# CFSum: A Coarse-to-Fine Contribution Network for
Multimodal Summarization

Min Xiao 1,2, Junnan Zhu1,2, Haitao Lin1,2, Yu Zhou1,3, Chengqing Zong1,2
1 State Key Laboratory of Multimodal Artificial Intelligence Systems,
Institute of Automation, CAS, Beijing, China
2 School of Artificial Intelligence, University of Chinese Academy of
Sciences, Beijing, China
3 Fanyu AI Laboratory, Zhongke Fanyu Technology Co., Ltd, Beijing, China
$\left\\{\right.$min.xiao, junnan.zhu, haitao.lin, yzhou,
<EMAIL_ADDRESS>Corresponding author.

###### Abstract

Multimodal summarization usually suffers from the problem that the
contribution of the visual modality is unclear. Existing multimodal
summarization approaches focus on designing the fusion methods of different
modalities, while ignoring the adaptive conditions under which visual
modalities are useful. Therefore, we propose a novel Coarse-to-Fine
contribution network for multimodal Summarization (CFSum) to consider
different contributions of images for summarization. First, to eliminate the
interference of useless images, we propose a pre-filter module to abandon
useless images. Second, to make accurate use of useful images, we propose two
levels of visual complement modules, word level and phrase level.
Specifically, image contributions are calculated and are adopted to guide the
attention of both textual and visual modalities. Experimental results have
shown that CFSum significantly outperforms multiple strong baselines on the
standard benchmark. Furthermore, the analysis verifies that useful images can
even help generate non-visual words which are implicitly represented in the
image111Code is available at https://github.com/xiaomin418/CFSum.

##  1 Introduction

With the information explosion, the internet is flooded with various
multimodal information. Multimodal summarization (MMS) can help generate more
abundant and comprehensive summary information than unimodal based on extra
visual information. Existing studies on multimodal summarization include
multimodal sentence summarization (Li et al., 2018b), multimodal summarization
with multimodal output (Zhu et al., 2018), multimodal meeting summarization
(Li et al., 2019) and so on. In this paper, we focus on the task that
generating a text summary based on the input of a text and an image. It has
been proved that integrating multimodal data can help improve the quality of
the summary (Li et al., 2018b; Jangra et al., 2020; Palaskar et al., 2019; Yu
et al., 2021).

Figure 1: Experiments on existing mainstream multimodal summarization models.
The performance is not affected by masking images. “Concat” is the concatenate
fusion method, and “Attn” is the attention-based fusion method.

However, it is unclear whether the visual modality can indeed benefit the
process of summarization. Thus, we conduct an experiment to explore the
influence of masking images on the summary. As shown in Figure 1, the solid
lines mean the performance of summary generated by masking portions of images,
and the dashed lines indicate the origin performance. It can be observed that
the dashed and the solid lines roughly coincide, which indicates that masking
images do not affect the performance of the multimodal model. Some masking
rates can even raise the ROUGE-1 value of the summary. It indicates that
existing models do not make effective use of image information for the
summary.

Existing approaches have two major problems. First, existing studies focus on
multimodal fusion, such as concatenate, attention-based, and gate-based fusion
(referring to Related Work). However, they ignore the adaptive conditions
under which visual modalities are helpful. Thus they are poor at extracting
useful visual information. Furthermore, all fusion methods do not explicitly
model the image complementarity for the summary. Especially for the attention-
based method, the inter-attention is not accurate enough, which leads to
inefficient use of the image. Second, in many samples, the image may introduce
noise, while existing fusion methods assume that all images are helpful for
the summary without considering the interference of useless images. As
analyzed above, we believe that: 1) It is essential to eliminate the influence
of the useless image. 2) The contributions of the image to the summary need to
be clarified. In particular, it is necessary to consider the complementarity
of visual information relative to textual information.

Although we notice the lack of image contributions, it is difficult to detach
various roles of images from a single fusion layer. Thus, in this work, we
propose a novel Coarse-to-Fine contribution network for multimodal
Summarization (CFSum) to extract the role of the image at different stages.
First, we apply a pre-filter module to abandon useless images. It coarsely
obtains helpful images for the summary. Specifically, the consistency of
content between image and text is calculated. If the consistency is low, the
image will be masked in subsequent encoding. Second, when the image is
coarsely useful, the complement module is employed to finely guide the fusion
of text with the image. To consider image contributions for text with
different granularities, the complement module consists of two levels, word
level and phrase level. For the word level complement module, to obtain the
image complementarity over the text, the difference between bi-modal and uni-
modal inputs is measured through a classification task. Then we add a loss to
guide the attention between words and the image. For the phrase level
complement module, similar to the word level, the image complementarity on
phrases is acquired to guide the attention between phrases and the image.
Through these modules, the model can acquire more explicit image contributions
and provide better multimodal encoding for summary generation.

Our contributions are as follows:

(1) We propose a Coarse-to-Fine contribution network for multimodal
Summarization (CFSum) to model different contributions of images for
summarization.

(2) We innovatively design a pre-filter module to coarsely reduce the
interference of the useless images and develop two visual complement modules
to finely obtain image complementarity over the summary.

(3) Experimental results show that our model outperforms strong baselines.
Besides, extensive analysis proves that useful image even contributes to non-
visual words which are implicitly represented in the image.

##  2 Related Work

##### Multimodal Summarization Tasks.

In the field of multimodal summarization, there are usually three steps.
First, different feature extractor modules are adopted to extract the features
of the text and the image, respectively. Second, the different features are
fused at the fusion layer. Finally, the fused context features are fed into
the text decoder to generate a summary. Existing studies focus on multimodal
fusion. Specifically, the fusion methods consist of concatenate, attention-
based, and gate-based. The concatenate fusion directly concatenates multimodal
features into a fusion context (Li et al., 2018b, 2020a). It can fully extract
high-level features of different modalities, but there is a large gap between
high-dimensional spaces. The attention-based methods fuse all multimodal
features with attention mechanism (Atri et al., 2021; Palaskar et al., 2019;
Kitada et al., 2022), which can get the correlations between each unit of text
and image. Gate-based methods take text as the central modality (Jangra et
al., 2021) and exploit images to help focus on the core information (Liu et
al., 2020; Li et al., 2020b). In summary, (1) all fusion methods do not
explicitly model the image complementarity for the summary, which leads to
inefficient use of the image. (2) concatenate and attention-based cannot
eliminate the influence of useless images in the fusion layer.

Figure 2: CFSum framework. $L_{f},L_{w},L_{p}$ denote the starting layer of
the pre-filter, the word-level complement, and the phrase-level complement
modules, respectively.

##### Cross-modal tasks.

Some studies have noted the contributions of modalities and explored the
cross-modal influence in other multimodal tasks. Zeng et al. (2021) propose
loss modulation to explore the contribution of individual modalities and
devise a modality filter to reduce modality noise, which considers consistency
and complementarity between different modalities. Zhu et al. (2018) propose
multi-task summarization: the method also selects the image that best matches
the summary when generating a text summary. It guarantees the positive effect
of images on the summary. Li et al. (2022) exploit ReLu-based cross-attention
to align visual features to textual representation, which abandons low-value
attention scores for those unaligned visual features. Inspired by the above
studies, we propose CFSum, which considers various image contributions for
better encoding input text and generating the final summary.

##  3 Proposed Methods

###  3.1 Overview

In this section, we introduce the details of CFSum. Given a dataset consisting
of $n$ triplets $(t_{i},v_{i},s_{i})_{i\in[1,n]}$ with a text $t_{i}$, an
image $v_{i}$, and a summary $s_{i}$, the multimodal summarization task aims
at generating $s_{i}$ based on $t_{i}$ and $v_{i}$.

As depicted in Figure 2, the CFSum takes bi-modal and uni-modal streams as
input parallelly. It builds coarse and fine image contributions with three
modules (Coarse-to-Fine Structure). First, the pre-filter module coarsely
filters the images inconsistent with texts (Pre-filter Module). Second, two
levels of visual complement modules consisting of word level (Word-level
Complement) and phrase level (Phrase-level Complement) make accurate use of
useful images.

###  3.2 Coarse-to-Fine Structure

We build our model based on the multimodal transformer UNITER (Chen et al.,
2020) and GRU (Chung et al., 2014) encoder-decoder architectures. We refer the
model to UniG. As shown in Figure 2(a), in order to evaluate the
complementarity of different modalities, the bi-modal and uni-modal inputs are
operated parallelly with the same encoder. The two parallel streams can catch
the gain of the image. Additionally, we generate a summary relying on bi-modal
encoding. Uni-modal encoding assists in measuring various contributions and
guiding the bi-modal encoding.

Specifically, the multimodal encoder consists of $L=12$ multimodal transformer
layers. We serve the $L$ layers as a hierarchical structure and divide $L$
layers into three parts as shown in Figure 2(a). $L_{f},L_{w},L_{p}$ mark as
the starting layer of the pre-filter, the word-level complement, and the
phrase-level complement modules, respectively. Existing studies assume all
images benefit summary generation or input text encoding, resulting in damage
from unnecessary images. The pre-filter module is utilized to eliminate the
interference of misleading images in advance. Next, the word-level complement
module is developed to model the gain of the image on input words for the
summary. Then the image gain guides the subsequent attention between words and
the image. Finally, similar to the word level, the phrase-level complement
module concentrates on phrases at higher layers. Each component will be
elaborated in the following sub-sections.

###  3.3 Pre-filter Module

The bi-modal and uni-modal features from the $i^{th}$ layer are encoded as
$m^{i}\in\mathbb{R}^{C\times H},{u}^{i}\in\mathbb{R}^{T\times H}$, where
$i\in[1,L]$, and $C,T$ denote the lengths of bi-modal and uni-modal tokens.
$H$ denotes the hidden dimension. The bi-modal self-attention matrix in the
$i^{th}$ layer is $A^{i}=(a_{r,s}^{i})\in\mathbb{R}^{C\times C}$.

The pre-filter module aims at filtering images that are unnecessary to the
summary. As shown in Figure 2(a), given two encoded features ${m}^{L_{f}}$ and
${u}^{L_{f}}$ from the $L_{f}^{th}$ layer, the goal of the filtering module is
to select those useless images and guide the self-attention of all subsequent
layers. We believe that if the bi-modal feature has low consistency with the
uni-modal feature, the image may introduce interferential information.
Specifically, we first calculate the consistency $\Delta^{C}$ between uni-
modal feature $u^{L_{f}}$ and bi-modal feature $m^{L_{f}}$ as follows:

$\displaystyle pu=\textrm{MeanPool}(u^{L_{f}}),$ (1) $\displaystyle
pm=\textrm{MeanPool}(m^{L_{f}}),$ (2)
$\displaystyle\Delta^{C}=\textrm{Sign}(\textrm{cosine}(pu,pm)-\alpha)$ (3)

We define the indicator function as:

$I_{r,s}=\begin{cases}1,&\mbox{$r\geq T,s\geq T$}\\\
0,&\mbox{otherwise}\end{cases}$ (4)

which represents the text attending to the image, the image attending to the
text, and the image attending to itself shown in Figure 2(a). Then we
calculate the new subsequent self-attention $na^{i}_{r,s}$ with:

$\displaystyle na^{i}_{r,s}=$ $\displaystyle
a^{i}_{r,s}\times(1-I_{r,s})+a^{i}_{r,s}\times I_{r,s}\times\Delta^{C},$ (5)
$\displaystyle i\in[L_{f}+1,L]$

By correcting the attention matrix, the image with a large deviation in
content is cropped out. In other words, the multimodal inconsistency features
degenerate into text-only features through this process. The simple method has
been shown to be effective in our experiments.

###  3.4 Word-level Complement

This section introduces a word-level complement module, considered as an
auxiliary task during the training process. First, we measure the image gain
on input words for the summary. Then the image gain is applied to guide the
attention between words and the image (as shown in Figure 2(b)).

