-040 [LHCb-PAPER-2013-040] |  LHCb-PAPER-2013-039 [LHCb-PAPER-2013-039] |  LHCb-PAPER-2013-038 [LHCb-PAPER-2013-038] |  LHCb-PAPER-2013-037 [LHCb-PAPER-2013-037] |  LHCb-PAPER-2013-036 [LHCb-PAPER-2013-036]
LHCb-PAPER-2013-035 [LHCb-PAPER-2013-035] |  LHCb-PAPER-2013-034 [LHCb-PAPER-2013-034] |  LHCb-PAPER-2013-033 [LHCb-PAPER-2013-033] |  LHCb-PAPER-2013-032 [LHCb-PAPER-2013-032] |  LHCb-PAPER-2013-031 [LHCb-PAPER-2013-031]
LHCb-PAPER-2013-030 [LHCb-PAPER-2013-030] |  LHCb-PAPER-2013-029 [LHCb-PAPER-2013-029] |  LHCb-PAPER-2013-028 [LHCb-PAPER-2013-028] |  LHCb-PAPER-2013-027 [LHCb-PAPER-2013-027] |  LHCb-PAPER-2013-026 [LHCb-PAPER-2013-026]
LHCb-PAPER-2013-025 [LHCb-PAPER-2013-025] |  LHCb-PAPER-2013-024 [LHCb-PAPER-2013-024] |  LHCb-PAPER-2013-023 [LHCb-PAPER-2013-023] |  LHCb-PAPER-2013-022 [LHCb-PAPER-2013-022] |  LHCb-PAPER-2013-021 [LHCb-PAPER-2013-021]
LHCb-PAPER-2013-020 [LHCb-PAPER-2013-020] |  LHCb-PAPER-2013-019 [LHCb-PAPER-2013-019] |  LHCb-PAPER-2013-018 [LHCb-PAPER-2013-018] |  LHCb-PAPER-2013-017 [LHCb-PAPER-2013-017] |  LHCb-PAPER-2013-016 [LHCb-PAPER-2013-016]
LHCb-PAPER-2013-015 [LHCb-PAPER-2013-015] |  LHCb-PAPER-2013-014 [LHCb-PAPER-2013-014] |  LHCb-PAPER-2013-013 [LHCb-PAPER-2013-013] |  LHCb-PAPER-2013-012 [LHCb-PAPER-2013-012] |  LHCb-PAPER-2013-011 [LHCb-PAPER-2013-011]
LHCb-PAPER-2013-010 [LHCb-PAPER-2013-010] |  LHCb-PAPER-2013-009 [LHCb-PAPER-2013-009] |  LHCb-PAPER-2013-008 [LHCb-PAPER-2013-008] |  LHCb-PAPER-2013-007 [LHCb-PAPER-2013-007] |  LHCb-PAPER-2013-006 [LHCb-PAPER-2013-006]
LHCb-PAPER-2013-005 [LHCb-PAPER-2013-005] |  LHCb-PAPER-2013-004 [LHCb-PAPER-2013-004] |  LHCb-PAPER-2013-003 [LHCb-PAPER-2013-003] |  LHCb-PAPER-2013-002 [LHCb-PAPER-2013-002] |  LHCb-PAPER-2013-001 [LHCb-PAPER-2013-001]
LHCb-PAPER-2012-057 [LHCb-PAPER-2012-057] |  LHCb-PAPER-2012-056 [LHCb-PAPER-2012-056] |  |  |
LHCb-PAPER-2012-055 [LHCb-PAPER-2012-055] |  LHCb-PAPER-2012-054 [LHCb-PAPER-2012-054] |  LHCb-PAPER-2012-053 [LHCb-PAPER-2012-053] |  LHCb-PAPER-2012-052 [LHCb-PAPER-2012-052] |  LHCb-PAPER-2012-051 [LHCb-PAPER-2012-051]
LHCb-PAPER-2012-050 [LHCb-PAPER-2012-050] |  LHCb-PAPER-2012-049 [LHCb-PAPER-2012-049] |  LHCb-PAPER-2012-048 [LHCb-PAPER-2012-048] |  LHCb-PAPER-2012-047 [LHCb-PAPER-2012-047] |  LHCb-PAPER-2012-046 [LHCb-PAPER-2012-046]
LHCb-PAPER-2012-045 [LHCb-PAPER-2012-045] |  LHCb-PAPER-2012-044 [LHCb-PAPER-2012-044] |  LHCb-PAPER-2012-043 [LHCb-PAPER-2012-043] |  LHCb-PAPER-2012-042 [LHCb-PAPER-2012-042] |  LHCb-PAPER-2012-041 [LHCb-PAPER-2012-041]
LHCb-PAPER-2012-040 [LHCb-PAPER-2012-040] |  LHCb-PAPER-2012-039 [LHCb-PAPER-2012-039] |  LHCb-PAPER-2012-038 [LHCb-PAPER-2012-038] |  LHCb-PAPER-2012-037 [LHCb-PAPER-2012-037] |  LHCb-PAPER-2012-036 [LHCb-PAPER-2012-036]
LHCb-PAPER-2012-035 [LHCb-PAPER-2012-035] |  LHCb-PAPER-2012-034 [LHCb-PAPER-2012-034] |  LHCb-PAPER-2012-033 [LHCb-PAPER-2012-033] |  LHCb-PAPER-2012-032 [LHCb-PAPER-2012-032] |  LHCb-PAPER-2012-031 [LHCb-PAPER-2012-031]
LHCb-PAPER-2012-030 [LHCb-PAPER-2012-030] |  LHCb-PAPER-2012-029 [LHCb-PAPER-2012-029] |  LHCb-PAPER-2012-028 [LHCb-PAPER-2012-028] |  LHCb-PAPER-2012-027 [LHCb-PAPER-2012-027] |  LHCb-PAPER-2012-026 [LHCb-PAPER-2012-026]
LHCb-PAPER-2012-025 [LHCb-PAPER-2012-025] |  LHCb-PAPER-2012-024 [LHCb-PAPER-2012-024] |  LHCb-PAPER-2012-023 [LHCb-PAPER-2012-023] |  LHCb-PAPER-2012-022 [LHCb-PAPER-2012-022] |  LHCb-PAPER-2012-021 [LHCb-PAPER-2012-021]
LHCb-PAPER-2012-020 [LHCb-PAPER-2012-020] |  LHCb-PAPER-2012-019 [LHCb-PAPER-2012-019] |  LHCb-PAPER-2012-018 [LHCb-PAPER-2012-018] |  LHCb-PAPER-2012-017 [LHCb-PAPER-2012-017] |  LHCb-PAPER-2012-016 [LHCb-PAPER-2012-016]
LHCb-PAPER-2012-015 [LHCb-PAPER-2012-015] |  LHCb-PAPER-2012-014 [LHCb-PAPER-2012-014] |  LHCb-PAPER-2012-013 [LHCb-PAPER-2012-013] |  LHCb-PAPER-2012-012 [LHCb-PAPER-2012-012] |  LHCb-PAPER-2012-011 [LHCb-PAPER-2012-011]
LHCb-PAPER-2012-010 [LHCb-PAPER-2012-010] |  LHCb-PAPER-2012-009 [LHCb-PAPER-2012-009] |  LHCb-PAPER-2012-008 [LHCb-PAPER-2012-008] |  LHCb-PAPER-2012-007 [LHCb-PAPER-2012-007] |  LHCb-PAPER-2012-006 [LHCb-PAPER-2012-006]
LHCb-PAPER-2012-005 [LHCb-PAPER-2012-005] |  LHCb-PAPER-2012-004 [LHCb-PAPER-2012-004] |  LHCb-PAPER-2012-003 [LHCb-PAPER-2012-003] |  LHCb-PAPER-2012-002 [LHCb-PAPER-2012-002] |  LHCb-PAPER-2012-001 [LHCb-PAPER-2012-001]
LHCb-PAPER-2011-045 [LHCb-PAPER-2011-045] |  LHCb-PAPER-2011-044 [LHCb-PAPER-2011-044] |  LHCb-PAPER-2011-043 [LHCb-PAPER-2011-043] |  LHCb-PAPER-2011-042 [LHCb-PAPER-2011-042] |  LHCb-PAPER-2011-041 [LHCb-PAPER-2011-041]
LHCb-PAPER-2011-040 [LHCb-PAPER-2011-040] | LHCb-PAPER-2011-039161616LHCb-PAPER-2011-039 does not exist. |  LHCb-PAPER-2011-038 [LHCb-PAPER-2011-038] |  LHCb-PAPER-2011-037 [LHCb-PAPER-2011-037] |  LHCb-PAPER-2011-036 [LHCb-PAPER-2011-036]
LHCb-PAPER-2011-035 [LHCb-PAPER-2011-035] |  LHCb-PAPER-2011-034 [LHCb-PAPER-2011-034] |  LHCb-PAPER-2011-033 [LHCb-PAPER-2011-033] |  LHCb-PAPER-2011-032 [LHCb-PAPER-2011-032] |  LHCb-PAPER-2011-031 [LHCb-PAPER-2011-031]
LHCb-PAPER-2011-030 [LHCb-PAPER-2011-030] |  LHCb-PAPER-2011-029 [LHCb-PAPER-2011-029] |  LHCb-PAPER-2011-028 [LHCb-PAPER-2011-028] |  LHCb-PAPER-2011-027 [LHCb-PAPER-2011-027] |  LHCb-PAPER-2011-026 [LHCb-PAPER-2011-026]
LHCb-PAPER-2011-025 [LHCb-PAPER-2011-025] |  LHCb-PAPER-2011-024 [LHCb-PAPER-2011-024] |  LHCb-PAPER-2011-023 [LHCb-PAPER-2011-023] |  LHCb-PAPER-2011-022 [LHCb-PAPER-2011-022] |  LHCb-PAPER-2011-021 [LHCb-PAPER-2011-021]
LHCb-PAPER-2011-020 [LHCb-PAPER-2011-020] |  LHCb-PAPER-2011-019 [LHCb-PAPER-2011-019] |  LHCb-PAPER-2011-018 [LHCb-PAPER-2011-018] |  LHCb-PAPER-2011-017 [LHCb-PAPER-2011-017] |  LHCb-PAPER-2011-016 [LHCb-PAPER-2011-016]
LHCb-PAPER-2011-015 [LHCb-PAPER-2011-015] |  LHCb-PAPER-2011-014 [LHCb-PAPER-2011-014] |  LHCb-PAPER-2011-013 [LHCb-PAPER-2011-013] |  LHCb-PAPER-2011-012 [LHCb-PAPER-2011-012] |  LHCb-PAPER-2011-011 [LHCb-PAPER-2011-011]
LHCb-PAPER-2011-010 [LHCb-PAPER-2011-010] |  LHCb-PAPER-2011-009 [LHCb-PAPER-2011-009] |  LHCb-PAPER-2011-008 [LHCb-PAPER-2011-008] |  LHCb-PAPER-2011-007 [LHCb-PAPER-2011-007] |  LHCb-PAPER-2011-006 [LHCb-PAPER-2011-006]
LHCb-PAPER-2011-005 [LHCb-PAPER-2011-005] |  LHCb-PAPER-2011-004 [LHCb-PAPER-2011-004] |  LHCb-PAPER-2011-003 [LHCb-PAPER-2011-003] |  LHCb-PAPER-2011-002 [LHCb-PAPER-2011-002] |  LHCb-PAPER-2011-001 [LHCb-PAPER-2011-001]
LHCb-PAPER-2010-002 [LHCb-PAPER-2010-002] |  LHCb-PAPER-2010-001 [LHCb-PAPER-2010-001] |  |  |
Table 20: LHCb-CONFs (which have their identifier as their cite code). Most CONF notes have been superseded by a paper and are thus retired. This is indicated in the bibtex entry. Do not cite retired CONF notes. DNE: Does not exist.  |  |  |  LHCb-CONF-2022-003 [LHCb-CONF-2022-003] |  LHCb-CONF-2022-001 [LHCb-CONF-2022-001]
---|---|---|---|---
LHCb-CONF-2021-005 [LHCb-CONF-2021-005] |  LHCb-CONF-2021-004 [LHCb-CONF-2021-004] |  LHCb-CONF-2021-003 [LHCb-CONF-2021-003] |  LHCb-CONF-2021-002 [LHCb-CONF-2021-002] |  LHCb-CONF-2021-001 [LHCb-CONF-2021-001]
