# Combining Knowledge Graphs and Large Language Models

Amanda Kau
Australian National University
Canberra, Australia
<EMAIL_ADDRESS>
Xuzeng He11footnotemark: 1
Australian National University
Canberra, Australia
<EMAIL_ADDRESS>
Aishwarya Nambissan
Australian National University
Canberra, Australia
<EMAIL_ADDRESS>
Aland Astudillo
Swinburne University of Technology
Victoria, Australia
<EMAIL_ADDRESS>
Hui Yin
Swinburne University of Technology
Victoria, Australia
<EMAIL_ADDRESS>
Amir Aryani
Swinburne University of Technology
Victoria, Australia
<EMAIL_ADDRESS>
These authors contributed equally to this work.

###### Abstract

In recent years, Natural Language Processing (NLP) has played a significant
role in various Artificial Intelligence (AI) applications such as chatbots,
text generation, and language translation. The emergence of large language
models (LLMs) has greatly improved the performance of these applications,
showing astonishing results in language understanding and generation. However,
they still show some disadvantages, such as hallucinations and lack of domain-
specific knowledge, that affect their performance in real-world tasks. These
issues can be effectively mitigated by incorporating knowledge graphs (KGs),
which organise information in structured formats that capture relationships
between entities in a versatile and interpretable fashion. Likewise, the
construction and validation of KGs present challenges that LLMs can help
resolve. The complementary relationship between LLMs and KGs has led to a
trend that combines these technologies to achieve trustworthy results. This
work collected 28 papers outlining methods for KG-powered LLMs, LLM-based KGs,
and LLM-KG hybrid approaches. We systematically analysed and compared these
approaches to provide a comprehensive overview highlighting key trends,
innovative techniques, and common challenges. This synthesis will benefit
researchers new to the field and those seeking to deepen their understanding
of how KGs and LLMs can be effectively combined to enhance AI applications
capabilities.

##  1 Introduction

The rapid advancement of natural language processing (NLP) in recent years can
be attributed to the availability of large datasets and the surge in computing
power. Consequently, numerous large language models (LLMs) have been
developed, such as Google’s BERT [1] and T5 [2], OpenAI’s GPT series[3]. LLMs
are widely used in various tasks, including language translation, content
creation, and virtual assistants. They excel in text generation, enabling
applications such as automated essay writing, report generation, and creative
storytelling. LLMs provide highly accurate translations in language
translation, facilitating communication across different languages. They are
also used in chatbots and customer service to handle queries efficiently and
provide personalized responses. Additionally, LLMs assist in summarizing large
volumes of text, extracting key information from documents, and performing
sentiment analysis to learn public opinion. The release of OpenAI’s GPT-3 in
2020, with its 175 billion parameters, significantly boosted public interest
due to its remarkable performance across these diverse applications [4].

However, the knowledge in LLMs is frozen in their parameters at the time of
training, leading to several limitations. These models tend to generate
inaccurate or nonsensical information (hallucinations), need more detailed
expertise in specific domains, particularly regarding new knowledge that
emerges after their training and is often unclear in their decision-making
processes (lack of interpretability). Numerous research efforts have been
dedicated to incorporating alternative knowledge sources, such as linguistic,
retrieval-based, and graph-based knowledge, to enhance language models [5, 6,
7]. These types of models are termed knowledge-enhanced pre-trained models
(KEPLMs). In 2021, Wei et al. [5] surveyed various KEPLMs and their improved
performance over vanilla pre-trained models (PLMs). In 2022, Zhen et al. [6]
classified knowledge enhancement approaches into explicit and implicit
incorporation methods. While explicit methods inserted relevant knowledge into
LLMs by modifying model inputs and employing external memories, implicit
methods focused on the knowledge contained within LLMs from training, like in
BERT [1], which understood the contextual knowledge of words. In the next
year, Hu et al. [7] surveyed KEPLMs, focusing on two key tasks in NLP: Natural
Language Understanding and Natural Language Generation. While these earlier
works touched on diverse external knowledge sources, a more recent review in
2024 by Yang et al. [8] focused solely on injected knowledge from knowledge
graphs (KGs). There has been a growing focus on KGs as sources of structured
knowledge for LLM-based models. The intuitive structures of KGs effectively
represent real-world knowledge by representing entities with nodes and
relationships between them as edges, which enables a greater understanding of
a word’s semantics via its context. Consequently, LLMs can effectively recall
facts, particularly in specific domains.

In fact, the utilization of Knowledge Graphs (KGs) is constrained by the
availability of existing graphs. KGs are difficult, costly, and time-consuming
to construct, requiring numerous steps such as entity extraction, knowledge
fusion, and coreference resolution. Moreover, KGs are specific to each domain,
so separate ones are constructed for each application and may become
irrelevant with time if not updated as knowledge evolves [9]. Several works
employ LLMs to enhance the KG construction process, but they use LLMs simply
as information extractors. One example is a KG for the service domain BEAR
[10], which was created by prompting ChatGPT to extract content from
unstructured data to populate an existing ontology.

In previous surveys [5, 6, 7, 8], there was an emphasis on utilising KGs as
knowledge sources to support LLMs. KGs could provide external facts to LLMs,
not necessarily only for LLMs’ pre-training, but as retrieved facts to ground
LLMs as well. However, there was less focus on the benefits that LLMs could
bring to KGs. On the other hand, in methods like the previously mentioned BEAR
[10], LLMs were used merely to parse and extract relevant information from
documents for KG construction, ignoring other benefits LLMs could bring to KG
construction. The closest work to ours is a recent, comprehensive survey by
Khorashadizadeh et al. [11] outlining the mutual benefits between LLMs and
KGs. However, unlike previous surveys highlighting knowledge injection as the
sole benefit KGs bring to LLMs, this paper will delve into the other benefits
that KGs provide. Furthermore, while [11] classified LLM-KG cooperation
methods by their uses, this paper seeks to explore the different ways that
LLMs and KGs can be jointly used.

In fact, this integration allows for better performance across a series of NLP
tasks, such as named entity recognition and relation classification. Motivated
by the diversity of ways KGs and LLMs can be employed in conjunction, we
propose the following research questions:

  * •

(RQ1) How can KGs be utilised to enhance the capabilities of LLMs?

  * •

(RQ2) In what ways can LLMs be leveraged to support and enhance KGs

  * •

(RQ3) Are there more advantages if models combine KGs and LLMs in a more joint
fashion?

To answer these questions, we conducted an arXiv search for articles related
to LLMs and KGs published no more than five years ago. We conducted our search
from February 2024 to May 2024, and we chose arXiv as the main source of our
review as it includes a wide range of articles. To identify relevant papers,
we reviewed the title and abstract of each article by searching for keywords
such as “Large Language Model” or “Knowledge Graph”, and we considered
articles relevant if they reported on original research related to both LLMs
and KGs, either with a topic on LLMs empowered by KGs, KGs empowered by LLMs
or some Hybrid approaches.

To present the results of our search, we begin with a brief background of LLMs
and KGs, including previous surveys, provided in Section 2. The subsequent
sections correspond to the research questions posed and detail methods
including KGs added to LLMs in Section 3, KGs empowered by LLMs in Section 4,
and some hybrid approaches in Section 5. A thematic analysis is provided in
Section 6, followed by discussions in Section 7 we review the described
methodologies’ different aspects, advantages, and limitations. Finally, the
concluding remarks in Section 8 are where we close our review by presenting a
general analysis and current and future challenges in this field.

##  2 Background

###  2.1 Large Language Models (LLMs)

Various PLMs have been released in the past, including BERT [1] and GPT-1 [3].
BERT (Bidirectional Encoder Representations from Transformers) was released in
2018, featuring a transformer-based model that understood contexts
bidirectionally [1]. The model could consider both the preceding and following
words when processing input text, allowing it to accurately capture the
meaning of words and sentences. The first GPT (Generative Pre-trained
Transformer) was also released in the same year, which focused on generating
text by predicting the next word [3]. Further developments saw the success of
extending PLMs to LLMs by increasing their size and complexity, giving rise to
their remarkable ability to comprehend natural language.

