# Procedural Text Mining with Large Language Models

Anisa Rula<EMAIL_ADDRESS>University of BresciaVia Branze
38BresciaItaly25123 and  Jennifer D’Souza<EMAIL_ADDRESS>TIB Leibniz
Information Centre for Science and TechnologyWelfengarten
1BHannoverGermany30167

(2018)

###### Abstract.

Recent advancements in the field of Natural Language Processing, particularly
the development of large-scale language models that are pretrained on vast
amounts of knowledge, are creating novel opportunities within the realm of
Knowledge Engineering. In this paper, we investigate the usage of large
language models (LLMs) in both zero-shot and in-context learning settings to
tackle the problem of extracting procedures from unstructured PDF text in an
incremental question-answering fashion. In particular, we leverage the current
state-of-the-art GPT-4 (Generative Pre-trained Transformer 4) model,
accompanied by two variations of in-context learning that involve an ontology
with definitions of procedures and steps and a limited number of samples of
few-shot learning. The findings highlight both the promise of this approach
and the value of the in-context learning customisations. These modifications
have the potential to significantly address the challenge of obtaining
sufficient training data, a hurdle often encountered in deep learning-based
Natural Language Processing techniques for procedure extraction.

knowledge representation, knowledge capture

††copyright: acmcopyright††journalyear: 2018††doi:
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##  1\. Introduction

Extracting complex knowledge from unstructured sources is a challenge: in the
industrial domain, for example, troubleshooting documents may contain the
description of long and articulated procedures (i.e., sequences of steps to be
performed in a precise order and under specific conditions) and those natural
language instructions may be represented in very different textual forms, thus
making it hard for a knowledge extraction algorithm to correctly identify and
structure the relevant information. Oftentimes, automatic extraction is
followed by manual revision of domain experts. In any case, all machine-
learning-based methods require training data which is often not readily
available, therefore novel approaches are emerging to exploit interactive
dialogues and language models (Bellan et al., 2021).

Extracting procedural knowledge from human natural language instructions is a
challenging task. Firstly, natural language instructions are not interpretable
by machines. In the easiest case, the instructions are given as numbered lists
which can easily be identified. However, complications arise when the document
contains procedures in different forms: a list without numbers, an indented
text or simply a full text in which the different steps are connected by
conjunctions like ”then”, ”afterwards”, etc. Secondly, procedures can either
be composed of only simple steps or contain other sub-procedures that are
located elsewhere in the document. Thirdly, the procedures can differ
substantially from one document to the other because of different authors’ and
editors’ styles, but the goal would be to integrate information from different
documents. Specifically, we investigate the potential of large language models
(LLMs) in the context of extracting procedural knowledge from unstructured PDF
documents. LLMs demonstrate remarkable capabilities in natural language
processing, surpassing those possible using conventional symbolic AI and
machine learning technologies (Mahowald et al., 2023). Nevertheless, these
models often lack knowledge of nuanced, domain-specific details and are
susceptible to hallucinations.

This paper investigates the practical application of LLMs, with a particular
focus on the advanced GPT-4 (Generative Pre-trained Transformer 4), to address
the complex task of extracting procedures from unstructured PDF documents. The
core of our research revolves around an incremental question-answering
methodology, with a specific emphasis on harnessing LLMs in both zero-shot and
in-context learning scenarios. Our study is structured to encompass two
distinct approaches to in-context learning. The first approach involves the
incorporation of an ontology containing definitions and procedural steps,
while the second approach integrates a limited dataset tailored for few-shot
learning. This comprehensive investigation not only highlights the
considerable potential of our chosen approach but also underscores the
critical role played by customised in-context learning. These tailored
modifications are poised to make significant progress in tackling a persistent
challenge within deep learning-based NLP techniques: the scarcity of essential
training data for procedure extraction.

Aligned with our exploration, three fundamental research questions guide our
investigation:

  * •

RQ1: How well is ChatGPT4 able to list the steps and substeps in the text
versus the ontology settings?

  * •

RQ2: Is in-context learning beneficial for procedural text mining?

  * •

RQ3: Regardless of the ontology instantiation, is ChatGPT4 capable of the
correct application of the ontology?

Through systematic empirical inquiry, this study not only contributes to the
enhancement of procedural text mining but also offers insights into the
capacity of in-context learning enhancements to surmount the constraints
stemming from inadequate training data. The significance of this research
extends beyond procedural extraction, resonating within the broader landscape
of NLP applications and cultivating the evolution of more sophisticated and
adaptable information retrieval systems. Our code and dataset is publicly
released https://github.com/jd-coderepos/proc-tm/.