LHCb-CONF-2020-003 [LHCb-CONF-2020-003] |  LHCb-CONF-2020-002 [LHCb-CONF-2020-002] |  LHCb-CONF-2020-001 [LHCb-CONF-2020-001] |  |
LHCb-CONF-2019-005 [LHCb-CONF-2019-005] |  LHCb-CONF-2019-004 [LHCb-CONF-2019-004] |  LHCb-CONF-2019-003 [LHCb-CONF-2019-003] |  LHCb-CONF-2019-002 [LHCb-CONF-2019-002] |  LHCb-CONF-2019-001 [LHCb-CONF-2019-001]
LHCb-CONF-2018-006 [LHCb-CONF-2018-006] |  |  |  |
LHCb-CONF-2018-005 [LHCb-CONF-2018-005] |  LHCb-CONF-2018-004 [LHCb-CONF-2018-004] |  LHCb-CONF-2018-003 [LHCb-CONF-2018-003] |  LHCb-CONF-2018-002 [LHCb-CONF-2018-002]171717If you cite the gamma combination, always also cite the latest gamma paper as \cite{LHCb-PAPER-2013-020,*LHCb-CONF-2018-002} (unless you cite LHCb-PAPER-2013-020 separately too). |  LHCb-CONF-2018-001 [LHCb-CONF-2018-001]
LHCb-CONF-2017-005 [LHCb-CONF-2017-005] |  LHCb-CONF-2017-004 [LHCb-CONF-2017-004] |  LHCb-CONF-2017-003 [LHCb-CONF-2017-003] |  LHCb-CONF-2017-002 [LHCb-CONF-2017-002] |  LHCb-CONF-2017-001 [LHCb-CONF-2017-001]
LHCb-CONF-2016-018 [LHCb-CONF-2016-018] |  LHCb-CONF-2016-016 [LHCb-CONF-2016-016] |  |  |
LHCb-CONF-2016-015 [LHCb-CONF-2016-015] |  LHCb-CONF-2016-014 [LHCb-CONF-2016-014] |  LHCb-CONF-2016-013 [LHCb-CONF-2016-013] |  LHCb-CONF-2016-012 [LHCb-CONF-2016-012] |  LHCb-CONF-2016-011 [LHCb-CONF-2016-011]
LHCb-CONF-2016-010 [LHCb-CONF-2016-010] |  LHCb-CONF-2016-009 [LHCb-CONF-2016-009] |  LHCb-CONF-2016-008 [LHCb-CONF-2016-008] |  LHCb-CONF-2016-007 [LHCb-CONF-2016-007] |  LHCb-CONF-2016-006 [LHCb-CONF-2016-006]
LHCb-CONF-2016-005 [LHCb-CONF-2016-005] |  LHCb-CONF-2016-004 [LHCb-CONF-2016-004] |  LHCb-CONF-2016-003 [LHCb-CONF-2016-003] |  LHCb-CONF-2016-002 [LHCb-CONF-2016-002] |  LHCb-CONF-2016-001 [LHCb-CONF-2016-001]
LHCb-CONF-2015-005 [LHCb-CONF-2015-005] |  LHCb-CONF-2015-004 [LHCb-CONF-2015-004] |  LHCb-CONF-2015-003 [LHCb-CONF-2015-003] |  LHCb-CONF-2015-002 [LHCb-CONF-2015-002] |  LHCb-CONF-2015-001 [LHCb-CONF-2015-001]
LHCb-CONF-2014-004 [LHCb-CONF-2014-004] |  LHCb-CONF-2014-003 [LHCb-CONF-2014-003] |  LHCb-CONF-2014-002 [LHCb-CONF-2014-002] |  LHCb-CONF-2014-001 [LHCb-CONF-2014-001] |
LHCb-CONF-2013-013 [LHCb-CONF-2013-013] |  LHCb-CONF-2013-012 [LHCb-CONF-2013-012] |  LHCb-CONF-2013-011 [LHCb-CONF-2013-011] |  |
LHCb-CONF-2013-010 [LHCb-CONF-2013-010] |  LHCb-CONF-2013-009 [LHCb-CONF-2013-009] |  LHCb-CONF-2013-008 [LHCb-CONF-2013-008] |  LHCb-CONF-2013-007 [LHCb-CONF-2013-007] |  LHCb-CONF-2013-006 [LHCb-CONF-2013-006]
LHCb-CONF-2013-005 [LHCb-CONF-2013-005] |  LHCb-CONF-2013-004 [LHCb-CONF-2013-004] |  LHCb-CONF-2013-003 [LHCb-CONF-2013-003] |  LHCb-CONF-2013-002 [LHCb-CONF-2013-002] |  LHCb-CONF-2013-001 [LHCb-CONF-2013-001]
LHCb-CONF-2012-034 [LHCb-CONF-2012-034] |  LHCb-CONF-2012-033 [LHCb-CONF-2012-033] |  LHCb-CONF-2012-032 [LHCb-CONF-2012-032] |  LHCb-CONF-2012-031 [LHCb-CONF-2012-031] |
LHCb-CONF-2012-030 [LHCb-CONF-2012-030] |  LHCb-CONF-2012-029 [LHCb-CONF-2012-029] |  LHCb-CONF-2012-028 [LHCb-CONF-2012-028] |  LHCb-CONF-2012-027 [LHCb-CONF-2012-027] |  LHCb-CONF-2012-026 [LHCb-CONF-2012-026]
LHCb-CONF-2012-025 [LHCb-CONF-2012-025] |  LHCb-CONF-2012-024 [LHCb-CONF-2012-024] |  LHCb-CONF-2012-023 [LHCb-CONF-2012-023] |  LHCb-CONF-2012-022 [LHCb-CONF-2012-022] |  LHCb-CONF-2012-021 [LHCb-CONF-2012-021]
LHCb-CONF-2012-020 [LHCb-CONF-2012-020] |  LHCb-CONF-2012-019 [LHCb-CONF-2012-019] |  LHCb-CONF-2012-018 [LHCb-CONF-2012-018] |  LHCb-CONF-2012-017 [LHCb-CONF-2012-017] |  LHCb-CONF-2012-016 [LHCb-CONF-2012-016]
LHCb-CONF-2012-015 [LHCb-CONF-2012-015] |  LHCb-CONF-2012-014 [LHCb-CONF-2012-014] |  LHCb-CONF-2012-013 [LHCb-CONF-2012-013] |  LHCb-CONF-2012-012 [LHCb-CONF-2012-012] |  LHCb-CONF-2012-011 [LHCb-CONF-2012-011]
LHCb-CONF-2012-010 [LHCb-CONF-2012-010] |  LHCb-CONF-2012-009 [LHCb-CONF-2012-009] |  LHCb-CONF-2012-008 [LHCb-CONF-2012-008] |  LHCb-CONF-2012-007 [LHCb-CONF-2012-007] |  LHCb-CONF-2012-006 [LHCb-CONF-2012-006]
LHCb-CONF-2012-005 [LHCb-CONF-2012-005] |  LHCb-CONF-2012-004 [LHCb-CONF-2012-004] |  LHCb-CONF-2012-003 [LHCb-CONF-2012-003] |  LHCb-CONF-2012-002 [LHCb-CONF-2012-002] |  LHCb-CONF-2012-001 [LHCb-CONF-2012-001]
LHCb-CONF-2011-062 [LHCb-CONF-2011-062] |  LHCb-CONF-2011-061 [LHCb-CONF-2011-061] |  |  |
LHCb-CONF-2011-060 [LHCb-CONF-2011-060] |  LHCb-CONF-2011-059 [LHCb-CONF-2011-059] |  LHCb-CONF-2011-058 [LHCb-CONF-2011-058] |  LHCb-CONF-2011-057 [LHCb-CONF-2011-057] |  LHCb-CONF-2011-056 [LHCb-CONF-2011-056]
LHCb-CONF-2011-055 [LHCb-CONF-2011-055] |  LHCb-CONF-2011-054 [LHCb-CONF-2011-054] |  LHCb-CONF-2011-053 [LHCb-CONF-2011-053] |  LHCb-CONF-2011-052 [LHCb-CONF-2011-052] |  LHCb-CONF-2011-051 [LHCb-CONF-2011-051]
LHCb-CONF-2011-050 [LHCb-CONF-2011-050] |  LHCb-CONF-2011-049 [LHCb-CONF-2011-049] |  LHCb-CONF-2011-048 [LHCb-CONF-2011-048] |  LHCb-CONF-2011-047 [LHCb-CONF-2011-047] |  LHCb-CONF-2011-046 [LHCb-CONF-2011-046]
LHCb-CONF-2011-045 [LHCb-CONF-2011-045] |  LHCb-CONF-2011-044 [LHCb-CONF-2011-044] |  LHCb-CONF-2011-043 [LHCb-CONF-2011-043] |  LHCb-CONF-2011-042 [LHCb-CONF-2011-042] |  LHCb-CONF-2011-041 [LHCb-CONF-2011-041]
LHCb-CONF-2011-040 [LHCb-CONF-2011-040] |  LHCb-CONF-2011-039 [LHCb-CONF-2011-039] |  LHCb-CONF-2011-038 [LHCb-CONF-2011-038] |  LHCb-CONF-2011-037 [LHCb-CONF-2011-037] |  LHCb-CONF-2011-036 [LHCb-CONF-2011-036]
LHCb-CONF-2011-035 [LHCb-CONF-2011-035] |  LHCb-CONF-2011-034 [LHCb-CONF-2011-034] |  LHCb-CONF-2011-033 [LHCb-CONF-2011-033] |  LHCb-CONF-2011-032 DNE |  LHCb-CONF-2011-031 [LHCb-CONF-2011-031]
LHCb-CONF-2011-030 [LHCb-CONF-2011-030] |  LHCb-CONF-2011-029 [LHCb-CONF-2011-029] |  LHCb-CONF-2011-028 [LHCb-CONF-2011-028] |  LHCb-CONF-2011-027 [LHCb-CONF-2011-027] |  LHCb-CONF-2011-026 [LHCb-CONF-2011-026]
LHCb-CONF-2011-025 [LHCb-CONF-2011-025] |  LHCb-CONF-2011-024 [LHCb-CONF-2011-024] |  LHCb-CONF-2011-023 [LHCb-CONF-2011-023] |  LHCb-CONF-2011-022 [LHCb-CONF-2011-022] |  LHCb-CONF-2011-021 [LHCb-CONF-2011-021]
LHCb-CONF-2011-020 [LHCb-CONF-2011-020] |  LHCb-CONF-2011-019 [LHCb-CONF-2011-019] |  LHCb-CONF-2011-018 [LHCb-CONF-2011-018] |  LHCb-CONF-2011-017 [LHCb-CONF-2011-017] |  LHCb-CONF-2011-016 [LHCb-CONF-2011-016]
LHCb-CONF-2011-015 [LHCb-CONF-2011-015] |  LHCb-CONF-2011-014 [LHCb-CONF-2011-014] |  LHCb-CONF-2011-013 [LHCb-CONF-2011-013] |  LHCb-CONF-2011-012 [LHCb-CONF-2011-012] |  LHCb-CONF-2011-011 [LHCb-CONF-2011-011]
LHCb-CONF-2011-010 [LHCb-CONF-2011-010] |  LHCb-CONF-2011-009 [LHCb-CONF-2011-009] |  LHCb-CONF-2011-008 [LHCb-CONF-2011-008] |  LHCb-CONF-2011-007 [LHCb-CONF-2011-007] |  LHCb-CONF-2011-006 [LHCb-CONF-2011-006]
LHCb-CONF-2011-005 [LHCb-CONF-2011-005] |  LHCb-CONF-2011-004 [LHCb-CONF-2011-004] |  LHCb-CONF-2011-003 [LHCb-CONF-2011-003] |  LHCb-CONF-2011-002 [LHCb-CONF-2011-002] |  LHCb-CONF-2011-001 [LHCb-CONF-2011-001]
LHCb-CONF-2010-014 [LHCb-CONF-2010-014] |  LHCb-CONF-2010-013 [LHCb-CONF-2010-013] |  LHCb-CONF-2010-012 [LHCb-CONF-2010-012] |  LHCb-CONF-2010-011 [LHCb-CONF-2010-011] |
LHCb-CONF-2010-010 [LHCb-CONF-2010-010] |  LHCb-CONF-2010-009 [LHCb-CONF-2010-009] |  LHCb-CONF-2010-008 [LHCb-CONF-2010-008] |  |
Earlier documents in LHCb-CONF series are actually proceedings.