LLMs nowadays are all based on the transformers architecture [12], which
excels in handling long sequences due to its characteristic self-attention
mechanism. LLMs generally take a sequence of text or code called a prompt as
the input. The tokenizer, a part of the LLM architecture, subsequently
converts the input into a list of tokens and feeds them into the model, where
each token is a word in the input prompt. New tokens are generated one by one
until they reach a special token indicating the end of the generation, or the
total length exceeds the limit. These generated tokens are converted back into
the text format as the final output of the model. Figure 1 shows a simple
scheme of this workflow. This process can be formally modelled through some
equations. The input prompt, after being tokenized, can be seen as a list of
tokens $x$, where $x$ = [$x_{1}$, . . . , $x_{n}$] if there are $n$ tokens in
total. LLMs based on the transformer architecture usually maintain a sequence
of hidden states. At step $t$, hidden state $h_{t}$ can be calculated using
the current token $x_{t}$ and all the previous hidden states:

$h_{t}=LLM(x_{t},[h_{0},\ldots,h_{n-1}])$ (1)

The model then further transforms $h_{t}$ into a probability distribution,
which can be used to sample the next generated token:

$P(x)=\prod_{t=1}^{|x|}P(x_{t}|[h_{0},\ldots,h_{t-1}])$ (2)

In this case, due to its ability to recognise, summarise, translate, predict,
and generate text and other forms of content, current LLMs have a wide range
of applications, including question answering and code generation. More
recently, some focus has been directed towards multimodal LLMs, which give
LLMs abilities like the capabilities of vision in Google’s Gemini [13] and
GPT-4 with vision (GPT-4V) [14].

Figure 1: LLM text generation workflow.

###  2.2 Knowledge Graphs (KGs)

A KG is a directed labelled graph in which nodes represent any real-world
entity or concept, and edges represent the relationships between nodes. This
structured data format has proven effective and applicable to a wide variety
of domains, including biology and finance, as well as in modelling social
networks or storing general information like in the Google Knowledge Graph.
Aside from being able to model relationships, KGs can provide further insight
into a word’s semantics via its context or neighbouring nodes. The graph
structure can also be studied, as was done in [15] to uncover research trends
over time, or in [16] where structure provided insight into the impact and
success of research publications. Data in KGs is usually presented as a
(subject, object, predicate) triple. This could be extended in the case of
temporal knowledge graphs, which use a quadruple representation (subject,
object, predicate, timestamp) to capture facts over time.

The construction of a KG involves three general steps: knowledge acquisition,
knowledge refinement, and knowledge evolution [17]. Knowledge acquisition
involves collecting information about entities and relations from multi-
structured data to build the KG. Since extracted triples could be incomplete,
the next knowledge refinement step fixes these issues with additional data.
Finally, the evolution of real-world knowledge over time may not be reflected
in the static KGs that were built, so the graphs are dynamically updated with
the knowledge evolution step. KG construction methods are based on
crowdsourcing or text mining. Crowdsourcing-based KGs like WordNet [18] and
ConceptNet [19] require significant human labour to construct as they depend
on contributions from volunteers. Meanwhile, KGs constructed by text mining
utilise a series of subtasks like named entity recognition and relationship
extraction to extract graph data from text. The resulting KGs are, however,
limited by the quality and scope of the given data. Both crowdsourcing and
text mining methods of construction suffer from limitations. As such, numerous
methods have been proposed to utilise LLMs for tasks like relation extraction
and property identification in the KG construction process, as discussed in
[11]. Utilising LLMs, KG construction could be more automatic whilst
maintaining accuracy.

##  3 LLMs Empowered by KGs

Knowledge injection. A myriad of techniques have been implemented in research
and industry to perform knowledge injection using KGs, usually including
additional knowledge in LLM prompts as shown in Figure 2. For instance, Baek
et al. [20] proposed KAPING (Knowledge-Augmented language model PromptING),
which retrieved facts from a KG and prepended them to input questions to
construct LLM prompts for zero-shot question answering. A similar approach was
adopted in Sen et al. [21] where, instead of the previous approach, the facts
from the KG were weighted by a Knowledge Graph Question Answering (KGQA)
before being fed into the LLM. In KICGPT (Knowledge In Context with GPT) [22],
retrieved KG facts were re-ranked by the LLM. An application example is DRAK
(Domain-specific Retrieval-Augmented Knowledge), where retrieved KG facts are
also useful to LLMs in the biomolecular domain, which requires structured
knowledge [23]. These approaches are a mere subset of the numerous variations
of techniques utilised to inject KG knowledge into LLM prompts. Rather than
including retrieved KG facts into LLM prompts, Knowledge Solver [24] teaches
LLMs to traverse KGs in a multi-hop way to reason the answer to a question. In
this way, KGs could provide facts that LLMs could reason over, grounding them
in the process. Increasing LLM explainability. On the other hand, KGs can
contribute more to LLMs than by simply providing facts for knowledge
grounding. For the question-answering task, QA-GNN (Question Answering Graph
Neural Network) [25] performed joint reasoning over a LLM encoding of the
question context and KG to unify the two representations. For better model
interpretability, a graph neural network (GNN) was used to calculate weights
between graph nodes, providing a path of reasoning that the model took through
the KG to get to the answer. Another example is LMExplainer [26], which used a
KG and graph attention neural network to understand key decision signals of
LLMs, which were converted into natural language explanations for better
explainability. KGs could, therefore, also allow for better interpretability
of LLMs and offer insights into LLMs’ reasoning processes, which in turn
increase humans’ trust in LLMs.

Semantic understanding. KGs can also be applied to add semantic understanding
or entity embeddings into LLMs. For instance, LUKE (Language Understanding
with Knowledge-based Embeddings) [27], as an extension of BERT, is an entity-
aware self-attention mechanism that can help the model treat words and
entities in a given text as independent tokens and output contextualised
representations of them. As for adding semantic understanding, a recent
methodology called Right for Right Reasons (R3) [28] for performing KGQA using
LLMs casts the problem of common sense KGQA as a tree-structured search to
make full use of surfaced commonsense axioms, a key property that makes the
reasoning procedure verifiable, such that semantic understandings from KGs can
be added into LLMs.

Figure 2: KG-enhancement for LLMs can come in the form of (i) KG knowledge
injection into LLM prompts or (ii) other methods where KGs directly contribute
to LLMs.

##  4 KGs Empowered by LLMs

There are numerous examples of LLMs being empowered by KGs, but one could also
consider the opposite direction for integration: KGs empowered by LLMs.

Temporal forecasting. Recent research has shown that LLMs can perform
forecasting with KG data, especially for a special subset of KGs having
directions and timestamps, namely Temporal Knowledge Graphs (TKGs). Most
advanced research on TKGs mainly focuses on predicting future facts when given
historical facts, where using LLMs can be particularly beneficial. For
instance, Xia and their colleagues propose a Chain-of-History (CoH) reasoning
method for TKG prediction [29], where an LLM is mainly used to understand the
semantic meaning of entities, relationships, and timestamps in a TKG by
exploring important high-order history chains step-by-step and reasoning the
answers to the query only based on inferred history chains in the last step.
Alternatively, by using in-context learning (ICL) with LLMs whereby a few
examples were provided to the LLM so it could learn to perform forecasting,
Lee et al. [30] fed TKG facts to the LLM and found that LLMs were surprisingly
capable of learning patterns from historical data. This is despite the lack of
special architectures or modules usually required to perform this KG task. The
ability of LLMs to perform what is typically a KG task allows for the
possibility of using natural language to perform forecasting.

Knowledge graph construction. As discussed previously, one major challenge is
the time-consuming and costly construction process of KGs, another aspect to
which LLMs can contribute in various ways as depicted in Figure 3. LLMs are
trained on large, diverse datasets and store this knowledge implicitly.
BertNet [31] sought to harvest KGs of arbitrary relations from LLMs, useful
for general KGs. To achieve this, an initial prompt was paraphrased several
times and the LLM would provide responses to each of the paraphrased prompts,
which were converted into entity pairs and ranked. The top-ranking pairs
formed the KG. Kommineni et al. [32] crafted a semi-automatic KG construction
pipeline utilising ChatGPT-3.5, which prompted the LLM to generate high-level
competency questions about the data. The LLM was instructed to extract
entities and relationships from these questions to form an ontology, then map
retrieved information from documents onto the ontology to construct the KG.
Similar examples include AutoRD [33], a useful framework introduced recently
for extracting information about rare diseases and constructing corresponding
knowledge graphs. This system can process unstructured medical text as input
and output extraction results and a knowledge graph, where LLM is used to
extract entities and relations from medical ontologies. Most recently, an
unsupervised framework called TKGCon (Theme-specific Knowledge Graph
Construction) [34] utilised LLMs to construct both ontologies and theme-
specific KGs, by relying on LLMs to generate and decide relations between
entities to construct graph edges. These methods signal that LLMs are capable
of more than knowledge extraction from unstructured data. They can also
process and reason over data to construct and complete KGs. Furthermore,
Khorashadizadeh et al. [11] outlined other methods that used LLMs for specific
KG construction tasks like text-to-ontology mapping, entity extraction, and
ontology alignment. LLMs were also used for KG validation through fact-
checking and inconsistency detection.