The paper is structured as follows. We begin with a motivating example for our
work in section 2; then we describe the procedural ontology in section 3. In
section 4, we propose our approach and introduce the experimental dataset used
for the task of procedural text mining in section 5. Our experimental results
are discussed both quantitatively and qualitatively next in section 6. Finally
a brief discussion on related work is offered in section 7 and concluding
remarks in section 8.

##  2\. Motivating example

Extracting the relevant procedure from the manuals becomes crucial in every
context. Consider in a specific use case scenario a shop floor operator of the
raw materials supplier who needs to follow a maintenance procedure with the
gear head lathe machinery and thus he needs to interact with the maintenance
manual. The procedure comprises a linear sequence of activities that must be
completed in a specific order to address maintenance tasks and ensure optimal
machine performance (see Figure 1). The shop floor worker may need to answer
some questions for which a simple keyword-based search in the document is not
sufficient:
\- What are the steps involved in performing routine maintenance on machinery
gear head lathe?
\- Are there any sub-procedures or specialised steps for troubleshooting
specific issues during maintenance procedures for machinery gear head lathe?

Figure 1. Example of a procedure.

To overcome the limitations posed by search methods reliant on keywords, it
becomes necessary to extract and represent the procedural knowledge graph by
means of a vocabulary that covers domain concepts and domain-specific terms
used within procedures. This knowledge is extracted and represented with the
support of an in-context learning strategy that enables the customisation of
LLMs in a few-shot fashion. Their integration renders knowledge engineering
accessible to individuals without specialised expertise in formal
representation languages. This knowledge can be used to query semantic
procedural data to answer queries that were not possible in the traditional
unstructured form. Further, by querying enabled over semantically structured
procedures, computers can now more intelligently assist others in efficiently
managing, understanding, and executing procedures.

##  3\. Ontological representation of procedural knowledge

In this section, we discuss our proposed vocabulary that translates aspects of
procedures from unstructured text into equivalent machine-actionable
structured representations. The Procedure vocabulary encompassed within the
K-Hub Ontology111https://knowledge.c-innovationhub.com/k-hub/ plays a crucial
role in defining the foundational concepts and relationships essential for the
formal representation of procedures delineated within manuals (Rula et al.,
2023). The vocabulary is designed to define the relationships and structure
between plans and steps in a procedural context. It allows for specifying the
sequence of steps within a plan, associating steps with plans, indicating the
first and last steps of a plan, and decomposing steps into sub-plans. An
explanation of the key elements is given below:

Properties

  * •

p-plan:isStepOfPlan, specifies that a step is part of a plan. It defines the
relationship between steps and plans.

  * •

kh-p:nextStep, indicates the sequence of steps within a plan. It associates a
step with the next step that must be performed.

  * •

kh-p:startsWith, associates a plan with its initial step. This indicates the
first step in a plan.

  * •

kh-p:endsWith, associates a plan with its last step. This indicates the final
step in a plan.

  * •

kh-p:isDecomposedAsPlan: Specifies that a step can be decomposed into a sub-
plan. It associates a step with a plan that must be executed as a step of
another plan.

Classes:

  * •

p-plan:Step, represents the concept of a step in a procedure or plan. Each
atomic activity is an instance of the Step concept.

  * •

p-plan:Plan, represents the concept of a plan, which is composed of steps,
which must be executed in a given order. A procedure is an instance of the
Plan concept.

Figure 2. Procedure ontology.

The ontology in Figure 2 can be used to represent and query procedural
knowledge in a structured and standardised manner, making it easier to manage
and reason about procedural data. It aligns with the P-Plan ontology (Garijo
and Gil, 2012) and includes additional properties specific to procedural
relationships.

Listing 1 RDF knowledge graph represents the ”Motor Belts and Spindle
Alignment” procedure as a plan composed of steps, including the relationships
between steps and the plan’s start and end points. It also defines a
subprocess for spindle alignment within the main procedure.

Listing 1: RDF/XML Listing

⬇

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"

xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#"

xmlns:kh-p="https://knowledge.c-innovationhub.com/k-hub/procedure#"

xmlns:p-plan="http://purl.org/net/p-plan#">

<!-- Step 11.3.3: Tailstock Alignment \-->

<rdf:Description rdf:about="kh-p:Step11_3_3">

<rdf:type rdf:resource="p-plan:Step"/>

<rdfs:label>Tailstock Alignment</rdfs:label>

<rdfs:comment>Complete the previous task. Align the tailstock

according to provided instructions.</rdfs:comment>

<kh-p:nextStep rdf:resource="kh-p:Step11_3_4"/>

<kh-p:isStepOfPlan rdf:resource="kh-p-instance:Plan11_3"/>

</rdf:Description>

<!-- Step 11.3.4: Spindle Alignment \-->

<rdf:Description rdf:about="kh-p:Step11_3_4">

<rdf:type rdf:resource="p-plan:Step"/>

<rdfs:label>Spindle Alignment</rdfs:label>

<rdfs:comment>A subprocess for spindle alignment.