##  Appendix B Standard symbols

As explained in Sect. 4 this appendix contains standard typesetting of
symbols, particle names, units etc. in LHCb documents.

In the file lhcb-symbols-def.tex, which is included, a large number of symbols
is defined. While they can lead to quicker typing, the main reason is to
ensure a uniform notation within a document and between different LHCb
documents. If a symbol like \CP to typeset $C\\!P$ violation is available for
a unit, particle name, process or whatever, it should be used. If you do not
agree with the notation you should ask to get the definition in lhcb-symbols-
def.tex changed rather than just ignoring it.

All the main particles have been given symbols. The $\mathrm{B}$ mesons are
thus named ${{\mathrm{B}}^{+}}$, ${\mathrm{B}}^{0}$,
${\mathrm{B}}^{0}_{\mathrm{s}}$, and ${\mathrm{B}}_{\mathrm{c}}^{+}$. There is
no need to go into math mode to use particle names, thus saving the typing of
many $ signs. By default particle names are typeset in italic type to agree
with the PDG preference. To get roman particle names you can just change
\setboolean{uprightparticles}{false} to true at the top of this template.

There is a large number of units typeset that ensures the correct use of
fonts, capitals and spacing. As an example we have
${m_{{{\mathrm{B}}^{0}_{\mathrm{s}}}}}=5366.3\pm
0.6\text{\,Me\kern-1.00006ptV\\!/}c^{2}$. Note that $\,\upmu\text{m}$ is
typeset with an upright $\upmu$, even if the particle names have slanted Greek
letters.

A set of useful symbols are defined for working groups. More of these symbols
can be included later. As an example in the Rare Decay group we have several
different analyses looking for a measurement of
$\mathcal{C}_{7}^{{}^{\prime}\mathrm{(eff)}}$ and
$\mathcal{O}_{7.}^{{}^{\prime}}$

##  Appendix C List of all symbols

###  C.1 Experiments

\lhcb | LHCb | \atlas | ATLAS | \cms | CMS
---|---|---|---|---|---
\alice | ALICE | \babar | BaBar | \belle | Belle
\belletwo | Belle II | \besiii | BESIII | \cleo | CLEO
\cdf | CDF | \dzero | D0 | \aleph | ALEPH
\delphi | DELPHI | \opal | OPAL | \lthree | L3
\sld | SLD | \cern | CERN | \lhc | LHC
\lep | LEP | \tevatron | Tevatron | \bfactories | $\mathrm{B}$ Factories
\bfactory | $\mathrm{B}$ Factory | \upgradeone | Upgrade I | \upgradetwo | Upgrade II

####  C.1.1 LHCb sub-detectors and sub-systems

\velo | VELO | \rich | RICH | \richone | RICH1
---|---|---|---|---|---
\richtwo | RICH2 | \ttracker | TT | \intr | IT
\st | ST | \ot | OT | \herschel | HeRSCheL
\spd | SPD | \presh | PS | \ecal | ECAL
\hcal | HCAL | \MagUp | MagUp | \MagDown | MagDown
\ode | ODE | \daq | DAQ | \tfc | TFC
\ecs | ECS | \lone | L0 | \hlt | HLT
\hltone | HLT1 | \hlttwo | HLT2 |  |

###  C.2 Particles

####  C.2.1 Leptons

\electron | $\mathrm{e}$ | \en | $\mathrm{e}^{-}$ | \ep | $\mathrm{e}^{+}$
---|---|---|---|---|---
\epm | $\mathrm{e}^{\pm}$ | \emp | $\mathrm{e}^{\mp}$ | \epem | $\mathrm{e}^{+}\mathrm{e}^{-}$
\muon | $\upmu$ | \mup | $\upmu^{+}$ | \mun | $\upmu^{-}$
\mupm | $\upmu^{\pm}$ | \mump | $\upmu^{\mp}$ | \mumu | $\upmu^{+}\upmu^{-}$
\tauon | $\uptau$ | \taup | $\uptau^{+}$ | \taum | $\uptau^{-}$
\taupm | $\uptau^{\pm}$ | \taump | $\uptau^{\mp}$ | \tautau | $\uptau^{+}\uptau^{-}$
\lepton | $\ell$ | \ellm | $\ell^{-}$ | \ellp | $\ell^{+}$
\ellpm | $\ell^{\pm}$ | \ellmp | $\ell^{\mp}$ | \ellell | $\ell^{+}\ell^{-}$
\neu | $\upnu$ | \neub | $\overline{\upnu}$ | \neue | ${\upnu}_{e}$
\neueb | ${\overline{\upnu}}_{e}$ | \neum | ${\upnu}_{\mu}$ | \neumb | ${\overline{\upnu}}_{\mu}$
\neut | ${\upnu}_{\tau}$ | \neutb | ${\overline{\upnu}}_{\tau}$ | \neul | ${\upnu}_{\ell}$
\neulb | ${\overline{\upnu}}_{\ell}$ |  |  |  |

####  C.2.2 Gauge bosons and scalars

\g | $\upgamma$ | \H | $\mathrm{H}^{0}$ | \Hp | $\mathrm{H}^{+}$
---|---|---|---|---|---
\Hm | $\mathrm{H}^{-}$ | \Hpm | $\mathrm{H}^{\pm}$ | \W | $\mathrm{W}$
\Wp | $\mathrm{W}^{+}$ | \Wm | $\mathrm{W}^{-}$ | \Wpm | $\mathrm{W}^{\pm}$
\Z | $\mathrm{Z}$ |  |  |  |

####  C.2.3 Quarks

\quark | $\mathrm{q}$ | \quarkbar | $\overline{\mathrm{q}}$ | \qqbar | ${\mathrm{q}}{\overline{\mathrm{q}}}$
---|---|---|---|---|---
\uquark | $\mathrm{u}$ | \uquarkbar | $\overline{\mathrm{u}}$ | \uubar | ${\mathrm{u}}{\overline{\mathrm{u}}}$
\dquark | $\mathrm{d}$ | \dquarkbar | $\overline{\mathrm{d}}$ | \ddbar | ${\mathrm{d}}{\overline{\mathrm{d}}}$
\squark | $\mathrm{s}$ | \squarkbar | $\overline{\mathrm{s}}$ | \ssbar | ${\mathrm{s}}{\overline{\mathrm{s}}}$
\cquark | $\mathrm{c}$ | \cquarkbar | $\overline{\mathrm{c}}$ | \ccbar | ${\mathrm{c}}{\overline{\mathrm{c}}}$
\bquark | $\mathrm{b}$ | \bquarkbar | $\overline{\mathrm{b}}$ | \bbbar | ${\mathrm{b}}{\overline{\mathrm{b}}}$
\tquark | $\mathrm{t}$ | \tquarkbar | $\overline{\mathrm{t}}$ | \ttbar | ${\mathrm{t}}{\overline{\mathrm{t}}}$

####  C.2.4 Light mesons

\hadron | $\mathrm{h}$ | \pion | $\uppi$ | \piz | ${\uppi}^{0}$
---|---|---|---|---|---
\pip | ${\uppi}^{+}$ | \pim | ${\uppi}^{-}$ | \pipm | ${\uppi}^{\pm}$
\pimp | ${\uppi}^{\mp}$ | \rhomeson | $\uprho$ | \rhoz | ${\uprho}^{0}$
\rhop | ${\uprho}^{+}$ | \rhom | ${\uprho}^{-}$ | \rhopm | ${\uprho}^{\pm}$
\rhomp | ${\uprho}^{\mp}$ | \kaon | $\mathrm{K}$ | \Kbar | $\kern 0.0pt\overline{\kern 0.0pt\mathrm{K}}$
\Kb | ${\kern 0.0pt\overline{\kern 0.0pt\mathrm{K}}}$ | \KorKbar |  ( ) [-.7ex] $\kern 0.0pt\mathrm{K}$ | \Kz | ${\mathrm{K}}^{0}$
\Kzb | ${\kern 0.0pt\overline{\kern 0.0pt\mathrm{K}}}{}^{0}$ | \Kp | ${\mathrm{K}}^{+}$ | \Km | ${\mathrm{K}}^{-}$
\Kpm | ${\mathrm{K}}^{\pm}$ | \Kmp | ${\mathrm{K}}^{\mp}$ | \KS | ${\mathrm{K}}^{0}_{\mathrm{S}}$
\Vzero | $V^{0}$ | \KL | ${\mathrm{K}}^{0}_{\mathrm{L}}$ | \Kstarz | ${\mathrm{K}}^{*0}$
\Kstarzb | ${\kern 0.0pt\overline{\kern 0.0pt\mathrm{K}}}{}^{*0}$ | \Kstar | ${\mathrm{K}}^{*}$ | \Kstarb | ${\kern 0.0pt\overline{\kern 0.0pt\mathrm{K}}}{}^{*}$
\Kstarp | ${\mathrm{K}}^{*+}$ | \Kstarm | ${\mathrm{K}}^{*-}$ | \Kstarpm | ${\mathrm{K}}^{*\pm}$
\Kstarmp | ${\mathrm{K}}^{*\mp}$ | \KorKbarz | $\kern 0.0pt\shortstack{{(\rule[2.15277pt]{12.50002pt}{0.51212pt})} \\\ [-.7ex] $\kern 0.0pt\mathrm{K}$}{}^{0}$ | \etaz | $\upeta$
\etapr | $\upeta^{\prime}$ | \phiz | $\upphi$ | \omegaz | $\upomega$