##### Image gain measurement.

Intuitively, the text tokens should concern the image which is helpful for the
summary. In previous attention-based studies, inter-modality correlation can
be modeled as $\textrm{softmax}(\frac{QK}{\sqrt{D}})V$. $Q,K,V$ are the
projected features from the bi-modal input. However, it does not explicitly
model the image complementarity for the summary, which leads to inefficient
use of the image.

Following the motivation above, we hope to calculate the image gain on the
summary with mutual information. In other words, we want to measure whether
generating summaries based on bi-modal feature $m^{L}$ is more deterministic
than generating summaries based on uni-modal feature $u^{L}$. Thus, we expect
to calculate the image gain on the $k$-th word of the reference summary:

$GI_{k}=Gain(s_{k}/u^{L},s_{k}/m^{L})$ (6)

However, we intend to obtain $GI_{k}$ before generating summary $S$ and
encoding $m^{L}$. Thus $GI$ can be beneficial for generating $S$ and encoding
$m^{L}$. To this end, we define Copy Classification task $Y$ to approximate
the summary task $S$: for each input text token $t_{j}$, the target is to
binary categorize whether it appears in the reference summary. If the token
appears in the reference summary, it is classified as $\hat{y_{j}}=1$;
otherwise, $\hat{y_{j}}=0$. Next, the $GI_{j}$ is given by:

$GI_{j}=Gain(y_{j}/u^{L_{w}},y_{j}/m^{L_{w}})$ (7)

where $u^{L_{w}},m^{L_{w}}$ denote the uni-modal and the bi-modal feature
acquired by $L_{w}^{th}$ layer. Finally, we measure the gain that the image
brings to predict whether a word appears correctly in the summary as follows:

$\displaystyle GI_{j}$ $\displaystyle=Gain(y_{j}/m^{L_{w}},y_{j}/u^{L_{w}})$
$\displaystyle=logP(y_{j}=\hat{y_{j}}/m^{L_{w}})-logP(y_{j}=\hat{y_{j}}/u^{L_{w}})$
(8)

Derivation details can refer to the Appendix B. In addition, to ensure the
correct gain direction, we add a binary cross-entropy loss to train the Copy
Classification Task $Y$:

$\mathcal{L}_{copyc}=\textrm{BCE}(y_{j},\hat{y_{j}}/m^{L_{w}})+\textrm{BCE}(y_{j},\hat{y_{j}}/u^{L_{w}})$
(9)

##### Image gain application.

We introduce divergence loss to restrain that the image with greater gain
should receive more textual attention. In successive
$i^{th}\,_{i\in[L_{w}+1,L_{w}+3]}$ layer, the average inter-attention between
each text token $t_{j}$ and the image is:

$T2V_{j}^{i}=\frac{1}{2(C-T)}(\sum_{s=T+1}^{s=C}a_{j,s}^{i}+\sum_{s=T+1}^{s=C}a_{s,j}^{i})$
(10)

where $a_{j,s}^{i},a_{s,j}^{i}$ represent the attention of image-to-text and
text-to-image, respectively.

Finally, an attention divergence loss is added to restrain the inter-attention
scores $T2V_{j}^{i}$ with $GI_{j}$:

$\mathcal{L}_{word}=\textrm{KL}(\textrm{Softmax}(GI_{j})||\textrm{Avg}(T2V_{j}^{i}))$
(11)

By minimizing the divergence loss, the text token attends to the image
according to the gain it brings. Interaction between word gain and inter-
attention learns to pay attention to the useful image. Appendix C provides
examples to figure out the word-level complement.

###  3.5 Phrase-level Complement

Considering the image contribution to text of different granularities, we put
forward a phrase-level complement module similar to the word level (as shown
in Figure 2(c)).

##### Image gain measurement.

Different from copy classification task at the word level, we define Copy
Scorer task to measure the image gain on phrases: We obtain phrases
$\\{p_{1},...,p_{k}...\\}$ from the text with
StandfordNLP222https://github.com/stanfordnlp. $\\{l_{1},...,l_{k}...\\}$ is
the number of words in the phrases. The task targets scoring the proportion of
words that appear in both the phrase and the reference summary:

$\displaystyle R_{p_{k}}^{u}=\textrm{Scorer}(u^{L_{p}})$ (12) $\displaystyle
R_{p_{k}}^{m}=\textrm{Scorer}(m^{L_{p}})$ (13)

where Scorer is a MLP. The ground truth proportion is obtained with the
following:

$\hat{R_{p_{k}}}=\frac{Count_{\,t_{j^{\prime}}\in
p_{k}}(t_{j^{\prime}})}{l_{k}}$ (14)

where $Count_{\,t_{j^{\prime}}\in p_{k}}$ denotes the number of words that
appear in both the phrase $p_{k}$ and the reference summary. Therefore, the
image gain on phrase can be acquired as:

$GS_{p_{k}}=|R_{p_{k}}^{u}-\hat{R_{p_{k}}}|-|R_{p_{k}}^{m}-\hat{R_{p_{k}}}|$
(15)

Similarly, to guarantee the correctness of phrase gain, we add a squared loss
for the Copy Scorer task:

$\mathcal{L}_{copys}=\textrm{MSE}(R_{p_{k}}^{m},\hat{R_{p_{k}}})+\textrm{MSE}(R_{p_{k}}^{u},\hat{R_{p_{k}}})$
(16)

Especially, for the convenience of applying phrase gain $GS_{p_{k}}$, we
project it to token gain $GS_{j}$ as:

$GS_{j}=\max\\{GS_{p_{k}},t_{j}\in p_{k}\\}$ (17)

##### Image gain application.

Second, we introduce a phrase attention divergence loss to restrain that the
image with greater phrase gain should receive more textual attention. We
obtain the inter-attention score $T2V_{j}^{i}$ from $i\in[L_{p}+1,L_{p}+3]$
layers as formula 10. Finally, we restrain it with the following:

$\mathcal{L}_{phrase}=\textrm{KL}(\textrm{Softmax}(GS_{j})||\textrm{Avg}(T2V_{j}^{i}))$
(18)

The phrase-level restraint guarantees the image contributing to the text of
phrase granularity.

###  3.6 Training and Inference

In the training phase, to ensure the accuracy of the information difference
between bi-modal and uni-modal, we initialize the model only with the summary
generation loss. We apply negative log-likelihood for the target word sequence
as the overall loss:

$\mathcal{L}_{gen}=\frac{1}{T}\sum_{t=1}^{T}(-\log{P(s_{t})})$ (19)

Then the model is finetuned with the hierarchical modules’ objectives:

$\mathcal{L}=\mathcal{L}_{gen}+\mathcal{L}_{word}+\mathcal{L}_{phrase}+\mathcal{L}_{copyc}+\mathcal{L}_{copys}$
(20)

In the inference phase, we only maintain the pre-filter module.
$\mathcal{L}_{word}$ and $\mathcal{L}_{phrase}$ are added to let the model
learn how to fuse multimodal information. Hence, differences in training and
inference phases would not hurt the generation.

Dataset | Size | Src. Length | Ref. Length
---|---|---|---
train | 62,000 | 11/21.68/63 | 2/7.72/25
dev | 2,000 | 11/24.35/47 | 3/7.68/17
test | 2,000 | 11/22.97/51 | 3/7.67/24

Table 1: Statistical information about the dataset. “Src. Length” and “Ref.
Length” denote the number of words in the source sentence and reference
summary. Three values in each column represent: Min, Avg, Max.

##  4 Experiment

###  4.1 Settings

We experiment with the multimodal sentence summarization
dataset333http://www.nlpr.ia.ac.cn/cip/dataset.htm (Li et al., 2018a). It
contains 66,000 samples in total.

| ROUGE-1 | ROUGE-2 | ROUGE-L | BLEU | BERTScore | MoverScore
---|---|---|---|---|---|---
Lead$\vartriangle$ | 33.64 | 13.40 | 31.84 | - | - | -
Compress$\vartriangle$ | 31.56 | 11.02 | 28.87 | - | - | -
ABS$\vartriangle$ | 35.95 | 18.21 | 31.89 | - | - | -
SEASS$\vartriangle$ | 44.86 | 23.03 | 41.92 | - | - | -
Multi-Source$\vartriangle$ | 39.67 | 19.11 | 38.03 | - | - | -
Doubly-Attention$\vartriangle$ | 41.11 | 21.75 | 39.92 | - | - | -
MAtt$\vartriangle$ | 47.28 | 24.85 | 44.48 | - | - | -
MSE$\vartriangle$ | 45.63 | 23.68 | 42.97 | - | - | -
UniG (T) | 45.90 | 24.08 | 42.98 | 47.09 | 86.54 | 31.06
UniG | 46.22 | 24.28 | 43.47 | 46.85 | 86.57 | 30.95
K1 | CFSum-F3 | 47.39* | 25.42* | 44.35* | 48.51* | 86.90* | 31.89*
CFSum-W6 | 47.33* | 25.38* | 44.26* | 48.43* | 86.91* | 31.84*
CFSum-P9 | 47.28* | 25.13* | 44.18* | 48.19* | 86.91* | 31.67
K2 | CFSum-W6F9 | 47.53* | 25.37* | 44.41* | 48.48* | 86.94* | 32.24*
CFSum-F3W6 | 47.66* | 25.33* | 44.54* | 48.45* | 86.95* | 31.88*
CFSum-F3P9 | 47.72* | 25.51* | 44.58* | 48.66* | 86.96* | 32.03*
K3 | CFSum-F3W6P9 |  47.86* |  25.64* |  44.64* |  48.83* |  86.98* |  32.36*
CFSum-F9W3P6 | 47.58* | 25.42* | 44.49* | 48.35* | 86.95* | 32.10*

Table 2: Automatic evaluation results of CFSum. “$\vartriangle$” marks the
results from Li et al. (2018b) and Li et al. (2020b)555Because there is no
output from these systems, we only report ROUGEs in papers. In addition, BLEU,
BERTScore, and MoverScore cannot be recalculated.. “K1/2/3” denotes
one/two/three kind(s) of contribution(s). “*” indicates the model performs
significantly better than the UniG by the 95% confidence interval (p<0.05).

And each sample is a triplet of <sentence, image, summary>. Some statistical
information is shown in Table 1. Appendix D gives the categories of test
images.

We set both the text embedding dimension and hidden dimension as 768. We apply
“bert-base-uncased” (Devlin et al., 2019) vocabulary with 28,996 tokens. The
dropout (Srivastava et al., 2014) rate is set to $0.1$. Besides, the batch
size is set to $8$. For texts, we use the max text encoding length of 60, and
the minimum text decoding length is 8. For images, the object detection tool
BUTD (Anderson et al., 2018) is applied to extract the image feature, with the
maximum boxes as $36$. We use the Adam (Kingma and Ba, 2014) optimizer and set
the learning rate as $5e-05$, momentum parameters as
$\beta_{1}=0.9,\beta_{2}=0.98$. The model is initially trained with the
summary generation loss for 35 epochs. To obtain our final model, we train for
a further 15 epochs with the hierarchical framework. In the test phase, we
employ beam search and set the beam size as $4$ to generate the summary. The
parameter $\alpha$ in the pre-filter module is set as $\alpha=0.65$.

###  4.2 Comparative Methods

Lead: Exploiting the first eight words as the summary.
Compress (Clarke and Lapata, 2008): It uses integer linear programming to
infer global optimal compressions.
ABS (Rush et al., 2015): It utilizes an attention-based model to generate
words of summary conditioned on the input text.
SEASS (Zhou et al., 2017): It constructs a second-level sentence
representation with a sentence encoder and a selective gate for summarization.
Multi-Source (Libovický and Helcl, 2017): It combines multiple source
modalities based on the hierarchical attention mechanisms over each modality
for solving the multimodal machine translation.
Doubley-attentive (Calixto et al., 2017): It uses two separate attention
mechanisms to incorporate the visual feature, which minified the gap between
the image and the translation.
MAtt (Li et al., 2018b): It proposes modality attention and image filtering
for multimodal summarization.
MSE (Li et al., 2020b): It proposes to apply the visual selective gates to
multimodal summarization.
UniG: It is our base model with multimodal transformer UNITER and GRU decoder.
UniG (T): UniG fed only with textual modality.