</rdfs:comment>

<kh-p:isDecomposedAsPlan rdf:resource="kh-p:Plan11_3_4"/>

<kh-p:isStepOfPlan rdf:resource="kh-p-instance:Plan11_3"/>

</rdf:Description>

<!-- Plan 11.3.4: Spindle Alignment Plan \-->

<rdf:Description rdf:about="kh-p:Plan11_3_4">

<rdf:type rdf:resource="p-plan:Plan"/>

<rdfs:label>Spindle Alignment Plan</rdfs:label>

<kh-p:startsWith rdf:resource="kh-p:SubStep11_3_4_1"/>

<kh-p:endsWith rdf:resource="kh-p:SubStep11_3_4_4"/>

</rdf:Description>

<!-- SubStep 11.3.4.1: Loosen Bearing Lock Nut \-->

<rdf:Description rdf:about="kh-p:SubStep11_3_4_1">

<rdf:type rdf:resource="p-plan:Step"/>

<rdfs:label>Loosen Bearing Lock Nut</rdfs:label>

<rdfs:comment>As part of spindle alignment,

loosen the bearing lock nut.</rdfs:comment>

<kh-p:nextStep rdf:resource="kh-p:SubStep11_3_4_2"/>

<kh-p:isStepOfPlan rdf:resource="kh-p:Plan11_3_4"/>

</rdf:Description>

</rdf:RDF>

##  4\. Approach

This section provides an overview of the methodology adopted to construct
different versions of question-answering systems through in-context learning.
The primary focus during the design of conversational interactions was on the
extraction of procedures, including their constituent steps and substeps as
these form the fundamental components of any procedure. For the creation of
the conversational system, we employed GPT-4 (Generative Pre-trained
Transformer 4.0) hosted on ChatGPT-Plus, along with the
https://askyourpdf.com/ plugin for GPT-4. This choice was based on GPT-4 as a
cutting-edge standard in LLMS. However, given that raw pre-trained language
models may not seamlessly align with specific tasks, we employed in-context
learning through prompting to customise the native model to varying extents.
The following sections will provide detailed insights into the strategies
adopted for formulating query templates and the customised models functioning
as conversational systems.

###  4.1. Prompting and questioning

Figure 3. Question templates and their ordering

Figure 3 illustrates five distinct queries formulated for information
extraction purposes. The questions, and therefore construction, are posed in
an incremental manner. First, we ask questions about the list of steps and
substeps and then we ask questions regarding aggregations and comparisons, and
finally, the precedence relations among steps. We discuss shortly each of
these templates:

Template 1 (List): Requests a detailed list of steps for a specific procedure,
including any step or substep for each main step.
Template 2 (Counting): Asks for the total number of steps within a procedure.
Template 3 (Comparison): Aims to identify the procedure with the maximum
number of steps among a set of procedures.
Template 4 (Nested Procedures): Asks about the presence of sub-procedures or
substeps within the main procedure.
Template 5 (Sequence): Asks for the next step after a specific step in a
procedure and provides the step that comes immediately after the specified
step.

These questions were structured incrementally to address the model’s
limitations in handling complex queries that encompass the entire procedural
structure. This incremental approach shares similarities with the iterative
process of crafting conceptual models often involving interactions with domain
experts. It paves the way for versatile pipelines by combining diverse diverse
incremental inquiries.

###  4.2. In-context learning customisations

”Contextual learning” involves training models to understand the contextual
environment within which information is presented. This context can encompass
surrounding text, images, or other data facets. Contextual learning is
particularly important for tasks like NLP, where the meaning of a word or
phrase can vary based on its surrounding context.

Learning approaches: Raw vs Zero-shot vs 2-shot. The initial learning approach
aligns with ”zero-shot learning.” where the pre-trained GPT-4 model can
generalise its knowledge to tasks it has not been explicitly trained on but
gains its understanding by providing the model with some initial context. In
contrast ”2-shot learning” falls between traditional supervised learning and
zero-shot learning. This approach involves training a machine learning model
with just two labelled examples per class, enabling the model to generalise
and make predictions for new instances even with limited labelled data.