####  C.2.5 Charmed mesons

\Dbar | $\kern 0.0pt\overline{\kern 0.0pt\mathrm{D}}$ | \D | $\mathrm{D}$ | \Db | ${\kern 0.0pt\overline{\kern 0.0pt\mathrm{D}}}$
---|---|---|---|---|---
\DorDbar |  ( ) [-.7ex] $\kern 0.0pt\mathrm{D}$ | \Dz | ${\mathrm{D}}^{0}$ | \Dzb | ${\kern 0.0pt\overline{\kern 0.0pt\mathrm{D}}}{}^{0}$
\Dp | ${\mathrm{D}}^{+}$ | \Dm | ${\mathrm{D}}^{-}$ | \Dpm | ${\mathrm{D}}^{\pm}$
\Dmp | ${\mathrm{D}}^{\mp}$ | \DpDm | ${{\mathrm{D}}^{+}}{\kern-1.60004pt{{\mathrm{D}}^{-}}}$ | \Dstar | ${\mathrm{D}}^{*}$
\Dstarb | ${\kern 0.0pt\overline{\kern 0.0pt\mathrm{D}}}{}^{*}$ | \Dstarz | ${\mathrm{D}}^{*0}$ | \Dstarzb | ${\kern 0.0pt\overline{\kern 0.0pt\mathrm{D}}}{}^{*0}$
\theDstarz | ${\mathrm{D}}^{*}(2007)^{0}$ | \theDstarzb | ${\kern 0.0pt\overline{\kern 0.0pt\mathrm{D}}}^{*}(2007)^{0}$ | \Dstarp | ${\mathrm{D}}^{*+}$
\Dstarm | ${\mathrm{D}}^{*-}$ | \Dstarpm | ${\mathrm{D}}^{*\pm}$ | \Dstarmp | ${\mathrm{D}}^{*\mp}$
\theDstarp | ${\mathrm{D}}^{*}(2010)^{+}$ | \theDstarm | ${\mathrm{D}}^{*}(2010)^{-}$ | \theDstarpm | ${\mathrm{D}}^{*}(2010)^{\pm}$
\theDstarmp | ${\mathrm{D}}^{*}(2010)^{\mp}$ | \Ds | ${\mathrm{D}}^{+}_{\mathrm{s}}$ | \Dsp | ${\mathrm{D}}^{+}_{\mathrm{s}}$
\Dsm | ${\mathrm{D}}^{-}_{\mathrm{s}}$ | \Dspm | ${\mathrm{D}}^{\pm}_{\mathrm{s}}$ | \Dsmp | ${\mathrm{D}}^{\mp}_{\mathrm{s}}$
\Dss | ${\mathrm{D}}^{*+}_{\mathrm{s}}$ | \Dssp | ${\mathrm{D}}^{*+}_{\mathrm{s}}$ | \Dssm | ${\mathrm{D}}^{*-}_{\mathrm{s}}$
\Dsspm | ${\mathrm{D}}^{*\pm}_{\mathrm{s}}$ | \Dssmp | ${\mathrm{D}}^{*\mp}_{\mathrm{s}}$ | \DporDsp | ${\mathrm{D}}_{({\mathrm{s}})}^{+}$
\DmorDsm | ${\mathrm{D}}{}_{({\mathrm{s}})}^{-}$ | \DpmorDspm | ${\mathrm{D}}{}_{({\mathrm{s}})}^{\pm}$ |  |

####  C.2.6 Beauty mesons

\B | $\mathrm{B}$ | \Bbar | $\kern 0.0pt\overline{\kern 0.0pt\mathrm{B}}$ | \Bb | ${\kern 0.0pt\overline{\kern 0.0pt\mathrm{B}}}$
---|---|---|---|---|---
\BorBbar |  ( ) [-.7ex] $\kern 0.0pt\mathrm{B}$ | \Bz | ${\mathrm{B}}^{0}$ | \Bzb | ${\kern 0.0pt\overline{\kern 0.0pt\mathrm{B}}}{}^{0}$
\Bd | ${\mathrm{B}}^{0}$ | \Bdb | ${\kern 0.0pt\overline{\kern 0.0pt\mathrm{B}}}{}^{0}$ | \BdorBdbar |  ( ) [-.7ex] $\kern 0.0pt{{\mathrm{B}}^{0}}$
\Bu | ${\mathrm{B}}^{+}$ | \Bub | ${\mathrm{B}}^{-}$ | \Bp | ${{\mathrm{B}}^{+}}$
\Bm | ${{\mathrm{B}}^{-}}$ | \Bpm | ${\mathrm{B}}^{\pm}$ | \Bmp | ${\mathrm{B}}^{\mp}$
\Bs | ${\mathrm{B}}^{0}_{\mathrm{s}}$ | \Bsb | ${\kern 0.0pt\overline{\kern 0.0pt\mathrm{B}}}{}^{0}_{\mathrm{s}}$ | \BsorBsbar |  ( ) [-.7ex] $\kern 0.0pt{{\mathrm{B}}^{0}_{\mathrm{s}}}$
\Bc | ${\mathrm{B}}_{\mathrm{c}}^{+}$ | \Bcp | ${\mathrm{B}}_{\mathrm{c}}^{+}$ | \Bcm | ${\mathrm{B}}_{\mathrm{c}}^{-}$
\Bcpm | ${\mathrm{B}}_{\mathrm{c}}^{\pm}$ | \Bds | ${\mathrm{B}}_{({\mathrm{s}})}^{0}$ | \Bdsb | ${\kern 0.0pt\overline{\kern 0.0pt\mathrm{B}}}{}_{({\mathrm{s}})}^{0}$
\BdorBs | ${\mathrm{B}}_{({\mathrm{s}})}^{0}$ | \BdorBsbar | ${\kern 0.0pt\overline{\kern 0.0pt\mathrm{B}}}{}_{({\mathrm{s}})}^{0}$ |  |

####  C.2.7 Onia

\jpsi | ${\mathrm{J}\mskip-3.0mu/\mskip-2.0mu\uppsi}$ | \psitwos | $\uppsi{(2S)}$ | \psiprpr | $\uppsi(3770)$
---|---|---|---|---|---
\etac | $\upeta_{\mathrm{c}}$ | \psires | $\uppsi$ | \chic | $\upchi_{\mathrm{c}}$
\chiczero | $\upchi_{{\mathrm{c}}0}$ | \chicone | $\upchi_{{\mathrm{c}}1}$ | \chictwo | $\upchi_{{\mathrm{c}}2}$
\chicJ | $\upchi_{{\mathrm{c}}J}$ | \Upsilonres | $\Upsilon$ | \OneS | $\Upsilon{(1S)}$
\TwoS | $\Upsilon{(2S)}$ | \ThreeS | $\Upsilon{(3S)}$ | \FourS | $\Upsilon{(4S)}$
\FiveS | $\Upsilon{(5S)}$ | \chib | $\upchi_{b}$ | \chibzero | $\upchi_{{\mathrm{b}}0}$
\chibone | $\upchi_{{\mathrm{b}}1}$ | \chibtwo | $\upchi_{{\mathrm{b}}2}$ | \chibJ | $\upchi_{{\mathrm{b}}J}$
\theX | $\upchi_{c1}(3872)$ |  |  |  |

####  C.2.8 Light Baryons

\proton | $\mathrm{p}$ | \antiproton | $\overline{\mathrm{p}}$ | \neutron | $\mathrm{n}$
---|---|---|---|---|---
\antineutron | $\overline{\mathrm{n}}$ | \Deltares | $\Delta$ | \Deltaresbar | $\overline{\Delta}$
\Lz | $\Lambda$ | \Lbar | $\kern 0.0pt\overline{\kern 0.0pt\Lambda}$ | \LorLbar |  ( ) [-.7ex] $\kern 0.0pt\Lambda$
\Lambdares | $\Lambda$ | \Lambdaresbar | ${\kern 0.0pt\overline{\kern 0.0pt\Lambda}}$ | \Sigmares | $\Sigma$
\Sigmaz | ${\Sigma}{}^{0}$ | \Sigmap | ${\Sigma}{}^{+}$ | \Sigmam | ${\Sigma}{}^{-}$
\Sigmaresbar | $\kern 0.0pt\overline{\kern 0.0pt{\Sigma}}$ | \Sigmabarz | ${\kern 0.0pt\overline{\kern 0.0pt{\Sigma}}}{}^{0}$ | \Sigmabarp | ${\kern 0.0pt\overline{\kern 0.0pt{\Sigma}}}{}^{+}$
\Sigmabarm | ${\kern 0.0pt\overline{\kern 0.0pt{\Sigma}}}{}^{-}$ | \Xires | $\Xi$ | \Xiz | ${\Xi}^{0}$
\Xim | ${\Xi}^{-}$ | \Xiresbar | $\kern 0.0pt\overline{\kern 0.0pt{\Xi}}$ | \Xibarz | ${\kern 0.0pt\overline{\kern 0.0pt{\Xi}}}^{0}$
\Xibarp | ${\kern 0.0pt\overline{\kern 0.0pt{\Xi}}}^{+}$ | \Omegares | $\Omega$ | \Omegaresbar | $\kern 0.0pt\overline{\kern 0.0pt\Omega}$
\Omegam | ${\Omega}^{-}$ | \Omegabarp | ${\kern 0.0pt\overline{\kern 0.0pt\Omega}}^{+}$ |  |