Figure 3: Visualization of word-complement gain and phrase-complement gain
produced by our model. $\blacktriangle$/$\bullet$ indicates that the value is
greater than 0.

###  4.3 Automatic Evaluation Results

Our methods are reported with six automatic metrics, including ROUGE-1,
ROUGE-2, ROUGE-L (Lin and Hovy, 2002), BLEU (Papineni et al., 2002), BERTScore
(Zhang* et al., 2020), and MoverScore (Zhao et al., 2019). More details of
evaluation scripts are given in Appendix A.

##### Comparisons with Baselines.

We compare our work with our baselines and other work on the multimodal
sentence summarization dataset. Table 5 shows the results of different models.
The results show that UniG performs comparably with UniG (T). CFSums build on
UniG, and introduces coarse-to-fine contribution network. “F”, “W”, and “P”
represent the pre-filter, the word-level complement, and the phrase-level
complement modules contained in the CFSum. The footnote is the location of the
corresponding module. For example, CFSum-F3 contains a pre-filter module with
$L_{f}=3$. Generally, our methods CFSums outperform the baselines UniG (T) and
UniG. The best methods is CFSum-F3W6P9. And it achieves 1.64 higher points on
ROUGE-1 than UniG. We also conduct ablation experiments by applying one or two
kinds of contributions. The results demonstrate that each image contribution
benefits the model. In addition, combining all image contributions brings
greater gains than a single contribution. Therefore, it can be concluded that
different contributions are complementary to the summary. Besides, we conduct
ablation studies by placing the pre-filter module at the beginning ($L_{f}=3$)
or the end of the hierarchical layers ($L_{f}=9$). In comparison, placing the
pre-filter module at the beginning (CFSum-F3W6P9) yields better performance.

Model | Informativeness | Fluency | Non-Redundancy
---|---|---|---
UniG (T) | 3.63 | 3.48 | 2.91
UniG | 3.69 | 3.66 | 3.05
CFSum | 3.91 | 3.90 | 3.31

Table 3: Human evaluations. 1 stands for the worst, and 5 stands for the best
for three metrics.

###  4.4 Human Evaluation Results

We randomly select 50 samples from the test dataset and invite three
postgraduates to score 1-5 for the summary quality. The evaluation metrics
include informativeness, fluency, and non-redundancy. (1) Informativeness:
Does the system summary contain comprehensive reference content? (2) Fluency:
Is the system summary grammatically correct and readable? (3) Non-Redundancy:
Does the system summary not have redundant or incorrect information relative
to the reference summary? Table 3 shows the human evaluation results. We run
the inter-annotator agreement study on three volunteers’ scores and achieve
reasonable scores, 0.47, 0.39, and 0.43 on informativeness, fluency, and non-
redundancy, respectively. The results show that our method CFSum-F3W6S9
achieves the best performance on all three aspects over UniG (T) and UniG
baselines. Thus we conclude that our method is also effective through human
evaluation.

###  4.5 Further Analysis

####  4.5.1 Complement Modules Analysis

In other multimodal tasks such as image captioning and multimodal translation,
their models learn to attend to the image more for visual words like “red”,
“rose” and “woman” (Lu et al., 2017; Calixto et al., 2017). Since our proposed
complement modules aim at extracting complementary information relative to
textual modality, we want to know which word or phrase the image provides
gains on. As shown in Figure 3, we visualize the complement gain value for the
input words. We manually align the reference summary and the input text. The
word highlighted with a red box indicates that it appears in the reference
summary generatively666“Generatively” means that the summary word is obtained
by paraphrasing or synonymous substitution of the input word. or extractively.

First, we find that words with positive image gain can basically cover the
reference summary information. It proves that our calculated gain helps in
generating the target summary words. Second, it can be observed that different
complement modules bring positive gains in different areas, which means
different levels of complement modules are complementary. It further explains
that multiple contributions are better than a single contribution in the
experimental results. At last, it is worth noting that some words are gained
from the image but are not visible in the image, i.e., “relatives” and
“victims”. Therefore, we believe the image brings gain in both visible and
invisible words. We explain further in Gainable Images.

Figure 4: Visualization of gainable images.

####  4.5.2 Pre-filter Module Analysis

Since we believe that images should provide meaningful contributions instead
of robustness enhancements in multimodal summarization, we wonder whether
unpaired multimodal data may affect the performance of our model. Therefore,
we try generating the summary based on the unpaired image and text.

In the test set, most of the images are highly similar in theme and content.
Generating unpaired data with automatic shuffling is not significant for
analysis. Therefore, we manually exchange $v_{i}$ and $v_{j}$ in pairs
<$t_{i},v_{i}$>, <$t_{j},v_{j}$>, where $v_{i},v_{j}$ have different themes or
contents.

We exchange 20 pairs from 100 pairs of test samples. And we conduct
experiments with different sampling for three times. The mean and standard
deviation reports as Table 4. “Paired” represents ROUGE-1 on test set,
“Unpaired” represents ROUGE-1 on the unpaired set. “CFSum (filter-off)”
represents turning down the pre-filter mechanism.

Model | Paired | Unpaired
---|---|---
UniG | 46.22 | 46.20(±0.012)
CFSum | 47.86 | 47.46(±0.007)
CFSum (filter-off) | 47.77 | 47.12(±0.011)

Table 4: Performance of unpaired multimodal data for the baseline and our
methods.

The results show different trends in the two models. For UniG, unpaired multi-
modalities do not affect the performance. We guess UniG does not exploit
meaningful image information while relying only on text to generate the
summary. In contrast, CFSum hurt more severely from unpairing. The difference
exists because CFSum depends on the image and text. Thus, the unpaired image
would reduce the correct information that CFSum gets. However, CFSum still
performs better than UniG, proving that it is fault-tolerant. Furthermore,
CFSum (filter-off) significantly suffers from unpaired data, showing that pre-
filter can eliminate useless images.

####  4.5.3 Ablation Study

One of the most important hyperparameters in CFSum is the location of
different contribution modules. Because the three modules’ order in the
network is fixed, we change their absolute position in the encoder layers and
report the corresponding performance in Figure 5. $w$ denotes the number of
layers between two modules, and the $X$ axis denotes the starting layer of the
pre-filter module. The results show that the different layer settings achieve
comparable performance. It is noticeable that $w=2$ weakens the model. This is
due to the fact that the network with small $w$ loses the advantage of a
hierarchical structure in the encoder.

Figure 5: Ablation studies of layer setting.

####  4.5.4 Gainable Images

We select three gained words and corresponding gainable images to show in
Figure 4. Consistent with our perception, images bring gains on visual words,
such as “earthquake”. More importantly, they bring gains on non-visual words
such as “celebrate” and “victims”. For example, “celebrate” may be used in
competitions, events, and diplomacy as shown in Figure 4. Multimodal tasks
such as image captioning or multimodal question answering focus on
establishing associations between visual words and images. However, multimodal
summarization also needs to pay attention to the associations between non-
visual words and images. In other words, image contributes to both visual and
non-visual words.

##  5 Conclusion

Based on the observation that existing multimodal summary models do not take
full advantage of useful image information, this paper focuses on modeling
different contributions of images for summarization. Therefore, we propose a
novel framework CFSum consisting of pre-filter, word-level complement, and
phrase-level complement modules. The pre-filter coarsely eliminates the impact
of useless images. The two-level visual complement modules measure different
aspects of image gains and guide the fusion of different modalities.
Experimental results have shown that CFSum can significantly improve the
summary. More importantly, the complement modules make images contribute to
visual words and non-visual words.

## Limitations

Since our method constructs on the multimodal transformer, it cannot be
migrated to the dual-stream model. Experiment results show that CFSum can
achieve comparable performance with strong baselines. But it still cannot
surpass the SOTA of some dual-stream large models.

## Acknowledgements

The research work has been supported by the Natural Science Foundation of
China under Grant No. 62106263.