Figure 3: LLM-enhanced KG construction, where (i) LLMs are used for
information extraction or (ii) for general KG data operations.

##  5 Hybrid Approaches

Fusing textual and knowledge embeddings. In contrast to the methods presented
in previous sections, the approaches explained in this section combine KGs and
LLMs in a more unified way to build upon the explicit knowledge from KGs and
implicit knowledge found within LLMs. An example of this is ERNIE (Enhanced
Language RepresentatioN with Informative Entities) [35], which fuses lexical,
syntactic, and knowledge information together by stacking a textual T-Encoder
with a knowledge K-Encoder to represent word tokens and entities in a unified
feature space, similar to the illustration in Figure 4. The T-Encoder
functions exactly as the LLM, BERT [1], to obtain a feature representation of
word tokens. Together with entity embeddings from KGs, these features are
subsequently fed into the K-Encoder, which fuses these separate embeddings
into a unified output embedding. This approach improved performance for
knowledge-driven tasks like entity typing and relation classification. A
similar architecture is adopted by CokeBERT [36], which utilises an LLM to
encode word tokens and extracts knowledge contexts from KGs for each entity
detected in text. The word and knowledge embeddings were fused using the
K-Encoder from [35].

Adding knowledge within the LLM. Other approaches fuse KG knowledge with
textual embeddings within the LLM. KnowBERT [37] includes an additional KAR
(Knowledge Attention and Recontextualization) component to BERT’s
architecture. It takes contextual representations from a BERT layer, computes
a knowledge-enhanced representation using a list of possible entity links from
a KG, and passes the new representation to the next transformer block in BERT.
Compared to standard BERT, KnowBERT exhibited better performance on relation
extraction, words in context, and entity typing tasks.

Multimodal LLMs. Both types of embeddings have also been combined to perform
visual question-answering. KRISP [38] uses a multimodal BERT-pretrained
transformer to process a question and image pair in its implicit knowledge
model. A separate explicit knowledge model constructs a KG from question and
image symbols, including places, objects, and attributes. The two models work
together to predict answers regarding the image.

A common theme amongst these approaches is that they perform better tasks like
entity typing, which involves assigning entities into categories based on
their semantic meanings, and visual question answering. These tasks require
models to better understand semantics, which appears to be an advantage of the
hybrid approaches.

Figure 4: Hybrid Approaches that combine text and KG embeddings.

##  6 Thematic Analysis

In this section, the important models mentioned so far are categorised into
"Add-ons" versus "Joint" Knowledge Graph + LLM approaches.

  * •

Add-ons - The models categorised here use LLMs and KGs as supplementary tools
to enhance their functionality. They include models where LLMs are used for
creating KGs or KGs are used for providing information to LLMs. The purpose
behind employing this approach is so that KGs and LLMs can operate
independently to maximizing qualities such as scalability, cost reduction, or
flexibility.

  * •

Joint - The models under this category leverage the combined strengths of LLMs
and KGs to achieve enhanced performance in specific tasks. The tasks are
application-dependent, and this approach can provide comprehensive
understanding, optimized results, and improved accuracy.

The rationale behind categorizing each model into a specific category, along
with the potential advantages of the particular approach for the model, has
been provided in the Explanation section of Table LABEL:table:table1. This is
purely our contribution. This novel perspective can be used to understand how
these technologies can be used in isolation or together. This is an angle that
has not been previously explored in other papers.

Table 1: Models and types of interaction among KG-LLMs. Model |  Add-on or Joint |  Explanation
---|---|---
KnowPhish |  Add-on |  KnowPhish Detector (KPD) combines:  • KnowPhish: For logo-based detection. This on its own is just a Brand Knowledge Base. • Large Language Model (LLM): To extract brand information from webpage text, enabling detection of phishing attempts even without logos. Wikidata, a knowledge graph, was utilised to create the Brand Knowledge Base by providing information about brands as entities, including details such as logos, official website URLs, and aliases. The KG and LLM are used for two separate procedures and two separate intentions and act as add-ons [39]. The KG allows for better scaling to extend to many brands, while the LLM allows for extracting brand information from webpages from text. Using the KGs and LLMs as add-ons offers improved detection accuracy.
BEAR |  Add-on |  BEAR is an innovative open Knowledge Graph designed to capture knowledge pertinent to the service computing community. Service computing aims to act as a bridge between business services and IT services [10]. An ontology specific to the domain serves as the foundation for the KG, outlining the concepts and characteristics that would later populate the graph. The LLM is only used to improve the data extraction process to update the KG with the required relevant information. Utilising the LLM as an add-on eliminates the need for manual data annotation, saving time and costs.
K-BERT |  Joint |  BERT is an LLM that lacks domain-specific knowledge since it is pre-trained in general language from large-scale corpora. K-BERT addresses this by injecting domain knowledge from knowledge graphs into sentences. By using the functionalities of LLMs and KGs jointly, good performance in domain-specific tasks can be achieved in K-BERT without requiring extensive pre-training [40].
ERNIE |  Joint |  ERNIE is a language representation model trained on large-scale textual corpora and KGs, allowing it to simultaneously utilise lexical, syntactic, and knowledge information. Incorporating KGs in a joint fashion results in better language understanding [35].
LMExplainer |  Joint |  LMExplainer is a knowledge-enhanced tool designed to explain the predictions made by LLMs. It uses KGs and graph attention neural networks to describe the reasoning behind the model’s predictions [26]. This joint approach ensures that the explanations are human-understandable.

##  7 Strengths and Limitations of Existing Research

This section reviews the strengths and limitations of existing research
covered in this paper, which can be crucial to understanding this joint
approach of combining KGs and LLMs. One of the significant strengths we have
identified is the performance improvement brought by the utilisation of KGs
and LLMs in a joint fashion, especially in the knowledge-driven domain. Models
combining KGs and LLMs typically display a better semantic understanding of
knowledge, thus enabling them to perform tasks like entity typing better.
Additionally, as was seen in several methods like QA-GNN [25],
interpretability and explainability of the model can be increased when
combining KGs with LLMs, which are particularly important factors when LLMs
are being adopted in sensitive domains like healthcare, education, and
emergency response.

Nevertheless, current research also has limitations that may hinder this joint
approach’s broader application or effectiveness. One of the major issues is
that KGs in some domains may not be widely available, thus limiting the
ability to integrate KGs and LLMs. Even if LLMs were employed to help automate
the KG construction process, they could hallucinate or produce incorrect
results, compromising KG data’s accuracy and validity.

Furthermore, integrating KGs and LLMs can lead to even larger parameter sizes
and longer running times. Moreover, if additional modules are included, as was
the case in several hybrid models discussed in Section 5, the extra time and
computational resources would be needed to train these modules as well. As
demonstrated by Yang et al. [41], due to these knowledge graph-enhanced pre-
trained language models (KGPLMs) injecting the knowledge encoder module into
pre-trained language models (PLMs), their running times are consistently
longer than a vanilla LLM, BERT, across pre-training, fine-tuning, and
inference stages. This is although incorporating external knowledge from KGs
makes it easier for them to train and enhances their performance.

Another challenge is that KGs and LLMs will likely suffer from becoming
outdated due to the rapid evolution of knowledge. One may need to update KGs
or LLMs frequently to mitigate this issue. KGs are significantly easier than
LLMs to update, although additional KG completion steps are required. In the
case of LLMs, the impracticality of repeating lengthy and costly training
processes can significantly affect the time and costs involved. This calls for
additional methods to update LLMs, whether it is via KGs or other knowledge
sources.

The effectiveness of KG and LLM integration is also an area that demands
further research. According to a study from [42], only a marginal amount of
knowledge is successfully integrated into two well-known knowledge-enhanced
Language Models (LM), namely ERNIE and K-Adapter, and simply increasing the
size of the Knowledge Integration corpus may not lead to better knowledge-
enhanced LMs. This highlights a critical gap in the current approach and
suggests a need for more effective integration methods.