####  C.2.9 Charmed Baryons

\Lc | ${\Lambda}^{+}_{\mathrm{c}}$ | \Lcbar | ${\kern 0.0pt\overline{\kern 0.0pt\Lambda}}{}^{-}_{\mathrm{c}}$ | \Xic | ${\Xi}_{\mathrm{c}}$
---|---|---|---|---|---
\Xicz | ${\Xi}^{0}_{\mathrm{c}}$ | \Xicp | ${\Xi}^{+}_{\mathrm{c}}$ | \Xicbar | ${\kern 0.0pt\overline{\kern 0.0pt{\Xi}}}{}_{\mathrm{c}}$
\Xicbarz | ${\kern 0.0pt\overline{\kern 0.0pt{\Xi}}}{}_{\mathrm{c}}^{0}$ | \Xicbarm | ${\kern 0.0pt\overline{\kern 0.0pt{\Xi}}}{}_{\mathrm{c}}^{-}$ | \Omegac | ${\Omega}^{0}_{\mathrm{c}}$
\Omegacbar | ${\kern 0.0pt\overline{\kern 0.0pt\Omega}}{}_{\mathrm{c}}^{0}$ | \Xicc | ${\Xi}_{{\mathrm{c}}{\mathrm{c}}}$ | \Xiccbar | ${\kern 0.0pt\overline{\kern 0.0pt{\Xi}}}{}_{{\mathrm{c}}{\mathrm{c}}}$
\Xiccp | ${\Xi}^{+}_{{\mathrm{c}}{\mathrm{c}}}$ | \Xiccpp | ${\Xi}^{++}_{{\mathrm{c}}{\mathrm{c}}}$ | \Xiccbarm | ${\kern 0.0pt\overline{\kern 0.0pt{\Xi}}}{}_{{\mathrm{c}}{\mathrm{c}}}^{-}$
\Xiccbarmm | ${\kern 0.0pt\overline{\kern 0.0pt{\Xi}}}{}_{{\mathrm{c}}{\mathrm{c}}}^{--}$ | \Omegacc | ${\Omega}^{+}_{{\mathrm{c}}{\mathrm{c}}}$ | \Omegaccbar | ${\kern 0.0pt\overline{\kern 0.0pt\Omega}}{}_{{\mathrm{c}}{\mathrm{c}}}^{-}$
\Omegaccc | ${\Omega}^{++}_{{\mathrm{c}}{\mathrm{c}}{\mathrm{c}}}$ | \Omegacccbar | ${\kern 0.0pt\overline{\kern 0.0pt\Omega}}{}_{{\mathrm{c}}{\mathrm{c}}{\mathrm{c}}}^{--}$ |  |

####  C.2.10 Beauty Baryons

\Lb | ${\Lambda}^{0}_{\mathrm{b}}$ | \Lbbar | ${\kern 0.0pt\overline{\kern 0.0pt\Lambda}}{}^{0}_{\mathrm{b}}$ | \Sigmab | ${\Sigma}_{\mathrm{b}}$
---|---|---|---|---|---
\Sigmabp | ${\Sigma}_{\mathrm{b}}^{+}$ | \Sigmabz | ${\Sigma}_{\mathrm{b}}^{0}$ | \Sigmabm | ${\Sigma}_{\mathrm{b}}^{-}$
\Sigmabpm | ${\Sigma}_{\mathrm{b}}^{\pm}$ | \Sigmabbar | ${\kern 0.0pt\overline{\kern 0.0pt{\Sigma}}}_{\mathrm{b}}$ | \Sigmabbarp | ${\kern 0.0pt\overline{\kern 0.0pt{\Sigma}}}_{\mathrm{b}}^{+}$
\Sigmabbarz | ${\kern 0.0pt\overline{\kern 0.0pt{\Sigma}}}_{\mathrm{b}}^{0}$ | \Sigmabbarm | ${\kern 0.0pt\overline{\kern 0.0pt{\Sigma}}}_{\mathrm{b}}^{-}$ | \Sigmabbarpm | ${\kern 0.0pt\overline{\kern 0.0pt{\Sigma}}}_{\mathrm{b}}^{-}$
\Xib | ${\Xi}_{\mathrm{b}}$ | \Xibz | ${\Xi}^{0}_{\mathrm{b}}$ | \Xibm | ${\Xi}^{-}_{\mathrm{b}}$
\Xibbar | ${\kern 0.0pt\overline{\kern 0.0pt{\Xi}}}{}_{\mathrm{b}}$ | \Xibbarz | ${\kern 0.0pt\overline{\kern 0.0pt{\Xi}}}{}_{\mathrm{b}}^{0}$ | \Xibbarp | ${\kern 0.0pt\overline{\kern 0.0pt{\Xi}}}{}_{\mathrm{b}}^{+}$
\Omegab | ${\Omega}^{-}_{\mathrm{b}}$ | \Omegabbar | ${\kern 0.0pt\overline{\kern 0.0pt\Omega}}{}_{\mathrm{b}}^{+}$ |  |

###  C.3 Physics symbols

####  C.3.1 Decays

\BF | $\mathcal{B}$ | \BR | $\mathcal{B}$ | \BRvis | ${\mathcal{B}}_{\mathrm{{vis}}}$
---|---|---|---|---|---
\ra | $\rightarrow$ | \to | $\rightarrow$ |  |

####  C.3.2 Lifetimes

\tauBs | $\tau_{{{\mathrm{B}}^{0}_{\mathrm{s}}}}$ | \tauBd | $\tau_{{{\mathrm{B}}^{0}}}$ | \tauBz | $\tau_{{{\mathrm{B}}^{0}}}$
---|---|---|---|---|---
\tauBu | $\tau_{{{{\mathrm{B}}^{+}}}}$ | \tauDp | $\tau_{{{\mathrm{D}}^{+}}}$ | \tauDz | $\tau_{{{\mathrm{D}}^{0}}}$
\tauL | $\tau_{\mathrm{L}}$ | \tauH | $\tau_{\mathrm{H}}$ |  |

####  C.3.3 Masses

\mBd | $m_{{{\mathrm{B}}^{0}}}$ | \mBp | $m_{{{{\mathrm{B}}^{+}}}}$ | \mBs | $m_{{{\mathrm{B}}^{0}_{\mathrm{s}}}}$
---|---|---|---|---|---
\mBc | $m_{{{\mathrm{B}}_{\mathrm{c}}^{+}}}$ | \mLb | $m_{{{\Lambda}^{0}_{\mathrm{b}}}}$ |  |

####  C.3.4 EW theory, groups

\grpsuthree | $\mathrm{SU}(3)$ | \grpsutw | $\mathrm{SU}(2)$ | \grpuone | $\mathrm{U}(1)$
---|---|---|---|---|---
\ssqtw | $\sin^{2}\\!\theta_{\mathrm{W}}$ | \csqtw | $\cos^{2}\\!\theta_{\mathrm{W}}$ | \stw | $\sin\theta_{\mathrm{W}}$
\ctw | $\cos\theta_{\mathrm{W}}$ | \ssqtwef | ${\sin}^{2}\theta_{\mathrm{W}}^{\mathrm{eff}}$ | \csqtwef | ${\cos}^{2}\theta_{\mathrm{W}}^{\mathrm{eff}}$
\stwef | $\sin\theta_{\mathrm{W}}^{\mathrm{eff}}$ | \ctwef | $\cos\theta_{\mathrm{W}}^{\mathrm{eff}}$ | \gv | $g_{\mbox{\tiny V}}$
\ga | $g_{\mbox{\tiny A}}$ | \order | $\mathcal{O}$ | \ordalph | $\mathcal{O}(\alpha)$
\ordalsq | $\mathcal{O}(\alpha^{2})$ | \ordalcb | $\mathcal{O}(\alpha^{3})$ |  |

####  C.3.5 QCD parameters

\as | $\alpha_{s}$ | \MSb | $\overline{\mathrm{MS}}$ | \lqcd | $\Lambda_{\mathrm{QCD}}$
---|---|---|---|---|---
\qsq | $q^{2}$ |  |  |  |

####  C.3.6 CKM, $C\\!P$ violation

\eps | $\varepsilon$ | \epsK | $\varepsilon_{K}$ | \epsB | $\varepsilon_{B}$
---|---|---|---|---|---
\epsp | $\varepsilon^{\prime}_{K}$ | \CP | $C\\!P$ | \CPT | $C\\!PT$
\T | $T$ | \rhobar | $\overline{\rho}$ | \etabar | $\overline{\eta}$
\Vud | $V_{{\mathrm{u}}{\mathrm{d}}}^{\phantom{\ast}}$ | \Vcd | $V_{{\mathrm{c}}{\mathrm{d}}}^{\phantom{\ast}}$ | \Vtd | $V_{{\mathrm{t}}{\mathrm{d}}}^{\phantom{\ast}}$
\Vus | $V_{{\mathrm{u}}{\mathrm{s}}}^{\phantom{\ast}}$ | \Vcs | $V_{{\mathrm{c}}{\mathrm{s}}}^{\phantom{\ast}}$ | \Vts | $V_{{\mathrm{t}}{\mathrm{s}}}^{\phantom{\ast}}$
\Vub | $V_{{\mathrm{u}}{\mathrm{b}}}^{\phantom{\ast}}$ | \Vcb | $V_{{\mathrm{c}}{\mathrm{b}}}^{\phantom{\ast}}$ | \Vtb | $V_{{\mathrm{t}}{\mathrm{b}}}^{\phantom{\ast}}$
\Vuds | $V_{{\mathrm{u}}{\mathrm{d}}}^{\ast}$ | \Vcds | $V_{{\mathrm{c}}{\mathrm{d}}}^{\ast}$ | \Vtds | $V_{{\mathrm{t}}{\mathrm{d}}}^{\ast}$
\Vuss | $V_{{\mathrm{u}}{\mathrm{s}}}^{\ast}$ | \Vcss | $V_{{\mathrm{c}}{\mathrm{s}}}^{\ast}$ | \Vtss | $V_{{\mathrm{t}}{\mathrm{s}}}^{\ast}$
\Vubs | $V_{{\mathrm{u}}{\mathrm{b}}}^{\ast}$ | \Vcbs | $V_{{\mathrm{c}}{\mathrm{b}}}^{\ast}$ | \Vtbs | $V_{{\mathrm{t}}{\mathrm{b}}}^{\ast}$

####  C.3.7 Oscillations

\dm | $\Delta m$ | \dms | $\Delta m_{{\mathrm{s}}}$ | \dmd | $\Delta m_{{\mathrm{d}}}$
---|---|---|---|---|---
\DG | $\Delta\Gamma$ | \DGs | $\Delta\Gamma_{{\mathrm{s}}}$ | \DGd | $\Delta\Gamma_{{\mathrm{d}}}$
\Gs | $\Gamma_{{\mathrm{s}}}$ | \Gd | $\Gamma_{{\mathrm{d}}}$ | \MBq | $M_{{\mathrm{B}}_{\mathrm{q}}}$
\DGq | $\Delta\Gamma_{{\mathrm{q}}}$ | \Gq | $\Gamma_{{\mathrm{q}}}$ | \dmq | $\Delta m_{{\mathrm{q}}}$
\GL | $\Gamma_{\mathrm{L}}$ | \GH | $\Gamma_{\mathrm{H}}$ | \DGsGs | $\Delta\Gamma_{{\mathrm{s}}}/\Gamma_{{\mathrm{s}}}$
\Delm | $\Delta m$ | \ACP | ${\mathcal{A}}^{{C\\!P}}$ | \Adir | ${\mathcal{A}}^{\mathrm{dir}}$
\Amix | ${\mathcal{A}}^{\mathrm{mix}}$ | \ADelta | ${\mathcal{A}}^{\Delta}$ | \phid | $\phi_{{\mathrm{d}}}$
\sinphid | $\sin\\!{\phi_{{\mathrm{d}}}}$ | \phis | $\phi_{{\mathrm{s}}}$ | \betas | $\beta_{{\mathrm{s}}}$
\sbetas | $\sigma(\beta_{{\mathrm{s}}})$ | \stbetas | $\sigma(2\beta_{{\mathrm{s}}})$ | \stphis | $\sigma(\phi_{{\mathrm{s}}})$
\sinphis | $\sin\\!{\phi_{{\mathrm{s}}}}$ |  |  |  |