## References

  * Anderson et al. (2018) Peter Anderson, Xiaodong He, Chris Buehler, Damien Teney, Mark Johnson, Stephen Gould, and Lei Zhang. 2018.  Bottom-up and top-down attention for image captioning and visual question answering.  In _2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition_ , pages 6077–6086.
  * Atri et al. (2021) Yash Kumar Atri, Shraman Pramanick, Vikram Goyal, and Tanmoy Chakraborty. 2021.  See, hear, read: Leveraging multimodality with guided attention for abstractive text summarization.  _Know.-Based Syst._ , 227(C).
  * Calixto et al. (2017) Iacer Calixto, Qun Liu, and Nick Campbell. 2017.  Doubly-attentive decoder for multi-modal neural machine translation.  In _Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)_ , pages 1913–1924, Vancouver, Canada. Association for Computational Linguistics.
  * Chen et al. (2020) Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. 2020.  Uniter: Universal image-text representation learning.  In _Computer Vision – ECCV 2020_ , pages 104–120, Cham. Springer International Publishing.
  * Chung et al. (2014) Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014.  Empirical evaluation of gated recurrent neural networks on sequence modeling.
  * Clarke and Lapata (2008) James Clarke and Mirella Lapata. 2008.  Global inference for sentence compression: An integer linear programming approach.  _Journal of Artificial Intelligence Research_ , pages 399–429.
  * Devlin et al. (2019) Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019.  BERT: Pre-training of deep bidirectional transformers for language understanding.  In _Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)_ , pages 4171–4186, Minneapolis, Minnesota. Association for Computational Linguistics.
  * Jangra et al. (2020) Anubhav Jangra, Adam Jatowt, Mohammad Hasanuzzaman, and Sriparna Saha. 2020.  Text-image-video summary generation using joint integer linear programming.  In _Advances in Information Retrieval_ , pages 190–198, Cham. Springer International Publishing.
  * Jangra et al. (2021) Anubhav Jangra, Adam Jatowt, Sriparna Saha, and Mohammad Hasanuzzaman. 2021.  A survey on multi-modal summarization.  _CoRR_ , abs/2109.05199.
  * Kingma and Ba (2014) Diederik P Kingma and Jimmy Ba. 2014.  Adam: A method for stochastic optimization.  _arXiv preprint arXiv:1412.6980_.
  * Kitada et al. (2022) Shunsuke Kitada, Yuki Iwazaki, Riku Togashi, and Hitoshi Iyatomi. 2022.  Dm2s2: Deep multimodal sequence sets with hierarchical modality attention.  _IEEE Access_ , 10:120023–120034.
  * Li et al. (2020a) Haoran Li, Peng Yuan, Song Xu, Youzheng Wu, Xiaodong He, and Bowen Zhou. 2020a.  Aspect-aware multimodal summarization for chinese e-commerce products.  _Proceedings of the AAAI Conference on Artificial Intelligence_ , 34(05):8188–8195.
  * Li et al. (2018a) Haoran Li, Junnan Zhu, Tianshan Liu, Jiajun Zhang, and Chengqing Zong. 2018a.  Multi-modal sentence summarization with modality attention and image filtering.  In _International Joint Conference on Artificial Intelligence_.
  * Li et al. (2018b) Haoran Li, Junnan Zhu, Tianshang Liu, Jiajun Zhang, and Chengqing Zong. 2018b.  Multi-modal sentence summarization with modality attention and image filtering.  In _Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI-18_ , pages 4152–4158. International Joint Conferences on Artificial Intelligence Organization.
  * Li et al. (2020b) Haoran Li, Junnan Zhu, Jiajun Zhang, Xiaodong He, and Chengqing Zong. 2020b.  Multimodal sentence summarization via multimodal selective encoding.  In _Proceedings of the 28th International Conference on Computational Linguistics_ , pages 5655–5667, Barcelona, Spain (Online). International Committee on Computational Linguistics.
  * Li et al. (2022) Jiangfeng Li, Zijian Zhang, Bowen Wang, Qinpei Zhao, and Chenxi Zhang. 2022.  Inter- and intra-modal contrastive hybrid learning framework for multimodal abstractive summarization.  _Entropy_ , 24(6).
  * Li et al. (2019) Manling Li, Lingyu Zhang, Heng Ji, and Richard J. Radke. 2019.  Keep meeting summaries on topic: Abstractive multi-modal meeting summarization.  In _Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics_ , pages 2190–2196, Florence, Italy. Association for Computational Linguistics.
  * Libovický and Helcl (2017) Jindřich Libovický and Jindřich Helcl. 2017.  Attention strategies for multi-source sequence-to-sequence learning.  In _Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)_ , pages 196–202, Vancouver, Canada. Association for Computational Linguistics.
  * Lin and Hovy (2002) Chin-Yew Lin and Eduard Hovy. 2002.  Manual and automatic evaluation of summaries.  In _Proceedings of the ACL-02 Workshop on Automatic Summarization_ , pages 45–51, Phildadelphia, Pennsylvania, USA. Association for Computational Linguistics.
  * Liu et al. (2020) Nayu Liu, Xian Sun, Hongfeng Yu, Wenkai Zhang, and Guangluan Xu. 2020.  Multistage fusion with forget gate for multimodal summarization in open-domain videos.  In _Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)_ , pages 1834–1845, Online. Association for Computational Linguistics.
  * Lu et al. (2017) Jiasen Lu, Caiming Xiong, Devi Parikh, and Richard Socher. 2017.  Knowing when to look: Adaptive attention via a visual sentinel for image captioning.  _2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)_ , pages 3242–3250.
  * Palaskar et al. (2019) Shruti Palaskar, Jindřich Libovický, Spandana Gella, and Florian Metze. 2019.  Multimodal abstractive summarization for how2 videos.  In _Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics_ , pages 6587–6596, Florence, Italy. Association for Computational Linguistics.
  * Papineni et al. (2002) Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002.  Bleu: a method for automatic evaluation of machine translation.  In _Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics_ , pages 311–318, Philadelphia, Pennsylvania, USA. Association for Computational Linguistics.
  * Rush et al. (2015) Alexander M. Rush, Sumit Chopra, and Jason Weston. 2015.  A neural attention model for abstractive sentence summarization.  In _Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing_ , pages 379–389, Lisbon, Portugal. Association for Computational Linguistics.
  * Srivastava et al. (2014) Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014.  Dropout: A simple way to prevent neural networks from overfitting.  _J. Mach. Learn. Res._ , 15(1):1929–1958.
  * Yu et al. (2021) Tiezheng Yu, Wenliang Dai, Zihan Liu, and Pascale Fung. 2021.  Vision guided generative pre-trained language models for multimodal abstractive summarization.  In _Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing_ , pages 3995–4007, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
  * Zeng et al. (2021) Ying Zeng, Sijie Mai, and Haifeng Hu. 2021.  Which is making the contribution: Modulating unimodal and cross-modal dynamics for multimodal sentiment analysis.  In _Findings of the Association for Computational Linguistics: EMNLP 2021_ , pages 1262–1274, Punta Cana, Dominican Republic. Association for Computational Linguistics.
  * Zhang* et al. (2020) Tianyi Zhang*, Varsha Kishore*, Felix Wu*, Kilian Q. Weinberger, and Yoav Artzi. 2020.  Bertscore: Evaluating text generation with bert.  In _International Conference on Learning Representations_.
  * Zhao et al. (2019) Wei Zhao, Maxime Peyrard, Fei Liu, Yang Gao, Christian M. Meyer, and Steffen Eger. 2019.  MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance.  In _Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)_ , pages 563–578, Hong Kong, China. Association for Computational Linguistics.
  * Zhou et al. (2017) Qingyu Zhou, Nan Yang, Furu Wei, and Ming Zhou. 2017.  Selective encoding for abstractive sentence summarization.  In _Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)_ , pages 1095–1104, Vancouver, Canada. Association for Computational Linguistics.
  * Zhu et al. (2018) Junnan Zhu, Haoran Li, Tianshang Liu, Yu Zhou, Jiajun Zhang, and Chengqing Zong. 2018.  MSMO: Multimodal summarization with multimodal output.  In _Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing_ , pages 4154–4164, Brussels, Belgium. Association for Computational Linguistics.

##  Appendix A Experiment details

Here, we will introduce some detailed settings for our experiments. All
methods are run on NVIDIA GeForce RTX 3090. UniG has 139M parameters. When the
batch size is 8, it takes 20 hours to train for 50 epochs with a single GPU.

We also provide evaluation scripts for reproduction. For ROUGE score, we use
file2rouge777https://github.com/pltrdy/files2rouge with default settings. For
BERTScore888https://pypi.org/project/bert-score/0.2.1, we use the official
API. It exploits the pre-trained contextual embeddings from BERT to calculate
the similarity between the hypothesis sentences and the reference sentences.
For MoverScore, we use
moverscore_v2999https://github.com/AIPHES/emnlp19-moverscore, which leverages
BERT and Earth Mover Distance to measure the similarity.

##  Appendix B Derivation details

Derivation detail of formula 8 is:

$\displaystyle GI_{j}$ $\displaystyle=Gain(y_{j}/m^{L_{w}},y_{j}/u^{L_{w}})$
$\displaystyle=\textrm{KL}(\hat{y_{j}}||y_{j}/m^{L_{w}})-\textrm{KL}(\hat{y_{j}}||y_{j}/u^{L_{w}})$
$\displaystyle=P(\hat{y_{j}}=1)\cdot logP(y_{j}=1/m^{L_{w}})$
$\displaystyle+P(\hat{y_{j}}=0)\cdot logP(y_{j}=0/m^{L_{w}})$
$\displaystyle-P(\hat{y_{j}}=1)\cdot logP(y_{j}=1/u^{L_{w}})$
$\displaystyle-P(\hat{y_{j}}=0)\cdot logP(y_{j}=0/u^{L_{w}})$
$\displaystyle=P(y_{j}=\hat{y_{j}})\cdot logP(y_{j}=\hat{y_{j}}/m^{L_{w}})$
$\displaystyle-P(y_{j}=\hat{y_{j}})\cdot logP(y_{j}=\hat{y_{j}}/u^{L_{w}})$
$\displaystyle=logP(y_{j}=\hat{y_{j}}/m^{L_{w}})-logP(y_{j}=\hat{y_{j}}/u^{L_{w}})$
(21)

Thus the gain is simplified to entropy difference.

##  Appendix C Examples of Complement Modules

We will provide some examples to explain further Word-level Complement. For
one of the input words $t_{j}$, we assume that it appears in the reference
summary. Then the ground truth of the copy classification is $\hat{y_{j}}=1$.
We list hypothetical classification results of bi-modal and uni-modal in Table
5.

| P($y_{j}=1$) | P($y_{j}=0$)
---|---|---
$u^{L_{w}}$ | 0.4 | 0.6
$m^{L_{w}}$ | 0.6 | 0.4

Table 5: Copy classification task results.

Then, the $GI_{j}$ is calculated as:

$\displaystyle GI_{j}$ $\displaystyle=Gain(y_{j}/m^{L_{w}},y_{j}/u^{L_{w}})$
$\displaystyle=logP(y_{j}=1/m^{L_{w}})-logP(y_{j}=1/u^{L_{w}})$
$\displaystyle=log0.6-log0.4$ $\displaystyle=0.405$ (22)

which means the image may give the input word $t_{j}$ a gain of 0.405.
Furthermore, the image brings a positive gain. Thus in the attention layer,
the text word $t_{j}$ should give the image a higher attention score.

##  Appendix D Impact of image category

To further analyze the impact of our approach on different categories of
images. We categorize the test images with VGG19 and show the performance of
each type of image. As shown in Figure 6, there are 380 categories in the test
images, and we list the top 10 categories with the highest proportion. It can
be seen that the image is evenly distributed. The line charts also show that
CFSum is superior to UniG in all categories. Therefore there is no category
bias in our method.

Figure 6: Top10 categories of test images and their corresponding performance.

##  Appendix E Guided Attention

We visualize (1) the attention matrix from the $8^{th}$ encoder layer of
CFSum-F3W6S9, whose layer is under the word-level guidance. (2) the attention
matrix from the $11^{th}$ encoder layer of CFSum-F3W6S9, whose layer is under
the phrase-level guidance. The attention matrix is renormalized after removing
[CLS] and [SEP]. They are shown in Figure 7 and Figure 8.

From the attention under the word-level guidance, we can observe that some
input words which generatively or extractively occur in the reference summary
will attend to the image, such as “crash” and “relatives”. From the attention
under the phrase-level guidance, we can observe that some input phrases which
generatively or extractively occur in the reference summary attend to the
image more. Above all, it also proves that two visual complement modules
succeed in providing better encoding to generate summaries.

Figure 7: Visualize $8^{th}$ layer’s attention under the word-level guided
module. The reference summary is “crash investigation begins relatives mourn
eight victims”. $v_{0}\sim v_{9}$ is the image object detected feature. Figure
8: Visualize $11^{th}$ layer’s attention under the word-level guided module.
The reference summary is “crash investigation begins relatives mourn eight
victims”. $v_{0}\sim v_{9}$ is the image object detected feature.


∎

e1e-mail<EMAIL_ADDRESS>
e2e-mail<EMAIL_ADDRESS>
e3e-mail<EMAIL_ADDRESS>
e4e-mail<EMAIL_ADDRESS>
e5e-mail<EMAIL_ADDRESS>
e6e-mail<EMAIL_ADDRESS>
e7e-mail<EMAIL_ADDRESS>
11institutetext: Physics Division, National Center for Theoretical Sciences,
Taipei 10617, Taiwan 22institutetext: ETH Zürich, Institute for Particle
Physics and Astrophysics, CH-8093 Zürich, Switzerland 33institutetext:
Univeristy of Manchester, School of Physics and Astronomy, M13 9PL Manchester,
United Kingdom 44institutetext: Istituto Nazionale di Fisica Nucleare, Sezione
di Roma, Piazzale A. Moro 2, I-00185 Roma, Italy 55institutetext: Istituto
Nazionale di Fisica Nucleare, Laboratori Nazionali di Frascati, C.P. 13, 00044
Frascati, Italy 66institutetext: School of Physics and Technology, Wuhan
University, Wuhan 430072, China

# Uncovering tau leptons-enriched semi-visible jets at the LHC

Hugues Beauchesnee1,addr1 ID Cesare Cazzanigae2,addr2 ID Annapaola de
Cosae3,addr2 ID Caterina Doglionie4,addr3 ID Tobias Fitschene5,addr3 ID
Giovanni Grilli di Cortonae6,addr8,addr9 ID Ziyuan Zhoue7,addr2,addr7 ID

(Received: date / Accepted: date)

###### Abstract

This Letter proposes a new signature for confining dark sectors at the Large
Hadron Collider. Under the assumption of a QCD-like hidden sector, hadronic
jets containing stable dark bound states could manifest in proton-proton
collisions. We present a simplified model with a $Z^{\prime}$ boson yielding
the production of jets made up of dark bound states and subsequently leading
to the decays of those that are unstable to $\tau$ leptons and Standard Model
quarks. The resulting signature is characterised by non-isolated $\tau$ lepton
pairs inside semi-visible jets. We estimate the constraints on our model from
existing CMS and ATLAS analyses. We propose a set of variables that leverage
the leptonic content of the jet and exploit them in a supervised jet tagger to
enhance the signal-to-background separation. Furthermore, we discuss the
performance and limitations of current triggers for accessing sub-TeV
$Z^{\prime}$ masses, as well as possible strategies that can be adopted by
experiments to access such low mass regions. We estimate that with the
currently available triggers, a high mass search can claim a $5\sigma$
discovery (exclusion) of the $Z^{\prime}$ boson with a mass up to 4.5 TeV (5.5
TeV) with the full Run 2 data of the LHC when the fraction of unstable dark
hadrons decaying to $\tau$ lepton pairs is around $50\%$, and with a coupling
of the $Z^{\prime}$ to right-handed up-type quarks of 0.25. Furthermore, we
show that, with new trigger strategies for Run 3, it may be possible to access
$Z^{\prime}$ masses down to 700 GeV, for which the event topology is still
composed of two resolved semi-visible jets.