##  8 Conclusion

Driven by research questions “How can KGs be utilised to enhance the
capabilities of LLMs?” (RQ1), "In what ways can LLMs be leveraged to support
and enhance KGs" (RQ2) and “Are there more advantages if models combine KGs
and LLMs in a more joint fashion” (RQ3), we conducted a quick review to
explore the prevalence of using KGs purely for knowledge injection to support
LLM-based models, and of using LLMs merely as information extractors to
support KG-based models. To understand the advantages of this joint approach,
we reviewed over 20 state-of-the-art articles from arXiv and classified
existing methods into three different categories, including KGs empowered by
LLMs (KGs adding interpretability, semantic understanding, and entity
embeddings into LLMs), LLMs empowered by KGs (LLMs do forecasting with KG
data, injecting implicit knowledge, and contributing to KG construction) and
some Hybrid Approaches where KGs and LLMs are combined in a more unified way.
We also provided a Thematic Analysis for these methods and discussed their
strengths and limitations. To answer research questions, we found that models
typically use KGs or LLMs like add-ons, and there are more advantages if
models combine KGs and LLMs in a more joint fashion. Additionally, we found
that even though the joint approach can provide significant improvement in the
performance of the model by increasing its interpretability or explainability,
current research also has its limitations, such as limited domains for KGs,
higher consumption of computational resources, outdated frequently due to the
fast evolution of knowledge, and the lack of effectiveness in Knowledge
Integration.

This research area of combining KGs with LLMs represents a critical part in
the rising trend of artificial intelligence (AI), and can potentially lead to
more reliable and context-aware AI systems. Such models are equipped with
domain-specific knowledge and contribute to a wider range of applications than
solely using KGs or LLMs for problem-solving. Eventually, this research area
will significantly impact how we construct a more robust and explainable AI
system with higher performance and can provide avenues for other future
development.

Although the joint approach of combining KGs with LLMs has succeeded, some
unsolved challenges remain. To address the low effectiveness of knowledge
integration, future studies may continue exploring the potential solution to
this problem by modifying model architecture or fine-tuning. One possible
solution could be to inject knowledge into feature-based pre-training models.
Future studies could also focus on developing a smaller integrated model to
reduce computational resources and time, as integrating KGs and LLMs typically
leads to larger parameter sizes and longer running time. Given the fact that a
smaller KGPLM can outperform a larger plain LLM, it is possible that an
optimal integrated model that requires fewer computational resources can be
achieved. In the past year, there has also been a surge in interest in
multimodal LLMs, which can process audio, image, or video data together with
text, with a handful of multimodal LLMs released each month since the start of
2023. As these models are built on LLM backbones, it is foreseeable that they
could also inherit some of the limitations LLMs have experienced thus far and
might benefit from incorporating KGs. As such, other studies could explore the
potential use of multimodal KGs when combined with LLMs for the recent
advances in the research of multimodal models.

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# A High-Sensitivity Radon Emanation Detector System for Future Low-Background
Experiments

Daniel Wiebe111nee: Daniel Baur , Sebastian Lindemann  , Marc Schumann

###### Abstract

Radioactive radon atoms originating from the long-lived primordial
${}^{238}\mathrm{U}$ and ${}^{232}\mathrm{Th}$ decay chains are constantly
emanated from the surfaces of most materials. The radon atoms or their
radioactive daughter isotopes can significantly contribute to the background
of low-background experiments, e.g., the ${}^{222}\mathrm{Rn}$ progeny
${}^{214}\mathrm{Pb}$ dominates the background of liquid xenon detectors which
are currently leading the direct search for WIMP dark matter. We report on a
new detector system to directly quantify the ${}^{222}\mathrm{Rn}$ surface
emanation of materials. Using cryogenic physisorption traps, emanated radon
atoms are transferred from an independent emanation vessel and concentrated
inside the dedicated detection vessel, where the charged daughter isotopes,
most importantly ${}^{214}\mathrm{Po}$ and ${}^{218}\mathrm{Po}$, are
electrostatically collected and detected on a silicon PIN photodiode. The
overall detection efficiency is $\sim$36 % for both polonium channels. The
intrinsic detection vessel background was measured to be $\sim
2.4\,\mathrm{cpd}$ ($28\,\mathrm{\upmu Bq}$) and $\sim 1.5\,\mathrm{cpd}$
($17\,\mathrm{\upmu Bq}$) for ${}^{218}\mathrm{Po}$ and ${}^{214}\mathrm{Po}$,
respectively. The radon emanation activity of the emanation vessel was
determined to be $(0.16\pm 0.3)\,\mathrm{mBq}$, resulting in a detection
sensitivity of $\sim$59 $\mathrm{\upmu Bq}$ (at $90\,\%$ C.L.).

##  1 Introduction

The best constraints on spin-independent WIMP-nucleon scattering for WIMP
masses $>5\,\mathrm{GeV}/c^{2}$ to date come from ultra-low background
experiments using dual-phase xenon time projection chambers (TPCs) [1, 2, 3,
4]. These instrument tonne-scale liquid xenon (LXe) targets to detect both the
scintillation and ionization signals generated by the interaction of particles
with the target xenon atoms. The ratio of light to charge signal is used to
distinguish between nuclear recoil (NR, WIMP-like) and electronic recoil (ER,
background-like) signals.

One of the dominant backgrounds in these experiments is the leakage of low-
energetic beta decays of ${}^{214}\mathrm{Pb}$ (ER signals), a daughter of
${}^{222}\mathrm{Rn}$, into the WIMP NR-signal region: Radon is part of the
ubiquitous primordial ${}^{238}\mathrm{U}$ decay chain and emanates off any
detector component via recoil ejection and diffusion. Due to its comparably
long half-live of $3.8\,\mathrm{d}$ and its chemical inertness, it distributes
within the LXe target. $9.3\,\%$ of the ${}^{214}\mathrm{Pb}$ daughter atoms
beta-decay directly to the ground state. Consequently, these radon-induced ER
background events can neither be mitigated via target fiducialization nor by
tagging coincident nuclear de-excitations with emission of a gamma ray. The
XENONnT experiment has recently reported a radon activity concentration of
$1.8\,\upmu\mathrm{Bq}/\mathrm{kg}$ [5], which has been further reduced in the
meanwhile [4].

Future experiments with a multi-ton-scale LXe target, such as DARWIN [6] or
XLZD [7], aim at exploring the entire WIMP parameter space accessible to the
LXe TPC technology [8, 9] and offer an interesting neutrino physics program
[10, 11, 12]. To reach the design sensitivity, their ER and NR backgrounds
must be dominated by irreducible interactions of astrophysical neutrinos [6],
requiring a reduction of the ${}^{222}\textrm{Rn}$ concentration to
$0.1\,\mathrm{\upmu Bq}$ [9]. This corresponds to an order of magnitude-
improvement with respect to the best experiment of the current-generation
[13]. The challenging goal will be met by a combination of background
mitigation methods: surface treatment/coating [14], detector design [15, 16,
17], active radon removal [18] as well as by using only ultra-low-emanation
materials for all detector parts in direct contact with the LXe. As Rn-
emanation is a surface effect, bulk measurements of the ${}^{226}\textrm{Ra}$
activity via standard gamma spectrometry can be misleading. Thus, the radon
emanation rate of all materials in contact with LXe must be quantified by
means of highly sensitive radon emanation detectors.

The concept of the electrostatic radon emanation chamber [19, 20] presented in
this work is by now an integral part of the radiopurity assay of modern rare-
event searches [21, 22, 23, 24, 25]. Such an instrument consists of a gas-
tight vacuum vessel that houses a silicon PIN photodiode set to negative high-
voltage, creating an electrical drift field with respect to the vessel on
ground potential. ${}^{222}\mathrm{Rn}$ atoms present in the vessel will
eventually decay, leaving the daughter ${}^{218}\mathrm{Po}$ in a positively
charged state with a probability of $\sim 90\,\%$ as a consequence of the
alpha decay recoil [26]. Accelerated in the drift field, the ionized daughters
can be collected electrostatically on the surface of the PIN diode, where the
radon daughters ${}^{218}\mathrm{Po}$ and ${}^{214}\mathrm{Po}$ are identified
by the spectrometric measurement of their alpha decay energies. Modeling the
evolution of the detected activity of the radon daughters during the
measurement allows the inference of the initial radon emanation rate.

This work presents the design, construction, and performance of the MonXe
radon emanation detector. Section 2 describes the working principle and
experimental setup of the detector. Section 3 explains the operation of the
instrument. Section 4 shows its performance in terms of background,
efficiency, and sensitivity. Section 5 presents exemplary screening
measurements of high-activity zeolite granulate and a low-activity PTFE
sample. The article concludes in Section 6 with a summary and an outlook on
future optimizations and planned measurements.