####  C.3.8 Tagging

\edet | $\varepsilon_{\mathrm{det}}$ | \erec | $\varepsilon_{\mathrm{rec/det}}$ | \esel | $\varepsilon_{\mathrm{sel/rec}}$
---|---|---|---|---|---
\etrg | $\varepsilon_{\mathrm{trg/sel}}$ | \etot | $\varepsilon_{\mathrm{tot}}$ | \mistag | $\omega$
\wcomb | $\omega^{\mathrm{comb}}$ | \etag | $\varepsilon_{\mathrm{tag}}$ | \etagcomb | $\varepsilon_{\mathrm{tag}}^{\mathrm{comb}}$
\effeff | $\varepsilon_{\mathrm{eff}}$ | \effeffcomb | $\varepsilon_{\mathrm{eff}}^{\mathrm{comb}}$ | \efftag | ${\varepsilon_{\mathrm{tag}}}(1-2\omega)^{2}$
\effD | ${\varepsilon_{\mathrm{tag}}}D^{2}$ | \etagprompt | $\varepsilon_{\mathrm{tag}}^{\mathrm{Pr}}$ | \etagLL | $\varepsilon_{\mathrm{tag}}^{\mathrm{LL}}$

####  C.3.9 Key decay channels

\BdToKstmm | ${{\mathrm{B}}^{0}}\\!\rightarrow{{\mathrm{K}}^{*0}}{\upmu^{+}}{\upmu^{-}}$ | \BdbToKstmm | ${{\kern 0.0pt\overline{\kern 0.0pt\mathrm{B}}}{}^{0}}\\!\rightarrow{{\kern 0.0pt\overline{\kern 0.0pt\mathrm{K}}}{}^{*0}}{\upmu^{+}}{\upmu^{-}}$ | \BsToJPsiPhi | ${{\mathrm{B}}^{0}_{\mathrm{s}}}\\!\rightarrow{{\mathrm{J}\mskip-3.0mu/\mskip-2.0mu\uppsi}}\phi$
---|---|---|---|---|---
\BdToJPsiKst | ${{\mathrm{B}}^{0}}\\!\rightarrow{{\mathrm{J}\mskip-3.0mu/\mskip-2.0mu\uppsi}}{{\mathrm{K}}^{*0}}$ | \BdbToJPsiKst | ${{\kern 0.0pt\overline{\kern 0.0pt\mathrm{B}}}{}^{0}}\\!\rightarrow{{\mathrm{J}\mskip-3.0mu/\mskip-2.0mu\uppsi}}{{\kern 0.0pt\overline{\kern 0.0pt\mathrm{K}}}{}^{*0}}$ | \BsPhiGam | ${{\mathrm{B}}^{0}_{\mathrm{s}}}\\!\rightarrow\phi{\upgamma}$
\BdKstGam | ${{\mathrm{B}}^{0}}\\!\rightarrow{{\mathrm{K}}^{*0}}{\upgamma}$ | \BTohh | ${\mathrm{B}}\\!\rightarrow\mathrm{h}^{+}\mathrm{h}^{\prime-}$ | \BdTopipi | ${{\mathrm{B}}^{0}}\\!\rightarrow{{\uppi}^{+}}{{\uppi}^{-}}$
\BdToKpi | ${{\mathrm{B}}^{0}}\\!\rightarrow{{\mathrm{K}}^{+}}{{\uppi}^{-}}$ | \BsToKK | ${{\mathrm{B}}^{0}_{\mathrm{s}}}\\!\rightarrow{{\mathrm{K}}^{+}}{{\mathrm{K}}^{-}}$ | \BsTopiK | ${{\mathrm{B}}^{0}_{\mathrm{s}}}\\!\rightarrow{{\uppi}^{+}}{{\mathrm{K}}^{-}}$
\Cpipi | $C_{{{\uppi}^{+}}{{\uppi}^{-}}}$ | \Spipi | $S_{{{\uppi}^{+}}{{\uppi}^{-}}}$ | \CKK | $C_{{{\mathrm{K}}^{+}}{{\mathrm{K}}^{-}}}$
\SKK | $S_{{{\mathrm{K}}^{+}}{{\mathrm{K}}^{-}}}$ | \ADGKK | $A^{{\Delta\Gamma}}_{{{\mathrm{K}}^{+}}{{\mathrm{K}}^{-}}}$ |  |

####  C.3.10 Rare decays

\BdKstee | ${{\mathrm{B}}^{0}}\\!\rightarrow{{\mathrm{K}}^{*0}}{\mathrm{e}^{+}\mathrm{e}^{-}}$ | \BdbKstee | ${{\kern 0.0pt\overline{\kern 0.0pt\mathrm{B}}}{}^{0}}\\!\rightarrow{{\kern 0.0pt\overline{\kern 0.0pt\mathrm{K}}}{}^{*0}}{\mathrm{e}^{+}\mathrm{e}^{-}}$ | \bsll | ${\mathrm{b}}\\!\rightarrow{\mathrm{s}}\ell^{+}\ell^{-}$
---|---|---|---|---|---
\AFB | $A_{\mathrm{FB}}$ | \FL | $F_{\mathrm{L}}$ | \AT#1 \AT2 | $A_{\mathrm{T}}^{2}$
\btosgam | ${\mathrm{b}}\\!\rightarrow{\mathrm{s}}{\upgamma}$ | \btodgam | ${\mathrm{b}}\\!\rightarrow{\mathrm{d}}{\upgamma}$ | \Bsmm | ${{\mathrm{B}}^{0}_{\mathrm{s}}}\\!\rightarrow{\upmu^{+}}{\upmu^{-}}$
\Bdmm | ${{\mathrm{B}}^{0}}\\!\rightarrow{\upmu^{+}}{\upmu^{-}}$ | \Bsee | ${{\mathrm{B}}^{0}_{\mathrm{s}}}\\!\rightarrow{\mathrm{e}^{+}\mathrm{e}^{-}}$ | \Bdee | ${{\mathrm{B}}^{0}}\\!\rightarrow{\mathrm{e}^{+}\mathrm{e}^{-}}$
\ctl | $\cos{\theta_{\ell}}$ | \ctk | $\cos{\theta_{K}}$ |  |

####  C.3.11 Wilson coefficients and operators

\C#1 \C9 | $\mathcal{C}_{9}$ | \Cp#1 \Cp7 | $\mathcal{C}_{7}^{{}^{\prime}}$ | \Ceff#1 \Ceff9 | $\mathcal{C}_{9}^{\mathrm{(eff)}}$
---|---|---|---|---|---
\Cpeff#1 \Cpeff7 | $\mathcal{C}_{7}^{{}^{\prime}\mathrm{(eff)}}$ | \Ope#1 \Ope2 | $\mathcal{O}_{2}$ | \Opep#1 \Opep7 | $\mathcal{O}_{7}^{{}^{\prime}}$

####  C.3.12 Charm

\xprime | $x^{\prime}$ | \yprime | $y^{\prime}$ | \ycp | $y_{{C\\!P}}$
---|---|---|---|---|---
\agamma | $A_{\Gamma}$ | \dkpicf | ${{\mathrm{D}}^{0}}\\!\rightarrow{{\mathrm{K}}^{-}}{{\uppi}^{+}}$ |  |

####  C.3.13 QM

\bra[1] \bra{a} | $\langle a|$ | \ket[1] \ket{b} | $|b\rangle$ | \braket[2] \braket{a}{b} | $\langle a|b\rangle$
---|---|---|---|---|---

###  C.4 Units (these macros add a small space in front)

\aunit[1] \aunit{kg} |  kg
---|---

####  C.4.1 Energy and momentum

\tev |  TeV | \gev |  GeV | \mev |  MeV
---|---|---|---|---|---
\kev |  keV | \ev |  eV | \gevgev | $\text{\,Ge\kern-1.00006ptV}^{2}$
\mevc | $\text{\,Me\kern-1.00006ptV\\!/}c$ | \gevc | $\text{\,Ge\kern-1.00006ptV\\!/}c$ | \mevcc | $\text{\,Me\kern-1.00006ptV\\!/}c^{2}$
\gevcc | $\text{\,Ge\kern-1.00006ptV\\!/}c^{2}$ | \gevgevcc | $\text{\,Ge\kern-1.00006ptV}^{2}\\!/c^{2}$ | \gevgevcccc | $\text{\,Ge\kern-1.00006ptV}^{2}\\!/c^{4}$

####  C.4.2 Distance and area (these macros add a small space)

\km |  km | \m |  m | \ma | $\text{\,m}^{2}$
---|---|---|---|---|---
\cm |  cm | \cma | $\text{\,cm}^{2}$ | \mm |  mm
\mma | $\text{\,mm}^{2}$ | \mum | $\,\upmu\text{m}$ | \muma | $\,\upmu\text{m}^{2}$
\nm |  nm | \fm |  fm | \barn |  b
\mbarn |  mb | \mub | $\,\upmu\text{b}$ | \nb |  nb
\invnb | $\text{\,nb}^{-1}$ | \pb |  pb | \invpb | $\text{\,pb}^{-1}$
\fb |  fb | \invfb | $\text{\,fb}^{-1}$ | \ab |  ab
\invab | $\text{\,ab}^{-1}$ |  |  |  |

####  C.4.3 Time

\sec |  s | \ms |  ms | \mus | $\,\upmu\text{s}$
---|---|---|---|---|---
\ns |  ns | \ps |  ps | \fs |  fs
\mhz |  MHz | \khz |  kHz | \hz |  Hz
\invps | $\text{\,ps}^{-1}$ | \invns | $\text{\,ns}^{-1}$ | \yr |  yr
\hr |  hr |  |  |  |

####  C.4.4 Temperature

\degc |  ∘C | \degk |  K
---|---|---|---

####  C.4.5 Material lengths, radiation

\Xrad | $X_{0}$ | \NIL | $\lambda_{\rm int}$ | \mip | MIP
---|---|---|---|---|---
\neutroneq | $n_{\text{eq}}$ | \neqcmcm | $n_{\text{eq}}/\text{cm}^{2}$ | \kRad |  kRad
\MRad |  MRad | \ci |  Ci | \mci |  mCi

####  C.4.6 Uncertainties

\sx | $\sigma_{x}$ | \sy | $\sigma_{y}$ | \sz | $\sigma_{z}$
---|---|---|---|---|---
\stat |  (stat) | \syst |  (syst) | \lumi |  (lumi)

####  C.4.7 Maths

\order | $\mathcal{O}$ | \chisq | $\chi^{2}$ | \chisqndf | $\chi^{2}/\mathrm{ndf}$
---|---|---|---|---|---
\chisqip | $\chi^{2}_{\text{IP}}$ | \chisqvs | $\chi^{2}_{\text{VS}}$ | \chisqvtx | $\chi^{2}_{\text{vtx}}$
\chisqvtxndf | $\chi^{2}_{\text{vtx}}/\mathrm{ndf}$ | \chisqfd | $\chi^{2}_{\text{FD}}$ | \gsim |  $>$$\sim$
\lsim |  $<$$\sim$ | \mean[1] \mean{x} | $\left\langle x\right\rangle$ | \abs[1] \abs{x} | $\left\|x\right\|$
\Real | $\mathcal{R}e$ | \Imag | $\mathcal{I}m$ | \PDF | PDF
\sPlot | sPlot | \sFit | sFit | \deriv | $\mathrm{d}$