††journal: Noname

##  1 Introduction

Despite the great success of the Standard Model, there is much evidence that
it is incomplete. The presence of dark matter in the universe (DM)
Bertone:2004pz ; Planck2018  is one of the most tantalising hints for physics
beyond the horizon of the Standard Model (SM). The lack of experimental
confirmation of the most simple hypotheses sought so far PhysRevLett.39.165 ;
Jungman:1995df  connects to the idea of new physics belonging to sectors of
matter barely accessible to Standard Model particles, or _dark sectors_ , that
has not been fully probed at collider experiments. Several Hidden Valley
models Strassler:2006im  predict the existence of hidden dark sectors that are
only weakly coupled to the SM. These models can rather easily accommodate DM
candidates, identifying them with stable particles within the dark sector (see
e.g. Ref. SpierMoreiraAlves:2010err ; Lee:2015gsa ; Okawa:2016wrr ;
Hochberg:2018vdo ; Beauchesne:2018myj ; Bernreuther:2019pfb ;
Beauchesne:2019ato ). Assuming that the dark sector is strongly coupled, and
that the confinement scale of the theory is sufficiently small, the mixture of
stable and unstable dark bound states coming from the hadronization of dark
quarks generates semi-visible jets at collider experiments Cohen:2015toa ;
Cohen:2017pzm ; Beauchesne:2017yhh ; Beauchesne:2018myj ; Bernreuther:2019pfb
; Beauchesne:2019ato ; Cazzaniga:2022hxl . These appear as visible jets of
particles in the detectors, with missing transverse momentum
($\cancel{E}_{\text{T}}$) aligned with the direction of one of the jets. Fully
hadronic semi-visible jets signatures have been proposed in Cohen:2015toa ;
Cohen:2017pzm ; Beauchesne:2017yhh ; Bernreuther:2019pfb  and searched for by
the CMS and ATLAS experiments in s- and t-channel production modes CMS:2021dzg
; ATLAS-CONF-2022-038 . Signatures with semi-visible jets enriched in leptons
from dark photon decays have been recently proposed in Cazzaniga:2022hxl . In
this Letter we present a simplified model developed following Fox:2011qd , in
which a $Z^{\prime}$ mediator acts as a messenger between the visible and dark
sectors, coupling to SM quarks and $\tau$ leptons, enabling the decay of dark
bound states to $\tau$ lepton pairs and leading to a $\tau$-enriched semi-
visible jets (SVJ$\tau$) signature. The effective fraction of invisible
particles inside the jet is expected to be larger with respect to the fully
hadronic case due to the neutrinos from $\tau$ decays, thus making it more
difficult to reconstruct the mediator mass. Electrons and muons are also
likely to be produced within the jet from leptonic $\tau$ decays, leading to
atypical signatures with high neutrino and lepton multiplicities within a
hadronic jet. We find that present searches have limited sensitivity to this
novel signature, and propose a SVJ$\tau$ tagger that leverages the features
from $\tau$ leptons inside the jet.

##  2 Model setup

The general interactions between the $Z^{\prime}$ boson and SM fermions is
given by:

$\displaystyle\mathcal{L}_{\text{SM}}\supset$
$\displaystyle-Z^{\prime}_{\mu}\bar{u}_{i}\gamma^{\mu}(g^{u_{R}}_{ij}P_{R}+g^{u_{L}}_{ij}P_{L})u_{j}$
(1)
$\displaystyle-Z^{\prime}_{\mu}\bar{d}_{i}\gamma^{\mu}(g^{d_{R}}_{ij}P_{R}+g^{d_{L}}_{ij}P_{L})d_{j}$
$\displaystyle-Z^{\prime}_{\mu}\bar{e}_{i}\gamma^{\mu}(g^{e_{R}}_{ij}P_{R}+g^{e_{L}}_{ij}P_{L})e_{j}.$

An explanation of how such couplings can be embedded in gauge invariant
effective theories can be found in Fox:2011qd , while Altmannshofer:2014cfa
gives an example of UV completion in terms of vector fermions. We introduce
dark quarks $q_{vi}$ charged under the non-abelian gauge group
$SU(N)^{\prime}$. Such hidden sector particles interact with the $Z^{\prime}$
boson via the general interactions:

$\displaystyle\mathcal{L}_{q_{v}}\supset-Z^{\prime}_{\mu}\bar{q}_{vi}\gamma^{\mu}(g^{q_{vR}}_{ij}P_{R}+g^{q_{vL}}_{ij}P_{L})q_{vj}.$
(2)

The dark sector will contain pseudo-scalar mesons, which can be expressed via
the matrix $\Pi=\pi_{0}\mathbb{I}+\pi_{a}t_{a}$, where the $t_{a}$ are the
generators of $SU(N)^{\prime}$. Assuming the coupling matrices to SM fermions
to be diagonal, their decay widths to the SM fermions $f_{i}$ will be:

$\displaystyle\Gamma_{\pi_{a}\to
f_{i}\bar{f}_{i}}=\;\frac{N_{c}F_{\pi}^{2}}{32\pi}(\Delta^{f}_{ii}\,\Delta^{q_{v}}_{a})^{2}\frac{m_{\pi^{a}}m_{f_{i}}^{2}}{M_{Z^{\prime}}^{4}}\sqrt{1-\frac{4m_{f_{i}}^{2}}{m_{\pi_{a}}^{2}}},$
(3)

where $N_{c}$ is the number of colours of $f_{i}$, $F_{\pi}$ is the dark pion
decay constant, $M_{Z^{\prime}}$ is the $Z^{\prime}$ boson mass, and we define
$\Delta^{f}_{ii}=g_{ii}^{f_{R}}-g_{ii}^{f_{L}}$,
$\Delta^{q_{v}}_{a}=g^{q_{vR}}_{a}-g^{q_{vL}}_{a}$. The dark couplings are
defined via $g^{q_{vR}}=g^{q_{vR}}_{0}\mathbb{I}+g^{q_{vR}}_{a}t_{a}$ and
$g^{q_{vL}}=g^{q_{vL}}_{0}\mathbb{I}+g^{q_{vL}}_{a}t_{a}$.
More specifically, we will present results for the following benchmark. We
assume two generations of dark quarks that transform under the fundamental
representation of an $SU(3)^{\prime}$ group. The couplings are taken as:

$\displaystyle
g^{d_{R}}_{ij}=g^{d_{L}}_{ij}=g^{u_{L}}_{ij}=g^{e_{L}}_{ij}=0,\quad
g^{u_{R}}_{ij}=g_{u}\delta_{ij},$ (4) $\displaystyle
g^{e_{R}}_{ij}=g_{\tau}\delta_{i3}\delta_{j3},\quad
g^{q_{vR}}_{ij}=g^{q_{vL}}_{ij}=g_{q_{v}}\delta_{ij}.$

While studying the collider signatures, we will assume the presence of some
small deviations of the $g^{q_{vL}}_{ij}$ and $g^{q_{vR}}_{ij}$ couplings to
break the $SU(2)$ flavour symmetry between the two dark quarks and change the
fraction of stable mesons produced. The choice of small $Z^{\prime}$ couplings
to electrons and muons for our benchmark is driven by the strong constraints
from high mass di-lepton searches CMS:2018ipm ; ATLAS:2019erb . A coupling to
light leptons for a $\sim 1$ TeV $Z^{\prime}$ boson mass would be constrained
to be much smaller than the $\tau$ lepton coupling. Finally, the coupling
$g_{q_{v}}$ has been fixed to 0.6, such that the narrow width approximation
holds when one takes into account the number of flavours and colours in the
dark sector as in CMS:2021dzg . We choose to vary $g_{u}/g_{\tau}$ implying
scanning over different values for the branching ratio of unstable dark sector
bound states to $\tau$ leptons ($\text{BR}_{\tau}$).

As a major phenomenological consequence, the proposed simplified model allows
for s-channel production of semi-visible jets from $pp\to Z^{\prime}\to
q_{v}\bar{q}_{v}$ with an enriched $\tau$ lepton content, originating from
dark bound states. Therefore, its final states will be characterised by a
large multiplicity of $\tau$ leptons decaying hadronically or leptonically,
and whose decay products will be clustered in jets with aligned missing
momentum from the stable dark bound states (SVJ$\tau$ signature). In this
study we only consider dark bound states decaying promptly, since the expected
$c\tau_{\text{min}}$ can be so small that displacements between the production
and decay points of unstable dark hadrons cannot be resolved.

##  3 Monte Carlo Simulations

Samples containing signal events, including initial- and final-state radiation
and underlying event, have been produced with Pythia8.307 Bierlich:2022pfr .
The Hidden Valley module Carloni:2011kk ; Carloni:2010tw  in Pythia8 was used
to simulate the showering, hadronization, and decays in the dark sector.
Detector effects are simulated using Delphes3 deFavereau:2013fsa . Following
previous studies CMS:2021dzg , anti-$k_{T}$ jets Cacciari:2008gp ;
Cacciari:2011ma  with $R=0.8$ (AK8 jets) are clustered requiring a minimum
$p_{\text{T}}$ of 200 GeV. Even if no double hadronization process is expected
in case of dark bound states decaying to $\tau$ leptons, the choice of AK8
jets in our study is mainly motivated by a better $Z^{\prime}$ boson mass
resolution. Since the dark bound states can have a large $\Delta R$ separation
from the jet axis due to showering and hadronization in the dark sector,
$\tau$ lepton decay products can be produced at relatively large angles, and a
large jet radius can help containing them. All the samples have been
normalised to the LO cross-section prediction computed with the
MadGraph5_aMC@NLO Alwall:2014hca  event generator using the NN23LO1 parton
distribution functions Ball:2013hta  from the Lhapdf Buckley:2014ana
repository.