##  2 Experimental Setup

The MonXe radon detector system features a set of two decoupled vessels for
the emanation and detection of radon, respectively. It allows the assessment
of emanation of large and bulky samples while optimizing the radon detection
in a reproducible measurement procedure. The hemispherical shape of the
detection vessel (volume: 1.2 liters, radius: 7.7 cm) was optimized in terms
of electrostatic collection efficiency via dedicated particle-tracking
simulations taking into account diffusion effects. The PIN diode is installed
in the central bore of the vessel’s CF160 flange, with the diode surface being
aligned with the inner flange plane. The CF160 flange features six additional
CF16 flanges to connect sensors and the concentration line. The detection
vessel’s inner surface is electropolished to minimize its intrinsic radon
emanation. The cylindrical emanation vessel has a height of
$40.0\,\mathrm{cm}$ and an inner diameter of $25.4\,\mathrm{cm}$,
corresponding to a volume of $20.4\,\mathrm{l}$. It is closed off with CF250
flanges on both sides. A photograph of the radon emanation detector is shown
in the left panel of Figure 1. The gas system connecting both vessels is
sketched in the right panel. The entire system was built from CF/Conflat and
VCR metal-sealed UHV components and exhibits a leak rate below
$10^{-9}\,\mathrm{mbar\,l}/s$.

Samples of arbitrary material, size, and shape can be placed inside the
emanation vessel, which is evacuated after installation of the sample, and
afterwards refilled with $1\,\mathrm{bara}$ of purified helium. In principle,
several emanation vessels could be installed in parallel to speed up an
extensive measurement campaign. The emanation rate of a sample is assessed by
transferring the emanated radon atoms into the detection vessel via cryogenic
physisorption on activated charcoal (charcoal: Blücher Saratech
100050-VC000021). Helium (grade 5.0) is used as carrier gas; the gas bottle is
directly attached to the purification trap (PT), which is kept at cryogenic
temperature during operation by immersing it into a liquid nitrogen bath. This
purifies the helium gas introduced into the system, removing radon and other
contaminants. The emanation vessel is filled with helium gas which is
subsequently extracted from the system by means of a vacuum pump via the cold
transfer trap (TT), where the radon atoms are adsorbed. The purification
(transfer) trap is made of an electropolished stainless steel cylinder of 10.0
cm length and 4.0 cm (1.2 cm) inner diameter to accommodate 75 g (10 g) of
activated charcoal. By subsequent heating of the transfer trap to
$175^{\circ}\mathrm{C}$, the radon gets desorbed and is flushed into the
detection vessel using clean He gas.

The pressure in the emanation and detection vessels is monitored in the radon
transfer and measurement phases using OMEGA PX409 sensors. A hot zirconium
getter (SAES MonoTorr PS3-MT3-R-2) is installed in between the emanation
vessel and the gas system’s main line to remove electronegative impurities
outgassing from the sample, which might affect the electrostatic collection
efficiency.

Figure 1:  Photograph (left) and schematic (right) of the MonXe radon
emanation detector. The examined sample (green brick) is placed inside the
emanation vessel (EV). The emanated radon atoms are transferred into the
hemispherical detection vessel (DV), where the activity is measured using a
PIN diode (PD, magenta). The transfer occurs by evacuating the emanation
vessel using a turbo molecular pump (TP) through the transfer trap (TT), which
is cooled to liquid nitrogen temperature. In this trap, radon atoms adsorb
onto activated charcoal (black spheroids). A hot zirconium getter (HG)
installed in series removes other contaminants such as O2, H2O in the gas. By
heating the transfer trap (TT), the radon atoms desorb again and are guided
into the detection vessel by a flow of helium, which is purified by cryogenic
activated charcoal housed inside the purification trap (PT).

The hemispherical detection vessel houses the silicon PIN photodiode
(Hamamatsu S3590-09) with a photosensitive area of $10\,\mathrm{mm}\times
10\,\mathrm{mm}$. The diode does not feature a protective epoxy cover and
directly exposes its p-layer to minimize the absorption of the energy of the
impinging alpha particles in an inactive material layer. The diode is embedded
in a PTFE cylinder installed in a CF40 double nipple centered on the flange of
the detection vessel. Its pins are connected to two SHV coaxial feedthroughs.
Their air sides are directly connected to a custom-developed frontend
electronics module. It provides a high-voltage of $-1.0\,\mathrm{kV}$ to the
diode to establish an almost radial electrical collection field between the
grounded vessel and the diode. A battery installed in series provides the
reverse bias voltage of $9\,\mathrm{V}$ to the diode. The analog current
signal of the diode is capacitively decoupled from the high-voltage circuit
and fed into a two-stage low-noise preamplifier with a total transimpedance
gain of $\sim 10^{8}\,\Omega$ and a bandwidth of $100\,\mathrm{kHz}$. Low- and
high-pass filters reduce the electronic noise. The shaped signals from alpha
decays of ${}^{214}\mathrm{Po}$ ($7.7\,\mathrm{MeV}$) and
${}^{218}\mathrm{Po}$ ($6.0\,\mathrm{MeV}$) create amplitudes of
$~{}750\,\mathrm{mV}$ and $~{}500\,\mathrm{mV}$, respectively. The typical
decay time of the signals is $\sim 50\,\mathrm{\upmu s}$. They are digitized
and analyzed by a 14-bit multichannel analyzer (CAEN DT5781a) sampling the
signal at $100\,\mathrm{MS}/\mathrm{s}$. An event is read out if the pulse
exceeds a threshold that is set sufficiently low such that it is surpassed by
any relevant alpha signal. For every event, its timestamp, pulse height, and
raw waveform data are stored. Storage of the raw data could be disabled,
however, especially during detector commissioning the direct access to the
waveform data was very useful.

During every measurement, which consists of the radon transfer and data
acquisition phases, ambient and process parameters, such as temperatures and
pressures, are monitored and stored in a database. A custom-developed
lightweight slow control system running on an industry-grade microcontroller
(KUNBUS RevPi Core 3$+$) is used for that purpose.

##  3 Measurement Procedure and Activity Model

${}^{226}\mathrm{Ra}$ has a long half-life of $1600\,\mathrm{years}$, and
${}^{222}\mathrm{Rn}$ is thus assumed to be produced and emanated with a
constant radon emanation activity $\mathcal{R}$. The emanation rate of a
sample is determined by measuring the alpha decays of the radon daughter
isotopes ${}^{214}\mathrm{Po}$ and ${}^{218}\mathrm{Po}$. The standard
measurement procedure with the radon emanation detector consists of three
phases: radon emanation, radon transfer, and the polonium activity
measurement. The activities of the various isotopes in these phases can be
computed analytically by solving the respective rate equations; the time
evolution of every isotope depends on its radioactive decays and the decays of
its mother isotopes.

#### Radon Emanation:

The sample under study is closed off in the emanation vessel EV, which is
subsequently evacuated and filled up with purified helium to atmospheric
pressure. Governed by the ${}^{222}\mathrm{Rn}$ half-life of
$3.82\,\mathrm{d}$, the emanated radon activity within the emanation vessel
asymptotically approaches the secular equilibrium activity
$\mathcal{R}^{\mathrm{sample}}$.

#### Radon Transfer:

After a few ${}^{222}\mathrm{Rn}$ half-lives (the time is optimized depending
on the expected emanation rate of the sample), the accumulated radon atoms are
concentrated and transferred from the emanation into the detection vessel DV.
The emanation vessel is evacuated via the cold transfer trap TT, which is
immersed in liquid nitrogen. Once the extracted radon atoms are adsorbed onto
the porous charcoal, the trap is closed off and then heated up to
$175\,^{\circ}\mathrm{C}$. The radon atoms desorb and mix with the carrier
gas. By connecting the transfer trap to the previously evacuated detection
vessel, the carrier gas and hence the majority of the radon atoms can expand
into the detection vessel. The remaining radon atoms are collected by flushing
purified helium through the transfer trap into the detection vessel up to an
absolute pressure of $1.0\,\mathrm{bara}$.