Contextual knowledge definitions. Contextual knowledge is provided through the
identification of the specific domain and intensional definitions of the
procedure elements to be extracted. Relying on intensional definitions offers
the advantage of compactness without requiring the provision of examples.
These definitions consist of the main concepts and propteries defined in the
ontology along with definitions of Procedure, Steps and Substeps. In the
figure, each definition is labelled with the question it was used for. These
choices were made to minimise external knowledge while providing an initial
empirical assessment of using intentional definitions in the customisation of
pre-trained models.

##  5\. Our Dataset

To understand the effectiveness of our approach we select four domains where
public data are easily accessible and which cover all the challenges.

  * •

Photography, manuals that provide instructions for operating, maintaining, and
troubleshooting cameras, covering settings, capturing images or videos, lens
care, and handling various scenarios.

  * •

Manufacturing, manuals that provide instructions for production, quality
control, assembly, maintenance, and safety in various manufacturing
operations.

  * •

Medicine, manuals that provide instructions for dental instrument usage,
sterilisation, X-rays, oral hygiene instructions, and emergency protocols.

  * •

Agriculture, manuals that provide instructions for the operation, maintenance,
and safety protocols of farm equipment.

We now give details on how we extracted the documents from each data source.
First, we examine all the procedures defined in the manuals. Second, we
specifically choose procedures that adhere to a structured format suitable for
enumeration, such as numbering, bullet points, or clear indentation. Third, we
prioritise procedures to be considered procedures that are only on one page or
at most spanned across two consecutive pages. For each domain, we extract
three examples of procedures either from the same manual or different manuals.

##  6\. Results

In this section, we discuss the results obtained by leveraging ChatGPT4 for
procedural text mining w.r.t. the 5 prompting scenarios introduced in
subsection 4.1.

###  6.1. Quantitative Evaluations

The quantitative evaluations are applied only in the case of the first
prompting scenario. In this case, ChatGPT4 is asked to extract all the steps
including subtasks and substeps for a given procedure. The quantitative
evaluations entail comparing all the extracted procedures versus their gold-
standard annotations in text and ontologised formats. The results are reported
in terms of ROUGE (Recall-Oriented Understudy for Gisting Evaluation) scores
which are a set of evaluation metrics used to assess the quality of generated
text by comparing them to the desired reference text.

For each of the four domains, roughly 3 or 4 procedures are considered,
respectively, where the first prompting scenario is tested. These cumulative
results are reported in Table 1. With this we answer RQ1, i.e. how well is
ChatGPT4 ble to list the steps and substeps in the text versus ontology
settings. As we see ChatGPT4 performs fairly high in the range above 70% in
extracting the procedures from the PDFs in the text format. The scores in the
ontologized format are fairly low. In part, ChatGPT4 follows its own process
for rewriting the extracted procedure in an ontology where it sometimes leaves
out the use of prefixes and instead uses the full URL. Such differences in
writing the output can cause the ROUGE scores to reflect low similarities
between the extracted procedure and the human-written reference.

Domain Rouge1 Rouge2 RougeL Rouge-Lsum Photo-graphy text 79.6 74.4 79.4 79.6
ont. 42.2 27.9 32.7 42.1 Medicine text 94.3 87.8 93.1 94.3 ont. 47.5 38.8 43.1
47.5 Manufact-uring text 93.8 86.8 92.5 93.8 ont. 55.7 46.0 52.9 55.5 Agri-
culture text 75.4 66.5 74.3 74.4 ont. 59.3 46.4 52.5 59.1

Table 1. Rouge scores comparing the automatically extracted procedures from
the PDF manuals versus the gold-standard annotated procedures in text and
ontologized (ont.) formats.

Table 2 reports the results for the 2-shot experimental setting for both the
text and ontologised formats. With this, we answer RQ2, i.e. is in-context
learning beneficial for procedural text mining. We see that it is. Our in-
context learning 2-shot setting is as follows: ChatGPT4 is shown 2 gold-
standard annotations as answers for the desired task where it is simply asked
to comprehend the task, and the third prompt is where it is asked to perform
the task. Thus the results reported in Table 2 for the zero-shot setting is
not identical to the results in Table 1. Nevertheless comparing the zero-
shot/2-shot evaluations in the text format for the one example, we see that
the results are boosted by at least 10 points in most cases. In the ontology
setting, particular, the results are boosted by nearly 30 or more points. With
this we see that ChatGPT4 acquires the skill of the desired application of the
ontology when not just shown the ontology but also a few examples showing its
desired application.