###  C.5 Kinematics

####  C.5.1 Energy, Momenta

\Ebeam | $E_{\mbox{\tiny BEAM}}$ | \sqs | $\sqrt{s}$ | \sqsnn | $\sqrt{s_{\scriptscriptstyle\text{NN}}}$
---|---|---|---|---|---
\pt | $p_{\mathrm{T}}$ | \ptsq | $p_{\mathrm{T}}^{2}$ | \ptot | $p$
\et | $E_{\mathrm{T}}$ | \mt | $M_{\mathrm{T}}$ | \dpp | $\Delta p/p$
\msq | $m^{2}$ | \dedx | $\mathrm{d}E/\mathrm{d}x$ |  |

####  C.5.2 PID

\dllkpi | $\mathrm{DLL}_{{\mathrm{K}}{\uppi}}$ | \dllppi | $\mathrm{DLL}_{{\mathrm{p}}{\uppi}}$ | \dllepi | $\mathrm{DLL}_{{\mathrm{e}}{\uppi}}$
---|---|---|---|---|---
\dllmupi | $\mathrm{DLL}_{{\upmu}\pi}$ |  |  |  |

####  C.5.3 Geometry

\degrees | ∘ | \murad | $\,\upmu\text{rad}$ | \mrad |  mrad
---|---|---|---|---|---
\rad |  rad |  |  |  |

####  C.5.4 Accelerator

\betastar | $\beta^{*}$ | \lum | $\mathcal{L}$ | \intlum[1] \intlum{2 $\text{\,fb}^{-1}$ } | $\int\mathcal{L}=2\,\text{\,fb}^{-1}$
---|---|---|---|---|---

###  C.6 Software

####  C.6.1 Programs

\bcvegpy | Bcvegpy | \boole | Boole | \brunel | Brunel
---|---|---|---|---|---
\davinci | DaVinci | \dirac | Dirac | \evtgen | EvtGen
\fewz | Fewz | \fluka | Fluka | \ganga | Ganga
\gaudi | Gaudi | \gauss | Gauss | \geant | Geant4
\hepmc | HepMC | \herwig | Herwig | \moore | Moore
\neurobayes | NeuroBayes | \photos | Photos | \powheg | Powheg
\pythia | Pythia | \resbos | ResBos | \roofit | RooFit
\root | Root | \spice | Spice | \tensorflow | TensorFlow
\urania | Urania |  |  |  |

####  C.6.2 Languages

\cpp | C++ | \ruby | Ruby | \fortran | Fortran
---|---|---|---|---|---
\svn | svn | \git | git | \latex | LaTeX

####  C.6.3 Data processing

\kbit |  kbit | \kbps |  kbit/s | \kbytes |  kB
---|---|---|---|---|---
\kbyps |  kB/s | \mbit |  Mbit | \mbps |  Mbit/s
\mbytes |  MB | \mbyps |  MB/s | \gbit |  Gbit
\gbps |  Gbit/s | \gbytes |  GB | \gbyps |  GB/s
\tbit |  Tbit | \tbps |  Tbit/s | \tbytes |  TB
\tbyps |  TB/s | \dst | DST |  |

###  C.7 Detector related

####  C.7.1 Detector technologies

\nonn | $\mathrm{{\mathit{n^{+}}}\mbox{-}on\mbox{-}{\mathit{n}}}$ | \ponn | $\mathrm{{\mathit{p^{+}}}\mbox{-}on\mbox{-}{\mathit{n}}}$ | \nonp | $\mathrm{{\mathit{n^{+}}}\mbox{-}on\mbox{-}{\mathit{p}}}$
---|---|---|---|---|---
\cvd | CVD | \mwpc | MWPC | \gem | GEM

####  C.7.2 Detector components, electronics

\tell1 | TELL1 | \ukl1 | UKL1 | \beetle | Beetle
---|---|---|---|---|---
\otis | OTIS | \croc | CROC | \carioca | CARIOCA
\dialog | DIALOG | \sync | SYNC | \cardiac | CARDIAC
\gol | GOL | \vcsel | VCSEL | \ttc | TTC
\ttcrx | TTCrx | \hpd | HPD | \pmt | PMT
\specs | SPECS | \elmb | ELMB | \fpga | FPGA
\plc | PLC | \rasnik | RASNIK | \elmb | ELMB
\can | CAN | \lvds | LVDS | \ntc | NTC
\adc | ADC | \led | LED | \ccd | CCD
\hv | HV | \lv | LV | \pvss | PVSS
\cmos | CMOS | \fifo | FIFO | \ccpc | CCPC

####  C.7.3 Chemical symbols

\cfourften | $\mathrm{C_{4}F_{10}}$ | \cffour | $\mathrm{CF_{4}}$ | \cotwo | $\mathrm{CO_{2}}$
---|---|---|---|---|---
\csixffouteen | $\mathrm{C_{6}F_{14}}$ | \mgftwo | $\mathrm{MgF_{2}}$ | \siotwo | $\mathrm{SiO_{2}}$

###  C.8 Special Text

\eg | e.g. | \ie | i.e. | \etal | et al.
---|---|---|---|---|---
\etc | etc. | \cf | cf. | \ffp | ff.
\vs | vs. |  |  |  |

####  C.8.1 Helpful to align numbers in tables

\phz |
---|---

##  Appendix D Supplementary material for LHCb-PAPER-20XX-YYY

This appendix contains supplementary material that will be posted on the
public CDS record but will not appear in the paper.

Please leave the above sentence in your draft for first and second circulation
and replace what follows by your actual supplementary material. For more
information about other types of supplementary material, see Section
Supplemental material. Plots and tables that follow should be well described,
either with captions or with additional explanatory text.

Three types of supplementary material should be distinguished:

  * •

A regular appendix: lengthy equations or long tables are sometimes better put
in an appendix in order not to interrupt the main flow of a paper. Appendices
will appear in the final paper, on arXiv and on the CDS record and should be
considered integral part of a paper, and are thus to be reviewed like the rest
of the paper. An example of an LHCb paper with an appendix is Ref. [LHCb-
PAPER-2013-070].

  * •

Supplementary material for CDS: plots or tables that would make the paper
exceed the page limit or are not appropriate to include in the paper itself,
but are desirable to be shown in public should be added to the paper drafts in
an appendix, and removed from the paper before submitting to arXiv or the
journal. See Appendix D for further instructions. Examples are: comparison
plots of the new result with older results, plots that illustrate cross-
checks. An example of an LHCb paper with supplementary material for CDS is
Ref. [LHCb-PAPER-2013-035]. Supplementary material for CDS cannot be
referenced in the paper. Supplementary material should be included in the
draft paper to be reviewed by the collaboration.

  * •

Supplementary material for the paper. This is usually called “supplemental
material”, which distinguishes it from supplementary material for CDS only.
Most journals allow to submit files along with the paper that will not be part
of the text of the article, but will be stored on the journal server. Examples
are plain text files with numerical data corresponding to the plots in the
paper. The supplemental material should be cited in the paper by including a
reference which should say “See supplemental material at [link] for [give
brief description of material].” The journal will insert a specific link for
[link]. The arXiv version will usually include the supplemental material as
part of the paper and so should not contain the words “at [link]”.
Supplemental material should be included in the draft paper to be reviewed by
the collaboration. An example of an LHCb paper with supplemental material is
Ref. [LHCb-PAPER-2015-029]

Figure 19:  Comparison of our result to those from other experiments. Note
that the style of this figure differs slightly from that of Figure 2 Table 21:
The $\sigma_{{{\mathrm{D}}^{+}_{\mathrm{s}}}}/\sigma_{{{\mathrm{D}}^{+}}}$
ratio (including branching fraction) as functions of $p_{\mathrm{T}}$
(2-6$\text{\,Ge\kern-0.90005ptV\\!/}c$) and $y^{*}$ and
$N^{\text{PV}}_{\text{tracks}}$ in collisions in the (upper) forward and
(lower) backward collision samples. The first uncertainty is statistical, the
second is the component of the systematic uncertainty that is uncorrelated
between bins and the third is the correlated component. Table 22: The
$\sigma_{{{\mathrm{D}}^{+}_{\mathrm{s}}}}/\sigma_{{{\mathrm{D}}^{+}}}$ ratio
(including branching fraction) as functions of $p_{\mathrm{T}}$
(6-12$\text{\,Ge\kern-0.90005ptV\\!/}c$) and $y^{*}$ and
$N^{\text{PV}}_{\text{tracks}}$ in collisions in the (upper) forward and
(lower) backward collision samples. The first uncertainty is statistical, the
second is the component of the systematic uncertainty that is uncorrelated
between bins and the third is the correlated component.