#### Polonium Activity Analysis:

Most impurities, e.g., contaminants from outgassing and the radon decay
products themselves, are removed by the hot getter during the transfer from
the emanation vessel into the detection vessel. Thus the sample signal
activities of ${}^{218}\mathrm{Po}$ and ${}^{214}\mathrm{Po}$ inside the decay
vessel, $A^{\mathrm{sample}}_{{}^{214}\mathrm{Po}}(t)$ and
$A^{\mathrm{sample}}_{{}^{218}\mathrm{Po}}(t)$, are zero at the start of the
measurement $t=t_{\mathrm{meas}}^{0}$. Once the transfer is finished, they
increase solely due to the exponential radioactive decay of the trapped radon
sample atoms. ${}^{218}\mathrm{Po}$ or ${}^{214}\mathrm{Po}$ decays are
identified by their respective energy in the MCA spectrum, see Figure 3
(right) on page 3. The number of detected events $n^{\mathrm{meas}}$ from a
certain polonium isotope in a measurement interval $\Delta t_{\mathrm{meas}}$
is given by

$\displaystyle n^{\mathrm{meas}}$
$\displaystyle=\varepsilon^{\mathrm{det}}\,\int_{\Delta
t_{\mathrm{meas}}}A^{\mathrm{sample}}(t)\,\mathrm{d}t\,\,+\bar{n}^{\mathrm{DV}}\,\,+\bar{n}^{\mathrm{EV}}\quad,$
(3.1)

which takes into account the detection efficiency $\varepsilon^{\mathrm{det}}$
(see Section 4.2) and the mean number of expected background events from both
the detection vessel $\bar{n}^{\mathrm{DV}}$ and the emanation vessel
$\bar{n}^{\mathrm{EV}}$ (see Section 4.1). Since the polonium signal
activities $A^{\mathrm{sample}}(t)$ can be expressed in analytical form (as
the solution of a system of coupled, inhomogeneous first-order differential
equations), Equation (3.1) can be solved for the radon activity at
$t_{\mathrm{meas}}^{0}$. By additionally taking into account the duration of
the radon emanation and transfer phases, one can then infer the radon
emanation activity $\mathcal{R}^{\mathrm{sample}}$ of the sample under study.

##  4 Detector Performance

In this Section, we present results on the performance of the radon emanation
detector obtained during detector commissioning.

###  4.1 Backgrounds

During a sample measurement, the surfaces of both the detection vessel (DV)
and emanation vessel (EV) also emanate ${}^{222}\mathrm{Rn}$ atoms, that,
along with leakage of other decays and detector artifacts, contribute to the
overall number of measured events $n^{\mathrm{meas}}$, as expressed by
Equation (3.1).

To measure the detection vessel background, it was filled with
$1\,\mathrm{bara}$ of purified helium. Several such blank measurements were
conducted. After approximately four weeks, average equilibrium detection
vessel background rates of 2.4 counts per day (cpd; $28\,\mathrm{\upmu Bq}$)
and 1.5 cpd ($17\,\mathrm{\upmu Bq}$) were measured in the regions of interest
for the 218Po and 214Po channels, respectively. These rates comprise the sum
of intrinsic 222Rn emanation from the detection vessel and additional
components that potentially result from the leakage of other decays, such as
those from the 220Rn chain, as well as detector-specific artifacts. Due to the
a priori unknown time dependence of all these effects, the detection vessel
background is determined in a data-driven manner for each sample measurement.
We estimate the number of events, $\bar{n}^{\mathrm{DV}}$, that is attributed
not to a sample but to the detection vessel, by averaging the number of
background events that occurred during the equivalent measurement duration in
the individual background measurements.

The radon emanation background rate of the emanation vessel
$\mathcal{R}^{\mathrm{EV}}$ is determined in a regular measurement without
sample, following the procedure outlined in Section 3 accounting for the
detection vessel background determined above. Two such background measurements
were conducted. For both measurements, both polonium channels are in excellent
agreement. Taking into account the detection efficiencies
$\varepsilon^{\mathrm{det}}$ from Equation (4.2) yields a value of

$\displaystyle\mathcal{R}^{\mathrm{EV}}=\left(0.16\pm
0.03\right)\,\mathrm{mBq}.$ (4.1)

With this measured EV background activity $\mathcal{R}^{\mathrm{EV}}$, the
expected number of EV background events $\bar{n}^{\mathrm{EV}}$ can be
calculated via the analytical polonium activity model. Any activity from
${}^{220}\mathrm{Rn}$ and its daughters is expected to have already decayed
during the transfer process and can thus be neglected. The background modeling
approach allows us to account for the unavoidable variations of the duration
of the radon emanation and transfer phases, caused by, e.g., different
emanation vessels and sample sizes.

###  4.2 Detection Efficiency

The overall detection efficiency $\varepsilon^{\mathrm{det}}$ is obtained by
comparing the experimentally measured emanation rate with the known reference
value of a calibrated sample. The sample was provided by G. Zuzel
(Jagiellonian University, Kraków, Poland). It consists of two 2 mm thick
stainless steel discs with a diameter of 20 mm, onto which
${}^{226}\mathrm{Ra}$ ions were electrodeposited. The reference measurements
of the same source were carried out by H. Simgen at the Max-Planck-Institut
für Kernphysik (MPIK) in Heidelberg, Germany, utilizing miniaturized
proportional counters [27]. An activity of $(47.6\pm 1.5)\,\mathrm{mBq}$ was
measured for both polonium channels. The source was stored in a CF40 vacuum
vessel, which can be closed off by two VCR bellow valves. For the calibration
measurements, it was connected to the gas system, replacing the emanation
vessel.

Figure 2:  (Left) The results of four independent measurements of a 226Ra
calibration source are shown relative to the absolute 222Ra emanation rate
obtained from miniaturized proportional counters. The detection efficiency
$\varepsilon^{\mathrm{det}}$, counting both ${}^{218}\mathrm{Po}$ (blue) and
${}^{214}\mathrm{Po}$ (red) decays, agrees in all four measurements, which
also validates the radon concentration and transfer procedure. (Right)
Estimation of MonXe’s sensitivity in terms of the Poissonian box-counting
model described in the text, considering only the ${}^{214}\mathrm{Po}$
background of the detection vessel (DV, dashed lines), and the backgrounds of
the detection and the emanation vessels (DV+EV, solid lines). In the absence
of a signal, the expected median upper limits at $90\,\%\,CL$ on the number of
detected ${}^{214}\mathrm{Po}$ signal events
${{}^{***}n}^{\mathrm{sample}}_{{}^{214}\mathrm{Po}}$ (red lines, left axis)
increase with increasing measurement time $\Delta t^{\mathrm{meas}}$. The
corresponding upper limits at $90\,\%\,CL$ on the radon emanation activity
${{}^{***}\mathcal{R}}^{\mathrm{sample}}$ of the sample (petrol lines, right
axis) show optima at $13\,\mathrm{\upmu Bq}$ and $59\,\mathrm{\upmu Bq}$,
respectively. (Upper limits are given as the median of the underlying Monte
Carlo distribution; the shaded areas indicate their widths) .

The calibration campaign consisted of four individual measurements of the
radon emanation rate from the calibrated sample, following the routine
presented in Section 3. The left panel of Figure 2 shows the individual
results relative to the reference value. All four measurements are in mutual
agreement, and both polonium channels are consistent with each other (see
values given in Figure 2, left). The detection efficiency is thus

$\displaystyle\mathcal{\varepsilon}^{\mathrm{det}}=\left(36.3\pm
0.2(\mathrm{stat.})\pm 1.4(\mathrm{syst.})\right)\,\%.$ (4.2)

The statistical uncertainty resembles the propagated Poissonion errors; the
systematic uncertainty comes from the $\sim 3\,\%$ uncertainty of the
reference measurement. The uncertainties have been adjusted for overdispersion
due to potentially unaccounted variations in the manual transfer procedure by
increasing the statistical uncertainty such that the fit of a constant results
in a reduced chi-square of unity. Note that
$\mathcal{\varepsilon}^{\mathrm{det}}$ already includes efficiency losses of
at least $50\,\%$ due to the finite solid angle coverage of the active
photodiode for alpha particles emitted on its surface. Simulation studies of
the electric collection field suggest an electrostatic collection efficiency
close to $100\,\%$ for the realized hemispherical detection vessel geometry
and a static collection field generated by a high-voltage of
$-1.0\,\mathrm{kV}$ [28].

###  4.3 Sensitivity

We evaluate the detector sensitivity in terms of a single-bin Poisson counting
experiment, following the analysis scheme outlined in Section 3: The integer
number of measured events $n^{\mathrm{meas}}$ is Poisson-distributed with the
expected value
$\bar{n}^{\mathrm{sample}}+\bar{n}^{\mathrm{DV}}+\bar{n}^{\mathrm{EV}}$.
Whether or not $n^{\mathrm{meas}}$ significantly exceeds the expected
background $\bar{n}^{\mathrm{DV}}+\bar{n}^{\mathrm{EV}}$, given a significance
level $\alpha=0.1$, is determined by computing the corresponding $p$-value
assuming the background-only hypothesis. If $p<\alpha$, the background-only
hypothesis is considered rejected by the data, and one can determine the
sample’s radon emanation rate from the excess number of signal events.