Domain Rouge1 Rouge2 RougeL Rouge-Lsum Photo-graphy text 85.5/85 77.6/77.6
85/85 85.5/85 ont. 42.7/70.3 23.2/55.2 35.1/60.4 42.5/70.1 Medicine text
87.2/91.8 74.1/83.4 83.2/89.7 87.2/91.1 ont. 44.8/86.9 36.3/83.5 40.4/85.8
44.6/86.9 Manufact-uring text 94.5/98.7 78.8/96.1 89.3/98.7 94.5/98.7 ont.
59/78.9 51.5/69.4 56.6/73.8 58.3/78.1 Agri-culture text 74.7/92.9 62.5/79.2
73/92.2 74.1/92.2 ont. 59.1/95.5 45.2/93.2 55.3/91.6 59.1/95.5

Table 2. Rouge scores comparing the automatically extracted procedures in the
zero-shot/2-shot experimental settings from the PDF manuals versus the gold-
standard annotated procedures in text and ontologized (ont.) formats.

###  6.2. Qualitative Evaluations

The qualitative evaluations are discussed in terms of 9 observations presented
as questions.

1. With the 2-shot in-context learning setting, can one actually tailor the model to respond in a certain way or a certain format? E.g., for prompt scenario 5 in medicine, when asked for the next instruction in sequence, we would like the agent to reply with just the name or the sentence corresponding to the next instruction. However, the model in the ”raw” setting seems to respond with the next instruction, but also with the substeps or additional information like notes or warnings related to the instruction. See response where it also adds the sentence “Refer to page 17 of the document.” which is additional information in the context of the instruction sentence “CONTROL BOX INSTALLATION”. However, in the 2-shot setting, the model after seeing reference examples, responds with just the essential information for the same query as shown in-context. See response now reads “… the next step after Step 4 ”Head Installation” is Step 5 ”Control Box Installation”.”.

2. How well is the extraction of instructions handled when intermingled with images? It seems that ChatGPT4 can distinguish between content related to text descriptions and images including their captions fairly well. For instance, procedure 1 in the agricultural domain for the prompt 1 i.e. to list all steps including substeps to the procedure about “Operating the Hydrostatic Transmission” presented such a case. To produce an accurate response, the agent had to overlook the image between step 1 and 2 which it successfully did. The chatgpt response can be compared to the gold-standard response. A second successful example pertains to the “Lowering ROPS Crossbar” procedure in the following manual with two intermingled figures. Even though the extracted response from ChatGPT4 is somehow a paraphrased version of the gold-standard response, it has successfully navigated the two interspersed images.

3. How does ChatGPT4 handle the extraction of a procedure across pages? In the manufacturing domain, the instructions for ”support plate installation” span two pages. This is example 2 handling the “Support plate installation” procedure in the set of procedures. The chatgpt response for the type 1 prompt to list all steps, substeps etc. accurately reflected the expected gold-standard. Thus we see that the agent is able to assimilate information across pages while maintaining the right context. As another example from the manufacturing domain is example 4 in the context of type 1 prompt for ”Removal and installation of Mechanical seal” sub-procedure for the ”Shaft-seal maintenance” procedure. Here again, the chatgpt response aside from splitting some instruction types, is 90% in accordance to the gold-standard. It has successfully extracted the first step from the first page and the remaining steps from the next page of the manual. For the same instruction, when prompted in the 2-shot setting with examples of the desired response provided in the prompt, the chatgpt response exactly matches the gold-standard. This behaviour is consistently observed for other domains as well.

4. Apart from text generation discrepancies, has ChatGPT4 completely overcome the limitation of LLMs of not being truly capable of mathematical logic or reasoning but simply still relegated as very powerful statistical text generators (Wei et al., 2022; Srivastava et al., 2022)? In the manufacturing domain, for type 3 comparison prompts, the language model was asked to compare the number of instructions given two contexts with procedures and reply which context had more instructions. Intriguingly, one of the contexts contained two procedures. Thus the task to the language model was to consider each independent procedure within each context and return which one had the most instructions. This is demonstrated in Figure 4. The incorrect model response over a relatively simply reasoning task offers further credence to the conjecture: are large language models simply very good statistical generators and otherwise incapable of truly reasoning?