For the SVJ${\tau}$ s-channel signal process, $50\cdot 10^{3}$ events have
been generated scanning a mass range for $M_{Z^{\prime}}$ between 0.5 TeV and
6 TeV. The number of stable and unstable dark hadrons produced in the dark
hadronization process can vary according to the details of the dark sector. In
previous literatureCohen:2015toa ; Cohen:2017pzm , an effective invisible
fraction parameter has been defined as
$r_{\text{inv}}=\langle{N_{\text{stable}}}/{(N_{\text{stable}}+N_{\text{unstable}})}\rangle$,
where $N_{\text{stable}}$ is the number of stable dark hadrons, and
$N_{\text{unstable}}$ is the number of those that are unstable, decaying back
to SM. This invisible fraction allows to capture variations in the details of
the hidden sector, and define the parameter space of a semi-visible jet
signature in terms of amount of missing transverse momentum111Other operative
definitions of this parameter are being studied, see e.g. Albouy:2022cin . The
higher $r_{\text{inv}}$, the more the final state topology will resemble a
mono-jet signature, where $\cancel{E}_{\text{T}}$ recoils against initial-
state radiation jets. Due to the effect of the neutrinos produced from $\tau$
leptons originating from DM bound states, the effective invisible fraction
inside the jets is expected to increase. We estimate this increase due to
neutrinos by adding a term to the above definition of invisible fraction of
the type $\langle
f_{\nu}\rangle\times\text{BR}_{\tau}\times(1-r_{\text{inv}})$, where $\langle
f_{\nu}\rangle$ represents the invisible component carried by neutrinos
averaged over all possible $\tau$ lepton decays, and estimated to be $\sim
0.45$. Thus, for a fixed $\text{BR}_{\tau}$, it is possible to determine the
expected experimental signature for SVJ$\tau$ in terms of the alignment of the
missing transverse momentum with the jets. In this work we scan over
$\text{BR}_{\tau}$ and fix $r_{\text{inv}}=0.3$, such that the invisible
fraction, including the neutrino contributions, is smaller then 0.7. This
regime can be probed by a SVJ-like search with event selections similar to
those performed by ATLAS and CMS CMS:2021dzg ; ATLAS-CONF-2022-038 .
The dark pseudo-scalar ($\pi_{v}$) and vector ($\rho_{v}$) meson masses can
differ according to the non-perturbative dynamics of the hidden sector, even
for mass-degenerate dark quarks. Following a similar approach as in
Cazzaniga:2022hxl , we employed the lattice QCD fits in Albouy:2022cin  to
predict the masses of dark vector mesons $m_{\rho_{v}}$ from the input ratio
$m_{\pi_{v}}/\Lambda_{v}$, where $\Lambda_{v}$ (HiddenValley:Lambda) is the
dark confinement scale , which fixes the overall mass scale for the dark bound
states. As a benchmark, $\Lambda_{v}$ has been set to 10 GeV and the ratio
$m_{\pi_{v}}/\Lambda_{v}=0.8$, therefore fixing $m_{\pi}$ (4900111:m0,
4900211:m0) at 8 GeV and $m_{\rho_{v}}$ (4900113:m0, 4900213:m0) at $\sim 25$
GeV. With these dark QCD parameter settings, the $\rho_{v}\to\pi_{v}\pi_{v}$
decay is open, and we assume $100\%$ branching ratio for this internal decay
in the dark sector. As a consequence, only unstable pseudo-scalar dark mesons
are allowed to decay back to the SM, and within the chosen parameter space,
their decays will be mainly into $\tau$ leptons and $c$ quarks.
In summary, there are four parameters sensitive to the details of the dark
sector: the confinement scale $\Lambda_{v}$, the pseudo-scalar mass ratio
$m_{\pi_{v}}/\Lambda_{v}$, the invisible fraction $r_{\text{inv}}$, and the
branching ratio to $\tau$ leptons $\text{BR}_{\tau}$. Moreover, there are two
portal parameters: the $Z^{\prime}$ pole mass $M_{Z^{\prime}}$, and the
coupling to dark quarks $g_{q_{v}}$. In order to probe the impact of enhanced
$\tau$ lepton production, we scan over the branching
$\text{BR}_{\tau}(0.15,0.3,0.55,0.7)$ which is equivalent to varying the ratio
$g_{u}/g_{\tau}$.
Following the same approach as in Cazzaniga:2022hxl , we have considered the
same background processes as in the CMS semi-visible jets search CMS:2021dzg .
All the background samples have been generated at LO with the
MadGraph5_aMC@NLO event generator using the NN23LO1 parton distribution
functions from the Lhapdf repository. The evolution of the parton-level events
and hadronization are performed with Pythia8. The QCD sample ($4.5\cdot
10^{7}$ events) has been produced requiring a generator level cut on the
leading parton jet transverse momentum $p_{\text{T}}>200$ GeV. The QCD
background is particularly relevant due to the large cross-section and the
possibility of mis-reconstruction of the jet momentum leading to additional
missing momentum aligned with the jet axis. The
$\text{t}\bar{\text{t}}+\text{jets}$ inclusive sample ($5\cdot 10^{7}$ events)
has been generated with up to two additional partons. This background mainly
becomes relevant when the top quarks are highly boosted, and therefore the W
boson decay and the b-initiated jet are merged into a larger jet. The
electroweak inclusive backgrounds $\text{Z}(\nu\bar{\nu})+\text{jets}$ and
$\text{W}(\ell\nu)+\text{jets}$ ($2.5\cdot 10^{7}$ events each) have been
produced with a generator level cut $H_{\text{T}}>100$ GeV and including up to
three additional partons in the matrix element.

##  4 Trigger and search strategies

A previous CMS search for fully hadronic SVJ signatures CMS:2021dzg  made it
evident that with the HLT $H_{\text{T}}$ or single jet $p_{\text{T}}$ trigger
thresholds available during Run 2 the possibility to search for low-mass
signatures would be very limited. In the following section several alternative
trigger strategies are simulated and evaluated with respect to their
effectiveness for the SVJ$\tau$ signal presented in this Letter. For this
purpose, the following event selection based on CMS:2021dzg  is applied:

  * •

Number of jets: $N(j^{\text{AK8}})\geq 2$

  * •

Jet separation: $\Delta\eta(j_{1}^{\text{AK8}},j_{2}^{\text{AK8}})<1.5$

  * •

Jet-$\cancel{E}_{\text{T}}$ separation:
$\Delta\phi_{\text{min}}(\cancel{E}_{\text{T}},~{}j^{\text{AK8}})<0.8$

  * •

Veto on mini-isolated leptons: $N_{\mu,e}=0$

In CMS:2021dzg  a criterion on the transverse mass222It is computed from the
4-vector of the di-jet system $(E_{\text{T},jj},\vec{p}_{\text{T},jj})$ and
$(\cancel{E}_{\text{T}},\vec{\cancel{E}_{\text{T}}})$ Cohen:2015toa :
$M^{2}_{\text{T}}=(E_{\text{T},jj}+\cancel{E}_{\text{T}})^{2}-(\vec{p}_{\text{T},jj}+\vec{\cancel{E}_{\text{T}}})^{2}$.
$M_{\text{T}}>1.5$ TeV is applied in addition to this. It is driven by the
requirement of using a fully efficient trigger. This selection however limits
the sensitivity to semi-visible jets at low mass, especially for SVJ$\tau$
signals, where the presence of neutrinos from $\tau$-leptons decay shift the
reconstructed $M_{\text{T}}$ to lower values (see Figure 1)33footnotetext: In
order to emulate trigger effects in the Delphes simulation, the selection on
$p_{\text{T}}$ and $H_{\text{T}}$ was applied to anti-$k_{T}$ $R=0.4$ jets,
while $M_{\text{T}}$ was calculated using $R=0.8$ jets. As expected, this
approach leads to turn-on curves and $M_{\text{T}}$ cut values that are more
optimistic with respect to what is observed in the CMS search. There the full
efficiency point for $M_{\text{T}}=1.5$ TeV was reached using thresholds of
$p_{\text{T}}=500$ GeV and $H_{\text{T}}=1.05$ TeV, while for the same
thresholds in this study we reach full efficiency for $M_{\text{T}}=1.2$ TeV..

Figure 1:  Signal (solid) and background (dotted) distributions for various
$\text{BR}_{\tau}$ hypotheses, for $M_{Z^{\prime}}=1$ and $1.5$ TeV. Also
shown are acceptance, i.e. fraction of events passing a lower threshold on
$M_{\text{T}}$, and the bin-by-bin signal-to-background ratio. For the latter,
all backgrounds considered were added together.The distributions in the top
panel are normalised to have an integral of one, while for the lower panel the
distributions were scaled according to cross-section times branching ratio
calculated for $g_{u}=0.25$. All distributions are shown after applying the
preselection in the text. The axes on the bottom of the plot show the
$M_{\text{T}}$ values at which various trigger selections have been determined
to be 95% efficient in the Delphes simulation with respect to the QCD dijet
sample.

As a result, a selection $M_{\text{T}}>1.5$ TeV, following the current trigger
strategy for s-channel hadronic semi-visible jets as in CMS:2021dzg , discards
most of the signal for benchmark points below 2 TeV.
The possibility of employing $\tau$ triggers similar to those used by ATLAS
and CMS during Run 2 ATLASTauTrig ; CMSTauTrig  was studied as an alternative
(B). These triggers apply calorimeter and track isolation criteria at
different levels of the triggers to select tau candidates, and different
options for isolation regions and track thresholds were studied. It was found
that the isolation requirements impacts the signal acceptance after
preselection, especially for small $\text{BR}_{\tau}$: The acceptance ranges
from $\approx 5~{}\%$ at BR${}_{\tau}=15~{}\%$ to $\approx 35~{}\%$ at
BR${}_{\tau}=0.70~{}\%$, with comparable results for all of the studied mass
points $0.5\leq M_{Z^{\prime}}\leq 5$ TeV. The isolation criterion
$N_{\text{core: }R<0.2}^{\text{tracks: }p_{\text{T}}>1~{}\text{GeV}}\leq 3$
and $N_{\text{iso: }0.2<R<0.4}^{\text{tracks:
}p_{\text{T}}>1~{}\text{GeV}}\leq 1$ defined with a core region with radius
$R=0.2$ was observed to yield generally better acceptances by a factor of
$\approx 1.5$ than with a radius of $R=0.1$.

The isolation criteria could be loosened using di-$\tau$ triggers, similar to
those used in the ATLAS $H\rightarrow\tau\tau$ analysis ATLAS-CONF-2022-032 ,
where the isolation is only applied to tau candidates below 25 GeV. It was
observed however that such a selection rejects significantly more signal
events for low values of $M_{Z^{\prime}}$, without offering a significant
improvement over the single tau trigger.
The invisible component of the SVJ$\tau$ signature also suggests the
investigation of $\cancel{E}_{\text{T}}$ triggers. However, it was observed
that such a trigger would not reach an acceptable efficiency for the selection
of SVJ$\tau$ signal samples with masses below $2$ TeV.
A topological trigger that makes use of the angle between the two jets was
studied and determined to be a more promising approach due to the fact that it
takes into consideration the $s-$channel production of the mediator. This kind
of selection can be applied already at the first trigger level (L1), to reduce
hadronic rates prior to the High Level Trigger (HLT). The presence of a pair
of anti-$k_{T}$ $R=0.4$ jets with $p_{\text{T}}>100$ GeV and an angular
separation of $\Delta\eta<2$ was required in combination with the
$H_{\text{T}}$ HLT trigger as specified above. Comparing the acceptance of QCD
background and signal events with and without the additional $\Delta R$
requirement, a larger decrease in the acceptance was observed for the
background. The QCD background acceptance is reduced by about 30% by imposing
the topological requirement on an $H_{\text{T}}$ trigger threshold of $600$
GeV, and similar reductions are obtained for higher thresholds. These studies
suggests that topological trigger strategies might aid in lowering the
$H_{\text{T}}$ trigger thresholds.
Hadronic trigger thresholds can also be lowered by using alternative data-
taking workflows such as Data Parking CMS-DP-2012-022  (delayed stream in
ATLAS ATLAS:2014ktg ), or Data Scouting 2017520  (Trigger-Level Analysis in
ATLAS PhysRevLett.121.081801 ). In Run 2, the latter technique only retained
global information about the jets reconstructed at the HLT. For additional
sensitivity to these models using the selections described in the next
section, these techniques will need to be supplemented with variables or full
event information in the detector region corresponding to the jet. In the
following analysis, we will use a hypothetical target $M_{\text{T}}$ cut as
low as $800$ GeV for low-mass signals, corresponding to an $H_{\text{T}}$
trigger threshold of $600$ GeV, according to Figure 1. Further studies to
demonstrate the feasibility of such a threshold (using e.g. the topological
$H_{\text{T}}$ trigger at L1, in combination with a tau selection at the HLT,
or employing alternative data-taking workflows) are required due to the
approximations inherent in the simulation setup used for these studies.