If no signal above the background is observed ($p\geq\alpha$), we quote the
observed upper limit ${}^{*}n^{\mathrm{sample}}$ on the number of signal
events as the largest value of $\bar{n}^{\mathrm{sample}}$ that still yields
$\leq n^{\mathrm{meas}}$ detected events with probability $\alpha$:

$\displaystyle
p=\sum_{n=0}^{n^{\mathrm{meas}}}\mathrm{Poisson}(n\,;\,\bar{n}^{\mathrm{DV}}+\bar{n}^{\mathrm{EV}}+{{}^{*}}n^{\mathrm{sample}})$
$\displaystyle\stackrel{{\scriptstyle!}}{{=}}\alpha.$ (4.3)

The observed upper limit is thus

$\displaystyle{{}^{*}n}^{\mathrm{sample}}$
$\displaystyle=\frac{1}{2}\,F_{\chi^{2}}^{-1}\left[1-\alpha;2(n^{\mathrm{meas}}+1)\right]-(\bar{n}^{\mathrm{DV}}+\bar{n}^{\mathrm{EV}}).$
(4.4)

In Equation (4.4), the sum of Poissonian probabilities is identified with the
cumulative chi-squared distribution $F_{\chi^{2}}$, which then allows
computing ${{}^{*}\bar{n}}^{\mathrm{sample}}$ in analytical form.

To estimate the experimental sensitivity, we compute the distribution of
expected upper limits on the detected number of signal events
${{}^{**}\bar{n}}^{\mathrm{sample}}$ at confidence level CL $=1-\alpha=0.9$.
Monte Carlo data resembling the distribution of the number of observed events
under the assumption of the background-only hypothesis is generated according
to Equation (4.4). The right panel of Figure 2 shows the median expected upper
limit on the number of signal events from the sample
${{}^{**}\bar{n}}^{\mathrm{sample}}$ for the ${}^{214}\mathrm{Po}$ line and
two different background contributions vs. the measurement time. Via the
activity model (3.1) and assuming infinite emanation time (which is
approximately the case after an emanation period of four weeks) and
infinitesimal transfer time, one can translate the expected upper limit of
signal events into the corresponding upper limit on the radon emanation rate
${{}^{**}\mathcal{R}}^{\mathrm{sample}}$.

The fluctuations in Figure 2 (right) are due to the quantized number of
expected detector vessel background events $\bar{n}^{\mathrm{DV}}(\Delta
t_{\mathrm{meas}})$. Initially, the ${{}^{**}\mathcal{R}}^{\mathrm{sample}}$
sensitivity curves steeply decrease until they reach a local minimum. For
longer measurement times, the mean and width of the distributions show a
steady increase. This (at first glance counter-intuitive) time-dependence is
caused by the asymptotically falling ratio of events from the signal (emanated
by the sample and transferred once into the DV) and background events
(constantly emitted from the DV walls): while the signal events will initially
dominate over the slowly and linearly increasing number of DV background
events, they decrease exponentially and eventually fall below the number of
accumulated DV background events. For the standard measurement procedure,
i.e., a sample placed in the EV and taking into account the detection
efficiency of Equation (4.2), and the Poissonian box-counting analysis
presented in Section 3, we hence quote the sensitivity of the MonXe radon
emanation detector as the minimum of the curve taking into account the
background from both vessels (EV+DV):

$\displaystyle{{}^{**}\mathcal{R}}^{\mathrm{sample}}=59\,\mathrm{\mu Bq.}$
(4.5)

As a consequence of the time behavior shown in Figure 2, a measurement is
terminated, and an upper limit on the emanation rate of the sample is placed
if no signal is detected after twelve days.

For comparison, the miniaturized proportional counters at the Max-Planck-
Institut für Kernphysik (MPIK) in Heidelberg, Germany, which are among the
most sensitive radon emanation instruments to date, achieve sensitivities of
$\sim$40 $\mathrm{\upmu Bq}$ [27] with some counters exhibiting an intrinsic
emanation activity up to four times lower the DV background rate of our
detector. The background emanation activity of the MPIK $80\,\mathrm{l}$
emanation vessel is $(0.16\pm 0.05)\,\mathrm{mBq}$ and thus comparable to the
value of the MonXe emanation vessel given in Equation (4.1) at a four times
larger volume. The R.E.S. facility at the South Dakota School of Mines and
Technology uses a detector concept similar to MonXe and features two large
emanation chambers of 13 l or 300 l. Its sensitivity of 0.2 mBq [29] is
comparable to the one of our instrument, while our vessel backgrounds and
detection efficiency are slightly superior [22].

##  5 Sample Screening Measurements

In this section, we demonstrate the performance of the MonXe radon emanation
detector based on two samples with very different emanation rates.

###  5.1 High-activity Sample: Zeolite Granulate

Figure 3:  Radon emanation measurement of $730\,\mathrm{g}$ of commercial
zeolite adsorbent pellets for 11.59 days, following an emanation phase of 7.73
days. (Left) The measured alpha spectrum shows the two expected peaks from
${}^{218}\mathrm{Po}$ (blue) and ${}^{214}\mathrm{Po}$ (red); the
${}^{210}\mathrm{Po}$ (green) peak is strongly suppressed because of its much
longer half-life. The peaks can be well described by Crystal Ball functions.
The tail towards lower energies is caused by angle-of-incidence-dependent
energy losses in the detector. The colored areas define the energy windows for
the event selection. The observed 222Rn activity, i.e., without correcting for
the finite detection efficiency, at the start of the measurement at
$t_{\mathrm{meas}}^{0}$ can be computed analytically using the number of
events observed in the peaks and the modeled polonium activity (box-counting
(BC) analysis). (Right) Detected events in the energy regions of interest vs.
time. The activity model is fitted to the data of both isotopes (solid lines).
The model describes the data very well and can also be used to obtain the
222Rn activity at the start of the measurement (activity model fit (AMF)
analysis); both analysis methods yield identical results.

Zeolites are a class of microporous minerals typically used as an adsorbent,
e.g., in backing pump adsorption traps to prevent the backstreaming of oil
vapor. The examined sample consists of $730\,\mathrm{g}$ of commercial zeolite
adsorbent pellets (Pfeiffer Vacuum Technology AG Zeolith PK 001 248-T). Figure
3 (left) shows the alpha energy spectrum acquired over a measurement period of
$\Delta t_{\mathrm{meas}}=11.59\,\mathrm{d}$ and after an emanation time of
$7.73\,\mathrm{d}$. One can clearly distinguish three peaks corresponding to
the energies of the alpha particles emitted by ${}^{210}\mathrm{Po}$,
${}^{218}\mathrm{Po}$, and ${}^{214}\mathrm{Po}$, at $5.3\,\mathrm{MeV}$,
$6.0\,\mathrm{MeV}$, and $7.7\,\mathrm{MeV}$, respectively. The long half-life
of ${}^{210}\mathrm{Pb}$ ($22.3\,\mathrm{y}$) strongly suppresses the
${}^{210}\mathrm{Po}$ peak. The peak shapes of ${}^{218}\mathrm{Po}$ and
${}^{214}\mathrm{Po}$ are well described by a Crystal Ball function. Their
low-energy tails are attributed to angle-of-incidence-dependent energy losses
when the alpha particles traverse the insensitive p-layer of the PIN
photodiode. From the Crystal Ball fits, one can extract the energy resolution
of $1.5\,\%$, determined as the full width at half maximum of the
${}^{214}\mathrm{Po}$ peak. The energy scale is defined by identifying the
peak’s mean values from the fit with the respective alpha energies. The
${}^{214}\mathrm{Po}$ and ${}^{218}\mathrm{Po}$ events are selected from a
predefined energy interval around the peak means. These were chosen to include
$>$99% of the Crystal Ball integrals while excluding the stray
${}^{220}\mathrm{Rn}$ events mentioned in section 4.1. Following the box-
counting (BC) analysis presented in Section 3, which takes into account the
background contributions of the EV and DV, the specific radon emanation
activity of the zeolite sample is

$\displaystyle{\mathcal{R}}^{\mathrm{zeolite}}=\left(562\pm
5(\mathrm{stat.})\pm 22(\mathrm{syst.})\right)\,\mathrm{mBq/kg}.$ (5.1)