Figure 4. Erroneous response from ChatGPT4 over a relatively simple
mathematical reasoning task. The first sentence is correct: ”Removal and
installation of Mechanical seal” (9 main steps) $<$ both procedures from
Context1 (8 main steps + 5 main steps). However the second sentence reflects
the reasoning error: ”Install the support plate with a pit cover” (8 main
steps) ¿ ”Removal and installation of Mechanical seal” (9 main steps) ¿
”Install the support plate without a pit cover” (5 main steps). Instead it
should have been: the procedure in Context2 has more steps than either of the
two procedures in Context1.

5. Are our instructions completely unambiguous to the model? We observed that in some cases they might be ambiguous. For instance, in prompt 4, i.e. probing the model to produce nested instructions setting, our prompt reads as follows:

Question: Can you provide a detailed list of the substeps of Step X in the
given Context which refers to ”[name]” procedure? If there are no substeps,
please reply with ”no substeps”. Answer:
---

Sometimes, if the main instruction itself is a rather long sentence, e.g., the
generated step 7 here, the prompt above proves ambiguous where ChatGPT4 splits
the long instruction into a sequence of steps as in this response. In-context
learning alleviates ambiguity. In our 2-shot setting, the same prompt results
with the correct response

6. Can ChatGPT4 effectively extract information from manuals in a 2-column format, processing each column accurately?

We find that it can extract content to create a response cleanly column-by-
column. However, if queried about a procedure described in the first column,
it may not be able to detect the end of the procedure as relegated just to
that one column. It could continue generating text even including a new
procedure starting in the second column of the same page. E.g., the following
manual on operating tractors has two distinct procedures, i.e. Operating the
Hydrostatic Transmission (listed completely in column 1) and Using Cruise
Control - 1026R (listed completely in column 2). In the prompt 1 scenario,
ChatGPT4 when asked to list steps for “Operating the Hydrostatic Transmission”
(see example 1), it successfully extracts the relevant text but continues
extracting text even for the “Cruise Control” procedure and lists its steps as
substeps to the last step for “Operating the Hydrostatic Transmission.” See
response here.

7. Is ChatGPT4 able to comprehend the correct application of the ontology even though the generated response does not match the gold-standard? This is also our RQ3.

A step in the ontology is described as follows: first given an instance name
and initialised as a Step type of a Plan. E.g., “kh-p-instance:Step2 a
p-plan:Step ;” Next the step is assigned a label. E.g., “rdfs:label ”Attach
the hoses to the flowmeter ;”. Then the next step to the given step is
specified. This can be a substep if the given step has substeps. E.g.
“kh-p:nextStep kh-p-instance:SubStep2_1 ;”. Then the step in question is
initialised as an instance of the corresponding main plan it belongs to. E.g.,
“kh-p:isStepOfPlan kh-p-instance:Plan1 ;”. For those steps with substeps, the
name of a subplan is specified. E.g., “kh-p:isDecomposedAsPlan kh-p-
instance:SubPlan2 .” This subplan will be the plan to which substeps of the
main plan are initialised. For a complete example, see lines 48 to 52 in the
gold-standard example 1 in the medical domain. Note if a step does not have
substeps then the specification of the next step goes to the next main step
and not the substep. In addition, the line specifying the decomposed plan of a
step will not be present. Now looking at the ChatGPT4 response for the same
procedure, and the same instruction step in lines 17 to 27, we see that it has
incorrectly specified the step in turn leading to an incorrect application of
the ontology. First as a next step, instead of specifying the sub step, it
used the subplan. Then for the substeps instead of specifying the decomposed
plan for the main step at the step specification, it specifies it at the plan
level which was initialised as the next step of the main step leading to
something meaningless and not machine-actionable.

An incorrect application of the ontology is also found in chatgpt response to
procedure 3 “Installation of FM Type.” Specifically take a look at the “Pole
Assembly Installation” main step lines 12 to 23. The main step is initalised
as a type of Plan. There is no specification of the decomposed plan with its
substeps. Thus in the zero-shot setting ChatGPT4 cannot be expected to
correctly apply the ontology. Promising enough, this changes in the 2-shot
setting, where ChatGPT4 is showed two examples of the correct application of
the ontology. Then via in-context learning, it is able to correctly apply the
ontology. See the ChatGPT4 response in the 2-shot setting for the same
example, as a perfect application of the ontology, thereby showing that
ChatGPT4 can be successfully guided via in-context learning toward the correct
application of an ontology. Thus in a sense, at least for the ontology
setting, it appears necessary to query ChatGPT4 via the in-context learning
methodology showing it some examples with the correct application of the
ontology.

As an observational note, in simpler ontology application scenarios, i.e. when
there are just steps with no substeps, it does very well. E.g. from
agriculture, for example 4, the ChatGPT4 ontologised response for the prompt 1
scenario to list steps is almost identical to the gold-standard.