##  5 Lepton flavour-based jet tagger

Figure 2: Performance of the Boosted Decision Tree jet tagger for
$M_{Z^{\prime}}=3$ TeV. AUCs are reported in the legend for different values
of $\text{BR}_{\tau}$.  Figure 3: Muon input variables with highest
$S/\sqrt{S+B}$ separation used for training the jet tagger. (a)
$\pi/\gamma$-based isolation, (b) normalised radial distance, (c) inter-
isolation. All distributions shown refer to the training sample and are
normalised to one.

The selection requirements employed by the CMS semi-visible jets search
CMS:2021dzg  rely on event-level variables or basic kinematic properties of
the reconstructed jets and missing transverse momentum. Even if this selection
rejects the vast majority of the SM background events (Table 1, A), in the
case of relatively large $\text{BR}_{\tau}$, it does not capture the
peculiarities of the SVJ$\tau$ signature, with larger lepton multiplicity,
additional missing momentum from neutrinos, and larger spread of the jet
constituents due to $\tau$ decays. In order to reduce the background further
and enhance the sensitivity of the search, we develop here a novel jet tagging
algorithm leveraging the specific features of signal jets. In this work we
propose a set of new variables able to capture the atypical lepton content if
the signal jets due to $\tau$ lepton decays. Indeed, since the signal is
enriched in $\tau$ leptons, and $\sim 35\%$ of them is expected to decay to
electrons and muons, we expect a relevant contribution of light leptons
enriching the jet signature. Compared to the SVJ-$\ell$ signature presented in
Cazzaniga:2022hxl , the leptonic fraction in the jets is lower, and the
$p_{\text{T}}$ distribution for electrons and muons is expected to be much
softer due to the presence of neutrinos in $\tau$ decays. The signature
addressed here is therefore more challenging. An adequate background reduction
requires a good exploitation of multiple properties of the signal leptons. In
our lepton flavour-based jet tagger we exploit 3 main features of electrons
and muons (labelled $\ell=e,\mu$) in signal jets: energy and momentum flow,
spatial distribution and isolation. For the first category of variables energy
fractions ($f(\ell)$) are used together with the average lepton $p_{\text{T}}$
normalised by the jet transverse momentum ($p_{\text{T,norm}}(\ell)$). The
second category of variables includes the radial distribution of electrons and
muons with respect to the jet axis normalised by the jet radius
($R_{norm}(\ell)$). Finally, the third class of variables includes the inter-
isolation ($\text{I}_{\text{int}}(\ell)$) proposed in Cazzaniga:2022hxl , as
well as a new isolation variable: $I_{\pi\gamma}(\ell)$. The latter is built
summing the momenta of all pions and photons reconstructed within a cone of
given radius centred around the lepton itself, and then normalising to lepton
$p_{\text{T}}$. This variable aims to capture $\pi_{v}\to\tau(h)\tau(\ell)$
when the dark hadron $\pi_{v}$ is boosted enough and the $\tau(h)$ products
overlaps with the lepton from $\tau(\ell)$.
Variables sensitive to the multiplicity of jet constituents are deliberately
excluded, as this property is strongly correlated with the details of the dark
sector.

These variables are used as input to a Boosted Decision Tree (BDT). The BDT is
trained using the TMVA machine learning package Hocker:2007ht  with the
adaptive boosting technique. The inputs are the two highest $p_{\text{T}}$
jets from simulated signal and background samples, with the variables
described above computed for each jet. To improve the performance of the
classifier, its hyperparameters have been optimised for a benchmark signal
point at $M_{Z^{\prime}}=3$ TeV mixing different $\text{BR}_{\tau}$
hypotheses. Each signal sample is weighted equally. Moreover, in order to
lower correlation to leptons multiplicity, which is highly dependent on the
details of the model, only signal and background jets with non-zero leptonic
content have been considered for the training.
The BDT exhibits strong rejection of jets from the SM background processes,
while preserving a significant amount of signal jets. The receiver operator
characteristic curves (ROC) for a benchmark mass point of $M_{Z^{\prime}}=3$
TeV and different values of $\text{BR}_{\tau}$ are shown in Figure 2. It is
expected that the performance of the tagger decreases with smaller values of
$\text{BR}_{\tau}$, since the chosen input variables aim to exploit the
enhanced fraction of electrons and muons due to decay processes
$\tau\to\ell\bar{\nu}_{\ell}\nu_{\tau}$ occurring inside the jet. The three
most important variables in the BDT, thus those with the highest separation as
defined in Hocker:2007ht , are found to be $\text{I}_{\text{int}}(\ell)$,
$R_{norm}(\ell)$ and $I_{\pi\gamma}(\ell)$. The distributions for such
variables for muons are shown in Figure 3.
We choose a working point (WP) corresponding to a threshold of 0.05 on the
discriminator output. Jets with a discriminator value higher than 0.05 are
labelled as semi-visible. This threshold has been chosed to maximise the
significance $s/\sqrt{s+b}$, where $s$ and $b$ are the expected number of
signal and background events passing the BDT cut, respectively. At this WP,
the BDT rejects $\sim 97\%$ of simulated background jets, while correctly
classifying $\sim 80\%,84\%,90\%,93\%$ of jets from the benchmark signal
models at $M_{Z^{\prime}}=3$ TeV for $\text{BR}_{\tau}=0.15,0.3,0.55,0.7$,
respectively.
It has been verified that the set of variables presented here present higher
discrimination power with respect to the jet-substructure variables used in
CMS:2021dzg .

Figure 4: (a) Expected limits on $\sigma\times BR(Z^{\prime}\to
q_{v}\bar{q}_{v})$ for the signal benchmark with $r_{\text{inv}}=0.3$ and
$\text{BR}_{\tau}=0.55$. The low-mass region ($700\leq M_{Z^{\prime}}\leq
1500$ GeV) accessible in Run 3 when requiring $M_{\text{T}}>800$ GeV is
separated by a vertical magenta line from the high mass search region
($M_{Z^{\prime}}\geq 1500$ GeV) accessible using the same triggers as in
CMS:2021dzg  for Run 2. The expected exclusion reach of the fully hadronic SVJ
inclusive analysis for the SVJ$\tau$ for the SVJ$\tau$ signature (dashed blue
line) is compared with the proposed SVJ$\tau$ BDT-based analysis (black dashed
line), exploiting the BDT jet tagger introduced in section 5. The theoretical
cross-section (red line) has been computed for a benchmark $g_{u}=0.25$ and
$g_{q_{v}}=0.6$. The purple line corresponds to the expected $5\sigma$
discovery line. (b) Current constraints and expected limits of the BDT-based
analysis as a function of the mass of the mediator $M_{Z^{\prime}}$ for
$\text{BR}_{\tau}=0.55$, $r_{\text{inv}}=0.3$ and $g_{q_{v}}=0.6$. The black
line corresponds to the SVJ$\tau$ BDT-based analysis.

##  6 Results

We report in this section the sensitivity and exclusion reach for the
SVJ$\tau$ signal, both in the case of a high mass search, like the one
reported by the CMS collaboration for Run 2 CMS:2021dzg , thus keeping the
same requirement $M_{\text{T}}>1500$ GeV, and with a new trigger strategy for
Run 3 requiring a reference selection $M_{\text{T}}>800$ GeV to probe lower
$M_{Z^{\prime}}$ masses. We have estimated the expected exclusion limit at 95%
confidence level (CL) for $\sigma\times\text{BR}$ for different
$M_{Z^{\prime}}$ and $\text{BR}_{\tau}$ hypotheses performing a binned
likelihood template fit of the $M_{\text{T}}$ spectrum and using the modified
frequentist approach $\text{CL}_{\text{s}}$ in the asymptotic approximation
Junk:1999kv ; Read:2002hq ; Cowan:2010js . The sensitivity reach has been
estimated for events selected according to the strategy defined in section 5.
We have combined two categories of events according to the jet-level decision
of the tagger at the optimal WP (as introduced in section 6): $0$-tag
category, where no jets are tagged as semi-visible, and $1/2$-tag category
where at least one jet is tagged as semi-visible.
We consider a benchmark point with $r_{\text{inv}}=0.3$ and
$\text{BR}_{\tau}=0.55$. For the high mass search, the LHC with full Run 2
collision data ($\sqrt{s}=13$ TeV, $\mathcal{L}_{int}=$ 138 $\text{fb}^{-1}$)
and following the BDT-based approach proposed here is expected to claim the
discovery (exclusion) of an hypothetical $Z^{\prime}$ boson with SM couplings
as described in section 2 up to masses of $\sim 4.5$ TeV ($5.5$ TeV) for a
benchmark $g_{u}=0.25$. We show that, the search could be able to probe
mediator masses down to 700 GeV with full Run 3 LHC collision data
($\sqrt{s}=13.6$ TeV, $\mathcal{L}_{int}=$ 250 $\text{fb}^{-1}$ ). More
details are shown in Figure 4.a. The sensitivity to the signal diminishes with
higher values of $\text{BR}_{\tau}$ due to the decreasing $M_{\text{T}}$
resolution. Furthermore, Figure 4.a, shows how the inclusive CMS SVJ search
CMS:2021dzg  has no sensitivity to SVJ$\tau$ signals, while the jet tagger
proposed in this Letter allows probing values of the coupling $g_{u}$ even
smaller than the benchmark 0.25, for the entire $M_{Z^{\prime}}$ mass range
tested.
Figure 4.b shows the expected limits of the BDT-based analysis on the coupling
$g_{u}$ for the same benchmark, together with previous experimental
constraints. Six searches are included: the dijet search by ATLAS
ATLAS:2019fgd , the dijet search by CMS CMS:2019gwf , the ditau search by
ATLAS ATLAS:2017eiz , the SVJ search by CMS CMS:2021dzg , the trigger-object-
level analysis (TLA) search by ATLAS ATLAS:2018qto  and the dijet data-
scouting search by CMS CMS:2018mgb . Dijet searches only include jets
originating from SM quarks, and their constraints are conservative as some SVJ
would also have passed the selection cuts (a detailed study is beyond the
scope of this work). In general, if the $Z^{\prime}$ also decays to dark
quarks, the branching ratios to SM jets and taus will be reduced, weakening
the constraints from dijet and ditau searches. As can be seen, the proposed
search strategy vastly outperforms previous searches.

##  7 Discussion and conclusions

In our study, we have investigated the possibility to access lower
$Z^{\prime}$ boson masses by lowering the $M_{\text{T}}$ requirement. We have
found that topological triggers based on $H_{\text{T}}$, $\tau$ triggers for
high tau branching ratios, and alternative data-taking techniques such as data
scouting or parking can be promising to achieve an increased sensitivity with
respect to the current trigger strategy.
Finally, we provided the expected sensitivity reach and exclusion limits
performing a bump hunt in the dijet transverse mass both in the case of a high
mass search fulfilling the CMS trigger requirements for the Run 2 SVJ search
CMS:2021dzg , and also for a lower mass search exploiting a different trigger
strategy for Run 3 LHC data taking. For the high mass search, the BDT-based
analysis can claim the discovery (exclusion) of a $Z^{\prime}$ mediator as
defined in section 2 up to masses of 4.5 TeV (5.5 TeV) with full Run2
collision data when the fraction of unstable dark hadrons decaying to $\tau$
lepton pairs is around $50\%$ and with a benchmark coupling $g_{u}=0.25$. For
the same benchmark parameter space, we estimate that our BDT-based strategy is
also capable to probe $Z^{\prime}$ boson masses down to 700 GeV when lowering
the $M_{\text{T}}$ requirement down to 800 GeV.