The box-counting analysis essentially ignores the knowledge of the individual
event trigger timestamps. However, for high-activity samples, this timing
information can be utilized to validate the underlying model assumptions.
Instead of just counting all events recorded during the entire measurement
$\Delta t_{\mathrm{meas}}$, one can subdivide $\Delta t_{\mathrm{meas}}$ in
equal intervals and count the detected polonium events in each bin, as shown
in Figure 3 (right) for $3\,\mathrm{h}$ intervals. For each time interval,
Equation (3.1) applies, and the underlying model parameters can now be
determined from a fit of the activity model to the data. The reduced
$\chi^{2}$-values of $0.89$ and $1.24$ indicate an excellent agreement between
data and model for ${}^{214}\mathrm{Po}$ and ${}^{218}\mathrm{Po}$. Both
analysis methods yield identical radon activities at the start of the
measurement $t_{\mathrm{meas}}^{0}$:
$A^{\mathrm{zeolite}}(t_{\mathrm{meas}}^{0})=\left(112.1\pm
0.4\right)\,\mathrm{mBq}$ (box-counting) and
$A^{\mathrm{zeolite}}(t_{\mathrm{meas}}^{0})=(112.2\pm 0.5)\,\mathrm{mBq}$
(fit of model). For the box-counting result, the quoted uncertainty resembles
the propagated Poisson uncertainty, whereas for the model fit analysis, the
statistical and systematic uncertainties arising from the fit were combined in
quadrature. The excellent agreement of data and model validates the detector
model assumption, e.g., that the initially zero polonium population at
$t_{\mathrm{meas}}^{0}$ only emerges from the decaying radon sample and the DV
background.

The bulk activity of the zeolite granulate was additionally measured with the
high-purity germanium gamma-spectrometer GeMSE [30]. The measurement of
${}^{226}\mathrm{Ra}$ is
$A_{{}^{226}\mathrm{Ra}}=11.4_{-0.7}^{+1.0}\,\mathrm{\frac{Bq}{kg}}$ reveals
that (at normal temperature and pressure and assuming secular equilibrium)
only $\sim$5 % of the ${}^{222}\mathrm{Rn}$ atoms produced in
${}^{226}\mathrm{Ra}$ decays are emanated from the surface.

###  5.2 Low-activity Sample: Semi-Finished PTFE

Polytetrafluoroethylene (PTFE, Teflon®) is a widely used construction material
in most low-background experiments due to its unique electrical insulation and
optical properties. PTFE semi-finished products are compression molded and
sintered from granulated PTFE resin. Here we present measurements of the radon
emanation of two semi-finished PTFE samples: a sample manufactured by
ElringKlinger AG and a sample manufactured by Fluorseals S.p.A.. Each sample
consisted of three cubic blocks (320 $\times$ 160 $\times$ 60 mm3 of $\sim$6
kg mass each).

In preparation for the measurements, a few microns were milled off from all
surfaces of the blocks. Since PTFE is expected only to emanate trace amounts
of radon (see, e.g., [21]), a series of deep and narrow grooves was saw-milled
into the blocks to increase the total surface area from $0.48\,\mathrm{m}^{2}$
to $2.47\,\mathrm{m}^{2}$ per sample. The blocks were cleaned in a bath of
$6\,\mathrm{mol/l}$ nitric acid, then immersed in deionized water and ethanol,
and finally blow-dried with pressurized helium. To reduce outgassing, the
emanation vessel housing the cleaned PTFE samples was evacuated with the
turbomolecular pump for two weeks before the start of the actual emanation
process.

For both sample measurements, the ${}^{214}\mathrm{Po}$ and
${}^{218}\mathrm{Po}$ channels led to compatible results; the following radon
emanation activities were measured:

$\displaystyle\mathcal{R}^{\text{ElringKlinger}}=61_{-19}^{+18}\,\mathrm{\frac{\upmu
Bq}{m^{2}}}\quad\text{and}\quad\mathcal{R}^{\text{Fluorseals}}=34_{-15}^{+14}\,\mathrm{\frac{\upmu
Bq}{m^{2}}}.$ (5.2)

The systematic uncertainties from the calibration are a factor of ten smaller
than the statistical uncertainties and are omitted in the following. A second
measurement of the Fluorseals sample yielded upper limits of
$43\,\mathrm{\upmu Bq}/\mathrm{m^{2}}$ and $51\,\mathrm{\upmu
Bq}/\mathrm{m^{2}}$ for the ${}^{214}\mathrm{Po}$ and ${}^{218}\mathrm{Po}$
channels, respectively, in agreement with the first measurement The Fluorseals
detection corresponds to a total sample emanation activity of
$84_{-37}^{+35}\,\mathrm{\upmu Bq}$ and thus lies only slightly above the
theoretical optimum sensitivity of the detector of $59\,\mathrm{\upmu Bq}$,
given in Equation (4.5). Statistical fluctuations can easily move the measured
activity above or below the detector’s significance limit.

For reference, the PTFE reflectors used in the XENON1T dark matter experiment
exhibit an emanation activity of $(24\pm 5)$ $\mathrm{\upmu
Bq}/\mathrm{m}^{2}$ [21], which is comparable to the value inferred for the
Fluorseals sample. These measurements were conducted with the miniaturized
proportional counter infrastructure at the Max-Planck-Institut für Kernphysik
in Heidelberg; a sample with a total mass of $32\,\mathrm{kg}$ and a surface
area of $4\,\mathrm{m}^{2}$ was examined. The XENON1T reflectors were treated
with a new diamond milling head to achieve a smooth surface to optimize the
material’s light reflectivity [31]. Since radon emanation strongly depends on
the surface properties of a sample, it is possible that this particular
treatment led to an improved micro-porosity of the surface compared to the one
of our saw-milled grooves. With an emanation activity of $(12{+5\atop-10})$
$\mathrm{\upmu Bq}/\mathrm{m}^{2}$, the PTFE used in the LZ dark matter
experiment is also cleaner. A very large sample of it was measured with the
R.E.S. facility mentioned above [22].

##  6 Conclusion

The background of many rare-event search experiments is affected by the
radioactive decays of ${}^{222}\mathrm{Rn}$ and its daughters. As radon
emanates from any detector construction material, quantifying the emanation
rate of potential materials is crucial for optimizing the background of the
next generation of low-background experiments. In this work, we present the
design and performance of the MonXe radon emanation detector, designed to
contribute to the radiopurity assay programs of the future DARWIN [6] and XLZD
[7] astroparticle physics observatories.

MonXe’s detection concept is based on the spectrometric measurement of alpha
decays of polonium atoms, which have been electrostatically collected on the
surface of a silicon PIN photodiode. By utilizing cryogenic physisorption
traps, the radon atoms emanating from a sample are transferred into a separate
detection vessel. The alpha decays of the ${}^{222}\mathrm{Rn}$
daughters${}^{218}\mathrm{Po}$ and ${}^{214}\mathrm{Po}$ are measured with an
energy resolution of $1.5\,\%$ and an detection efficiency of about 36% per
isotope. The sensitivity of the instrument, taking into account the measured
backgrounds in the emanation and detection vessels, was determined as
$59\,\mathrm{\upmu Bq}$ (at $90\,\%$ C.L.). The performance of the MonXe radon
emanation detector was demonstrated by determining the radon emanation of
high-activity commercial zeolite granulate and two samples of semi-finished
PTFE, with emanation rates close to the instrument’s sensitivity.

At the time of writing, the MonXe detector is operated manually. However, it
is foreseen to automate the radon sample transfer from the emanation into the
detection vessel by controlling new pneumatic valves and mass flow controller
by the slow control system. A second independent emanation vessel is currently
under construction and will decrease turnover times between measurements. The
second vessel will be electropolished to possibly improve its intrinsic
emanation, which currently limits the instrument’s sensitivity.

## Acknowledgments

This work was supported by the European Research Council (ERC) grant No.
724320 (ULTIMATE). We gratefully acknowledge H. Simgen and J. Westermann (Max-
Planck-Institut für Kernphysik, Heidelberg, Germany) for performing the
reference calibration measurements and G. Zuzel (Jagiellonian University,
Kraków, Poland) for providing the calibration source. We also thank the teams
of the mechanical and electronics workshops of Institute of Physics, Freiburg,
and in particular R. Mori for the development of the preamplifier. Finally, we
thank all the Bachelor students and interns who contributed to commissioning
the detector: J. Alt, W. Boemke, L. God, R. Kirsch, and V. Lieb.

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