8. Has ChatGPT4 hallucinated? One needs to still be wary of the use of ChatGPT4 as it can still entirely hallucinate content. Consider the prompt 4 listing of nested procedures scenario, in the 2-shot setting, despite precise instructions as well as in-context example, when asked to list the substeps of step 3 for “installation of the FM type” procedure, ChatGPT4 still hallucinated the entire response. Compare this with the expected answer for substep3_1, 3_2, and 3_3.

9. Is the 2-shot setting infallible, or does the model occasionally produce unexplained hallucinations? In the 2-shot setting, we have also observed scenarios where the model has hallucinated text. While it may have grasped the ontology components and application relatively well, for reasons we found unexplainable the text generated as steps were entirely made up and could not be found in the manual. E.g., the generated ChatGPT4 response in the 2-shot setting compared with the ontologised gold-standard or text-based gold-standard.

##  7\. Related Work

In prior research, it’s vital to examine the methods used for procedural text
mining and the incorporation of Large Language Models (LLMs) for knowledge
extraction.

Knowledge Extraction from Unstructured Sources

Extracting complex knowledge from unstructured sources presents several
challenges in several domains. This variability complicates the accurate
extraction and structuring of relevant information through knowledge
extraction algorithms which are usually applied to specific domains (Jaradeh
et al., 2021). The intricate nature of these documents requires manual review
by domain experts after automated extraction, underscoring the limitations of
machine-learning-based approaches (Dong et al., 2021). Innovative methods,
including interactive dialogues and language models, have emerged to address
the lack of readily available training data for machine learning methods
(Rebmann and van der Aa, 2021; Bertoli et al., 2022).

Procedural Text Mining and Large Language Models (LLMs) In response to the
challenges mentioned earlier, our research delves into the field of procedural
text mining, capitalizing on advancements in Natural Language Processing
(NLP). Large Language Models (LLMs) have emerged as a pivotal tool in this
endeavor, surpassing the capabilities of traditional symbolic AI and machine
learning technologies (Sun et al., 2023; Bellan et al., 2022). These models
offer a means to address the intricate and extensive nature of procedural
documents, with the potential to enhance knowledge extraction efficiency.

Integration of LLMs in Knowledge Extraction Large Language Models (LLMs)
demonstrate exceptional capabilities in natural language processing,
surpassing what conventional symbolic AI and machine learning technologies can
achieve (Mahowald et al., 2023). These capabilities have sparked a substantial
increase in proofs of concept and practical applications of LLMs, suggesting
their potential utility in various knowledge-related tasks (Min et al., 2023).
Nevertheless, the exploration of methods for effectively integrating LLMs into
structured, controllable, and repeatable approaches for the development and
deployment of such applications in production is still in its early stages and
requires further detailed consideration (Pan et al., 2023). Similarly, our
study centers on the integration of LLMs, notably the state-of-the-art GPT-4
model, in the context of extracting procedural knowledge from unstructured PDF
documents.

##  8\. Conclusion

In this study, we explored the feasibility of employing in-context learning
with pretrained language models to extract procedure elements from textual
documents. We examined the native GPT-4 model and two customised variants,
fine-tuned with procedure element definitions and limited examples.

Our findings suggest that in-context learning holds promise in addressing the
challenge of training data scarcity in natural language processing for
procedure element extraction. This approach opens up possibilities for
initiating procedure element construction from natural language text,
particularly in resource-constrained scenarios. Additionally, it underscores
the importance of domain experts in refining model-generated information,
emphasizing a human-in-the-loop approach. This research has yielded valuable
insights and lessons that will guide future investigations in this field.