###### Acknowledgements.

We thank F. Eble and R. Seidita for maintenance of the framework used in the
analysis, and revising the manuscript providing useful comments. C. Cazzaniga
and A. de Cosa are supported by the Swiss National Science Fundation (SNFS)
under the SNSF Eccellenza program. H. Beauchesne is supported by the National
Science and Technology Council, the Ministry of Education (Higher Education
Sprout Project NTU-111L104022), and the National Center for Theoretical
Sciences of Taiwan. G.G.d.C. is supported by the INFN Iniziativa Specifica
Theoretical Astroparticle Physics (TAsP) and by the Frascati National
Laboratories (LNF) through a Cabibbo Fellowship, call 2019. C. Doglioni and T.
Fitschen’s research is part of a project that has received funding from the
European Research Council under the European Union’s Horizon 2020 research and
innovation program (grant agreement 101002463).

##  Appendix A Event selection

The event selection on fully hadronic semi-visible jets used in this work is
shown in the first column of Table 1. This is based on a previous CMS study
CMS:2021dzg . The last three columns shows the signal efficiencies for
different values of BRτ and a benchmark mass point $M_{Z^{\prime}}=3$ TeV.

Selection | Signal efficiency ($\%$)
---|---
$\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\text{BR}_{\tau}:$ | 0.7 | 0.55 | 0.15
$N(AK8jets)\geq 2$ | 71.8 | 76.5 | 85.7
$\Delta\eta(j_{1},j_{2})<1.5$ | 51.5 | 53.5 | 56.6
$M_{\text{T}}>1500$ GeV | 16.2 | 25.5 | 42.7
$R_{\text{T}}>$ 0.15 | 10 | 14.3 | 20.2
$\Delta\phi_{\text{min}}(\cancel{E}_{\text{T}},j)<$ 0.8 | 9.3 | 13.6 | 19.5
$N_{\mu,e}$ = 0 | 5.7 | 9.2 | 17.3
Table 1: CMS SVJ search CMS:2021dzg  selections applied to SVJ$\tau$ signal
for a benchmark mass point $M_{Z^{\prime}}=3$ TeV.

##  Appendix B Acceptance for $\tau$ Triggers

Figure 5 shows the signal acceptance after the isolation criterion and the
preselection as specified in Sec. 4 for different BRτ and $M_{Z^{\prime}}$
with respect to the single- and di-$\tau$ triggers specified in the same
section.

Figure 5:  [top]: Signal acceptance for events fulfilling the criteron of at
least one particle level $\tau$ fulfilling the single $\tau$ trigger isolation
criterion of $1\leq N_{\text{core}}^{\text{tracks}}\leq 3$ and $1\leq
N_{\text{iso}}^{\text{tracks}}$ with $R_{\text{core}}=0.1$ [bottom]: Signal
acceptance for events fulfilling the di-$\tau$ trigger requirement of
$p_{\text{T}}(\tau^{\text{lead}})>35$ GeV and
$p_{\text{T}}(\tau^{\text{sublead}})>25$ GeV at particle level for various
mass points $M_{Z^{\prime}}$ as well as branching ratios $\text{BR}_{\tau}$.
The preselection as defined in 4 has been applied.

## References

  * (1) G. Bertone, D. Hooper, J. Silk, Phys. Rept. 405, 279 (2005).  DOI 10.1016/j.physrep.2004.08.031
  * (2) N. Aghanim, Y. Akrami, M. Ashdown, J. Aumont, C. Baccigalupi, M. Ballardini, A.J. Banday, R.B. Barreiro, N. Bartolo, S. Basak, R. Battye, K. Benabed, et al., Astronomy & Astrophysics 641, A6 (2020).  DOI 10.1051/0004-6361/201833910.  URL http://dx.doi.org/10.1051/0004-6361/201833910
  * (3) B.W. Lee, S. Weinberg, Phys. Rev. Lett. 39, 165 (1977).  DOI 10.1103/PhysRevLett.39.165.  URL https://link.aps.org/doi/10.1103/PhysRevLett.39.165
  * (4) G. Jungman, M. Kamionkowski, K. Griest, Phys. Rept. 267, 195 (1996).  DOI 10.1016/0370-1573(95)00058-5
  * (5) M.J. Strassler, K.M. Zurek, Phys. Lett. B 651, 374 (2007).  DOI 10.1016/j.physletb.2007.06.055
  * (6) D. Spier Moreira Alves, S.R. Behbahani, P. Schuster, J.G. Wacker, JHEP 06, 113 (2010).  DOI 10.1007/JHEP06(2010)113
  * (7) H.M. Lee, M.S. Seo, Phys. Lett. B 748, 316 (2015).  DOI 10.1016/j.physletb.2015.07.013
  * (8) S. Okawa, M. Tanabashi, M. Yamanaka, Phys. Rev. D 95(2), 023006 (2017).  DOI 10.1103/PhysRevD.95.023006
  * (9) Y. Hochberg, E. Kuflik, H. Murayama, Phys. Rev. D 99(1), 015005 (2019).  DOI 10.1103/PhysRevD.99.015005
  * (10) H. Beauchesne, E. Bertuzzo, G. Grilli Di Cortona, JHEP 04, 118 (2019).  DOI 10.1007/JHEP04(2019)118
  * (11) E. Bernreuther, F. Kahlhoefer, M. Krämer, P. Tunney, JHEP 01, 162 (2020).  DOI 10.1007/JHEP01(2020)162
  * (12) H. Beauchesne, G. Grilli di Cortona, JHEP 02, 196 (2020).  DOI 10.1007/JHEP02(2020)196
  * (13) T. Cohen, M. Lisanti, H.K. Lou, Phys. Rev. Lett. 115(17), 171804 (2015).  DOI 10.1103/PhysRevLett.115.171804
  * (14) T. Cohen, M. Lisanti, H.K. Lou, S. Mishra-Sharma, JHEP 11, 196 (2017).  DOI 10.1007/JHEP11(2017)196
  * (15) H. Beauchesne, E. Bertuzzo, G. Grilli Di Cortona, Z. Tabrizi, JHEP 08, 030 (2018).  DOI 10.1007/JHEP08(2018)030
  * (16) C. Cazzaniga, A. de Cosa, Eur. Phys. J. C 82(9), 793 (2022).  DOI 10.1140/epjc/s10052-022-10775-2
  * (17) A. Tumasyan, et al., JHEP 06, 156 (2022).  DOI 10.1007/JHEP06(2022)156
  * (18) Search for non-resonant production of semi-visible jets using Run 2 data in ATLAS.  Tech. rep., CERN, Geneva (2022).  URL https://cds.cern.ch/record/2815284
  * (19) P.J. Fox, J. Liu, D. Tucker-Smith, N. Weiner, Phys. Rev. D 84, 115006 (2011).  DOI 10.1103/PhysRevD.84.115006
  * (20) W. Altmannshofer, S. Gori, M. Pospelov, I. Yavin, Phys. Rev. D 89, 095033 (2014).  DOI 10.1103/PhysRevD.89.095033
  * (21) A.M. Sirunyan, et al., JHEP 06, 120 (2018).  DOI 10.1007/JHEP06(2018)120
  * (22) G. Aad, et al., Phys. Lett. B 796, 68 (2019).  DOI 10.1016/j.physletb.2019.07.016
  * (23) C. Bierlich, et al., (2022).  DOI 10.48550/ARXIV.2203.11601
  * (24) L. Carloni, J. Rathsman, T. Sjostrand, JHEP 04, 091 (2011).  DOI 10.1007/JHEP04(2011)091
  * (25) L. Carloni, T. Sjostrand, JHEP 09, 105 (2010).  DOI 10.1007/JHEP09(2010)105
  * (26) J. de Favereau, C. Delaere, P. Demin, A. Giammanco, V. Lemaître, A. Mertens, M. Selvaggi, JHEP 02, 057 (2014).  DOI 10.1007/JHEP02(2014)057
  * (27) M. Cacciari, G.P. Salam, G. Soyez, JHEP 04, 063 (2008).  DOI 10.1088/1126-6708/2008/04/063
  * (28) M. Cacciari, G.P. Salam, G. Soyez, Eur. Phys. J. C 72, 1896 (2012).  DOI 10.1140/epjc/s10052-012-1896-2
  * (29) J. Alwall, R. Frederix, S. Frixione, V. Hirschi, F. Maltoni, O. Mattelaer, H.S. Shao, T. Stelzer, P. Torrielli, M. Zaro, JHEP 07, 079 (2014).  DOI 10.1007/JHEP07(2014)079
  * (30) R.D. Ball, V. Bertone, S. Carrazza, L. Del Debbio, S. Forte, A. Guffanti, N.P. Hartland, J. Rojo, Nucl. Phys. B 877, 290 (2013).  DOI 10.1016/j.nuclphysb.2013.10.010
  * (31) A. Buckley, J. Ferrando, S. Lloyd, K. Nordström, B. Page, M. Rüfenacht, M. Schönherr, G. Watt, Eur. Phys. J. C 75, 132 (2015).  DOI 10.1140/epjc/s10052-015-3318-8
  * (32) G. Albouy, et al., Eur. Phys. J. C (2022).  DOI 10.1140/epjc/s10052-022-11048-8.  URL https://doi.org/10.48550/arXiv.2203.09503
  * (33) The ATLAS Tau Trigger in Run 2.  Tech. rep., CERN, Geneva (2017).  URL http://cds.cern.ch/record/2274201
  * (34) O. Davignon, Nuclear and Particle Physics Proceedings 287-288, 107 (2017).  DOI https://doi.org/10.1016/j.nuclphysbps.2017.03.055.  URL https://www.sciencedirect.com/science/article/pii/S2405601417301153.  The 14th International Workshop on Tau Lepton Physics
  * (35) Measuring $CP$ properties of Higgs boson interactions with $\tau$ leptons with the ATLAS detector.  Tech. rep., CERN, Geneva (2022).  URL https://cds.cern.ch/record/2809728
  * (36) Data Parking and Data Scouting
at the CMS Experiment (2012).  URL https://cds.cern.ch/record/1480607

  * (37) G. Aad, et al., Phys. Rev. D 91(5), 052007 (2015).  DOI 10.1103/PhysRevD.91.052007
  * (38) T.C. Collaboration, Physics Letters B 769, 520 (2017).  DOI https://doi.org/10.1016/j.physletb.2017.02.012.  URL https://www.sciencedirect.com/science/article/pii/S0370269317301028
  * (39) A. Collaboration, Phys. Rev. Lett. 121, 081801 (2018).  DOI 10.1103/PhysRevLett.121.081801.  URL https://link.aps.org/doi/10.1103/PhysRevLett.121.081801
  * (40) A. Hocker, et al., (2007).  URL https://doi.org/10.48550/arXiv.physics/0703039
  * (41) T. Junk, Nucl. Instrum. Meth. A 434, 435 (1999).  DOI 10.1016/S0168-9002(99)00498-2
  * (42) A.L. Read, J. Phys. G 28, 2693 (2002).  DOI 10.1088/0954-3899/28/10/313
  * (43) G. Cowan, K. Cranmer, E. Gross, O. Vitells, Eur. Phys. J. C 71, 1554 (2011).  DOI 10.1140/epjc/s10052-011-1554-0.  [Erratum: Eur.Phys.J.C 73, 2501 (2013)]
  * (44) G. Aad, et al., JHEP 03, 145 (2020).  DOI 10.1007/JHEP03(2020)145
  * (45) A.M. Sirunyan, et al., JHEP 05, 033 (2020).  DOI 10.1007/JHEP05(2020)033
  * (46) M. Aaboud, et al., JHEP 01, 055 (2018).  DOI 10.1007/JHEP01(2018)055
  * (47) M. Aaboud, et al., Phys. Rev. Lett. 121(8), 081801 (2018).  DOI 10.1103/PhysRevLett.121.081801
  * (48) A.M. Sirunyan, et al., JHEP 08, 130 (2018).  DOI 10.1007/JHEP08(2018)130