[htb]  Forward( mb ($\text{\,Ge\kern-1.00006ptV\\!/}c$))
$p_{\mathrm{T}}[\text{\,Ge\kern-1.00006ptV\\!/}c],y*\backslash\rm{PVnTracks}$
$[10,60]$ $[60,80]$ $[80,100]$ $[100,120]$ $[120,140]$ $[140,200]$
$[2,4],[1.8,2.3]$ $0.461\pm 0.024\pm 0.024\pm 0.021$ $0.521\pm 0.028\pm
0.021\pm 0.021$ $0.474\pm 0.015\pm 0.028\pm 0.017$ $0.492\pm 0.015\pm 0.019\pm
0.020$ $0.505\pm 0.018\pm 0.019\pm 0.019$ $0.568\pm 0.018\pm 0.019\pm 0.026$
$[2,4],[2.3,2.8]$ $0.439\pm 0.011\pm 0.015\pm 0.016$ $0.455\pm 0.022\pm
0.018\pm 0.016$ $0.470\pm 0.024\pm 0.016\pm 0.014$ $0.553\pm 0.015\pm 0.020\pm
0.019$ $0.503\pm 0.030\pm 0.015\pm 0.016$ $0.573\pm 0.018\pm 0.022\pm 0.022$
$[2,4],[2.8,3.3]$ $0.480\pm 0.032\pm 0.016\pm 0.015$ $0.521\pm 0.017\pm
0.018\pm 0.018$ $0.513\pm 0.015\pm 0.016\pm 0.016$ $0.489\pm 0.021\pm 0.018\pm
0.016$ $0.554\pm 0.019\pm 0.019\pm 0.017$ $0.626\pm 0.025\pm 0.024\pm 0.023$
$[4,6],[1.8,2.3]$ $0.486\pm 0.018\pm 0.018\pm 0.018$ $0.484\pm 0.018\pm
0.022\pm 0.017$ $0.483\pm 0.015\pm 0.017\pm 0.015$ $0.532\pm 0.026\pm 0.019\pm
0.017$ $0.502\pm 0.019\pm 0.019\pm 0.016$ $0.585\pm 0.020\pm 0.016\pm 0.022$
$[4,6],[2.3,2.8]$ $0.442\pm 0.015\pm 0.015\pm 0.015$ $0.479\pm 0.015\pm
0.018\pm 0.014$ $0.516\pm 0.015\pm 0.017\pm 0.014$ $0.512\pm 0.017\pm 0.020\pm
0.015$ $0.497\pm 0.017\pm 0.020\pm 0.014$ $0.621\pm 0.021\pm 0.018\pm 0.020$
$[4,6],[2.8,3.3]$ $0.461\pm 0.018\pm 0.022\pm 0.015$ $0.465\pm 0.033\pm
0.022\pm 0.014$ $0.492\pm 0.023\pm 0.018\pm 0.014$ $0.556\pm 0.024\pm 0.020\pm
0.017$ $0.512\pm 0.023\pm 0.018\pm 0.014$ $0.593\pm 0.032\pm 0.022\pm 0.019$
Backward( mb ($\text{\,Ge\kern-1.00006ptV\\!/}c$))
$p_{\mathrm{T}}[\text{\,Ge\kern-1.00006ptV\\!/}c],y*\backslash\rm{PVnTracks}$
$[10,60]$ $[60,80]$ $[80,100]$ $[100,120]$ $[120,140]$ $[140,180]$ $[180,250]$
$[2,4],[-3.3,-2.8]$$0.526\pm 0.012\pm 0.026\pm 0.019$$0.512\pm 0.016\pm
0.038\pm 0.028$$0.512\pm 0.011\pm 0.023\pm 0.020$$0.547\pm 0.016\pm 0.021\pm
0.023$$0.562\pm 0.020\pm 0.022\pm 0.020$$0.613\pm 0.017\pm 0.023\pm
0.024$$0.723\pm 0.023\pm 0.041\pm 0.036$ $[2,4],[-3.8,-3.3]$$0.467\pm 0.010\pm
0.022\pm 0.015$$0.510\pm 0.012\pm 0.026\pm 0.022$$0.543\pm 0.013\pm 0.025\pm
0.017$$0.517\pm 0.016\pm 0.022\pm 0.018$$0.569\pm 0.014\pm 0.034\pm
0.018$$0.614\pm 0.015\pm 0.020\pm 0.020$$0.902\pm 0.057\pm 0.028\pm 0.036$
$[2,4],[-4.3,-3.8]$$0.518\pm 0.015\pm 0.033\pm 0.021$$0.484\pm 0.022\pm
0.030\pm 0.020$$0.508\pm 0.014\pm 0.029\pm 0.017$$0.556\pm 0.023\pm 0.026\pm
0.018$$0.607\pm 0.020\pm 0.030\pm 0.019$$0.683\pm 0.022\pm 0.035\pm
0.025$$0.756\pm 0.084\pm 0.026\pm 0.028$ $[4,6],[-3.3,-2.8]$$0.505\pm 0.015\pm
0.029\pm 0.020$$0.485\pm 0.015\pm 0.027\pm 0.021$$0.536\pm 0.014\pm 0.028\pm
0.018$$0.567\pm 0.026\pm 0.024\pm 0.019$$0.611\pm 0.020\pm 0.023\pm
0.018$$0.683\pm 0.017\pm 0.023\pm 0.022$$0.736\pm 0.033\pm 0.023\pm 0.029$
$[4,6],[-3.8,-3.3]$$0.470\pm 0.015\pm 0.031\pm 0.015$$0.566\pm 0.018\pm
0.035\pm 0.018$$0.531\pm 0.015\pm 0.028\pm 0.014$$0.567\pm 0.016\pm 0.026\pm
0.017$$0.598\pm 0.034\pm 0.025\pm 0.016$$0.617\pm 0.018\pm 0.019\pm
0.017$$0.762\pm 0.036\pm 0.022\pm 0.025$ $[4,6],[-4.3,-3.8]$$0.428\pm 0.017\pm
0.031\pm 0.018$$0.456\pm 0.017\pm 0.034\pm 0.016$$0.554\pm 0.020\pm 0.036\pm
0.016$$0.585\pm 0.024\pm 0.036\pm 0.017$$0.578\pm 0.024\pm 0.028\pm
0.016$$0.644\pm 0.026\pm 0.030\pm 0.020$$0.819\pm 0.049\pm 0.032\pm 0.029$

[htb]  Forward( mb ($\text{\,Ge\kern-1.00006ptV\\!/}c$))
$p_{\mathrm{T}}[\text{\,Ge\kern-1.00006ptV\\!/}c],y*\backslash\rm{PVnTracks}$
$[10,60]$ $[60,80]$ $[80,100]$ $[100,120]$ $[120,140]$ $[140,200]$
$[6,8],[1.8,2.3]$ $0.486\pm 0.027\pm 0.026\pm 0.016$ $0.490\pm 0.020\pm
0.032\pm 0.016$ $0.514\pm 0.023\pm 0.029\pm 0.015$ $0.497\pm 0.023\pm 0.027\pm
0.016$ $0.520\pm 0.027\pm 0.025\pm 0.015$ $0.589\pm 0.050\pm 0.020\pm 0.019$
$[6,8],[2.3,2.8]$ $0.436\pm 0.027\pm 0.022\pm 0.016$ $0.461\pm 0.027\pm
0.028\pm 0.013$ $0.499\pm 0.023\pm 0.026\pm 0.013$ $0.509\pm 0.024\pm 0.026\pm
0.014$ $0.545\pm 0.041\pm 0.029\pm 0.015$ $0.522\pm 0.068\pm 0.037\pm 0.015$
$[6,8],[2.8,3.3]$ $0.481\pm 0.032\pm 0.032\pm 0.017$ $0.480\pm 0.029\pm
0.037\pm 0.015$ $0.469\pm 0.028\pm 0.027\pm 0.013$ $0.507\pm 0.041\pm 0.027\pm
0.015$ $0.525\pm 0.037\pm 0.029\pm 0.014$ $0.607\pm 0.047\pm 0.060\pm 0.018$
$[8,12],[1.8,2.3]$ $0.449\pm 0.037\pm 0.039\pm 0.015$ $0.603\pm 0.039\pm
0.044\pm 0.019$ $0.504\pm 0.031\pm 0.030\pm 0.014$ $0.487\pm 0.030\pm 0.029\pm
0.015$ $0.577\pm 0.042\pm 0.034\pm 0.017$ $0.559\pm 0.038\pm 0.028\pm 0.018$
$[8,12],[2.3,2.8]$ $0.385\pm 0.031\pm 0.033\pm 0.014$ $0.520\pm 0.036\pm
0.039\pm 0.017$ $0.504\pm 0.062\pm 0.035\pm 0.014$ $0.479\pm 0.036\pm 0.037\pm
0.014$ $0.504\pm 0.040\pm 0.032\pm 0.014$ $0.608\pm 0.043\pm 0.031\pm 0.018$
$[8,12],[2.8,3.3]$ $0.391\pm 0.042\pm 0.037\pm 0.013$ $0.429\pm 0.045\pm
0.042\pm 0.014$ $0.421\pm 0.040\pm 0.035\pm 0.013$ $0.535\pm 0.075\pm 0.050\pm
0.016$ $0.573\pm 0.065\pm 0.053\pm 0.016$ $0.595\pm 0.077\pm 0.043\pm 0.020$
Backward( mb ($\text{\,Ge\kern-1.00006ptV\\!/}c$))
$p_{\mathrm{T}}[\text{\,Ge\kern-1.00006ptV\\!/}c],y*\backslash\rm{PVnTracks}$
$[10,60]$ $[60,80]$ $[80,100]$ $[100,120]$ $[120,140]$ $[140,180]$ $[180,250]$
$[6,8],[2.8,3.3]$$0.541\pm 0.026\pm 0.050\pm 0.023$$0.529\pm 0.026\pm 0.054\pm
0.020$$0.461\pm 0.021\pm 0.036\pm 0.015$$0.528\pm 0.025\pm 0.035\pm
0.017$$0.506\pm 0.024\pm 0.029\pm 0.016$$0.677\pm 0.030\pm 0.028\pm
0.021$$0.815\pm 0.050\pm 0.057\pm 0.028$ $[6,8],[3.3,3.8]$$0.497\pm 0.029\pm
0.051\pm 0.016$$0.443\pm 0.024\pm 0.041\pm 0.015$$0.474\pm 0.025\pm 0.038\pm
0.013$$0.615\pm 0.029\pm 0.048\pm 0.018$$0.556\pm 0.029\pm 0.034\pm
0.015$$0.638\pm 0.030\pm 0.030\pm 0.018$$0.599\pm 0.060\pm 0.115\pm 0.021$
$[6,8],[3.8,4.3]$$0.470\pm 0.034\pm 0.062\pm 0.020$$0.497\pm 0.037\pm 0.070\pm
0.020$$0.464\pm 0.033\pm 0.054\pm 0.015$$0.498\pm 0.035\pm 0.048\pm
0.015$$0.636\pm 0.046\pm 0.058\pm 0.017$$0.587\pm 0.042\pm 0.039\pm
0.018$$0.710\pm 0.086\pm 0.049\pm 0.024$ $[8,12],[2.8,3.3]$$0.611\pm 0.051\pm
0.082\pm 0.023$$0.541\pm 0.035\pm 0.067\pm 0.024$$0.446\pm 0.028\pm 0.043\pm
0.015$$0.583\pm 0.033\pm 0.051\pm 0.019$$0.553\pm 0.037\pm 0.046\pm
0.018$$0.513\pm 0.029\pm 0.027\pm 0.016$$0.738\pm 0.063\pm 0.053\pm 0.024$
$[8,12],[3.3,3.8]$$0.510\pm 0.041\pm 0.077\pm 0.019$$0.517\pm 0.032\pm
0.073\pm 0.019$$0.592\pm 0.042\pm 0.068\pm 0.020$$0.606\pm 0.093\pm 0.065\pm
0.020$$0.458\pm 0.053\pm 0.042\pm 0.013$$0.588\pm 0.041\pm 0.040\pm
0.016$$0.660\pm 0.078\pm 0.065\pm 0.020$ $[8,12],[3.8,4.3]$$0.481\pm 0.061\pm
0.114\pm 0.021$$0.434\pm 0.057\pm 0.087\pm 0.021$$0.545\pm 0.072\pm 0.100\pm
0.021$$0.616\pm 0.081\pm 0.105\pm 0.022$$0.472\pm 0.065\pm 0.075\pm
0.016$$0.584\pm 0.079\pm 0.073\pm 0.021$$0.657\pm 0.154\pm 0.072\pm 0.024$

LHCb collaboration

A. N. Other1.

The author list for journal publications is generated from the Membership
Database shortly after ’approval to go to paper’ has been given. It will be
sent to you by email shortly after a paper number has been assigned. The
author list should be included in the draft used for first and second
circulation, to allow new members of the collaboration to verify that they
have been included correctly. Occasionally a misspelled name is corrected, or
associated institutions become full members. Therefore an updated author list
will be sent to you after the final EB review of the paper. In case line
numbering doesn’t work well after including the authorlist, try moving the
`\bigskip` after the last author to a separate line.

The authorship for Conference Reports should be “The LHCb collaboration”, with
a footnote giving the name(s) of the contact author(s), but without the full
list of collaboration names.

The authorship for Figure Reports should be “The LHCb collaboration”, with no
contact author and without the full list of collaboration names.