## References

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  * Bellan et al. (2022) Patrizio Bellan, Mauro Dragoni, and Chiara Ghidini. 2022\.  Leveraging pre-trained language models for conversational information seeking from text.  _arXiv preprint arXiv:2204.03542_ (2022).
  * Bertoli et al. (2022) Piergiorgio Bertoli, Francesco Corcoglioniti, Chiara Di Francescomarino, Mauro Dragoni, Chiara Ghidini, and Marco Pistore. 2022\.  Semantic modeling and analysis of complex data-aware processes and their executions.  _Expert Syst. Appl._ 198 (2022), 116702.
  * Dong et al. (2021) Zihui Dong, Shiladitya Paul, Karl Tassenberg, Geoff Melton, and Hongbiao Dong. 2021.  Transformation from human-readable documents and archives in arc welding domain to machine-interpretable data.  _Comput. Ind._ 128 (2021), 103439.
  * Garijo and Gil (2012) Daniel Garijo and Yolanda Gil. 2012.  Augmenting prov with plans in p-plan: scientific processes as linked data. CEUR Workshop Proceedings.
  * Jaradeh et al. (2021) Mohamad Yaser Jaradeh, Kuldeep Singh, Markus Stocker, Andreas Both, and Sören Auer. 2021\.  Better Call the Plumber: Orchestrating Dynamic Information Extraction Pipelines. In _ICWE_ _(Lecture Notes in Computer Science, Vol. 12706)_. Springer, 240–254.
  * Mahowald et al. (2023) Kyle Mahowald, Anna A. Ivanova, Idan A. Blank, Nancy Kanwisher, Joshua B. Tenenbaum, and Evelina Fedorenko. 2023.  Dissociating language and thought in large language models: a cognitive perspective.  arXiv:2301.06627 [cs.CL]
  * Min et al. (2023) Bonan Min, Hayley Ross, Elior Sulem, Amir Pouran Ben Veyseh, Thien Huu Nguyen, Oscar Sainz, Eneko Agirre, Ilana Heintz, and Dan Roth. 2023.  Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey.  _ACM Comput. Surv._ (jun 2023).  https://doi.org/10.1145/3605943
  * Pan et al. (2023) Shirui Pan, Linhao Luo, Yufei Wang, Chen Chen, Jiapu Wang, and Xindong Wu. 2023\.  Unifying Large Language Models and Knowledge Graphs: A Roadmap.  arXiv:2306.08302 [cs.CL]
  * Rebmann and van der Aa (2021) Adrian Rebmann and Han van der Aa. 2021.  Extracting Semantic Process Information from the Natural Language in Event Logs. In _CAiSE_ , Vol. 12751. Springer, 57–74.
  * Rula et al. (2023) Anisa Rula, Gloria Re Calegari, Antonia Azzini, Davide Bucci, Alessio Carenini, Ilaria Baroni, and Irene Celino. 2023.  K-Hub: A Modular Ontology to Support Document Retrieval and Knowledge Extraction in Industry 5.0. In _ESWC 2023 Proceedings_ _(LNCS, Vol. 13870)_. Springer, 454–470.
  * Srivastava et al. (2022) Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, et al. 2022\.  Beyond the imitation game: Quantifying and extrapolating the capabilities of language models.  _arXiv preprint arXiv:2206.04615_ (2022).
  * Sun et al. (2023) Kai Sun, Yifan Ethan Xu, Hanwen Zha, Yue Liu, and Xin Luna Dong. 2023.  Head-to-Tail: How Knowledgeable are Large Language Models (LLM)? A.K.A. Will LLMs Replace Knowledge Graphs?  arXiv:2308.10168 [cs.CL]
  * Wei et al. (2022) Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, et al. 2022\.  Emergent abilities of large language models.  _arXiv preprint arXiv:2206.07682_ (2022).


# Study of the X-ray Pulsar IGR J21343+4738 based on NuSTAR, Swift, and SRG
data

A.S. Gorban$,^{1,2}$ S.V. Molkov$,^{1}$ A.A. Lutovinov$,^{1}$ A.N.
Semena$,^{1}$
1Space Research Institute of the Russian Academy of Sciences<EMAIL_ADDRESS>Profsoyuznaya 84/32  117997 Moscow  Russia
2Higher School of Economics  Myasnitskaya 20  101000 Moscow  Russia

###### Abstract

We present the results of our study of the X-ray pulsar IGR J21343+4738 based
on NuSTAR, Swift, and SRG observations in the wide energy range 0.3—79 keV.
The absence of absorption features in the energy spectra of the source, both
averaged and phase-resolved ones, has allowed us to estimate the upper and
lower limits on the magnetic field of the neutron star in the binary system,
$B<2.5\times 10^{11}$ G and $B>3.4\times 10^{12}$ G, respectively. The
spectral and timing analyses have shown that IGR J21343+4738 has all
properties of a quasi-persistent X-ray pulsar with a pulsation period of
$322.71\pm{0.04}$ s and a luminosity $L_{x}\simeq 3.3$ $\times 10^{35}$ erg
s-1. The analysis of the long-term variability of the object in X-rays has
confirmed the possible orbital period of the binary system $\sim 34.3$ days
previously detected in the optical range.

Keywords: IGR J21343+4738, X-ray sources, binary systems, neutron stars,
accretion, magnetic field

20224812798[805]

15.11.2022

## Introduction

Studying Be/X-ray binaries (BeXRBs) during both outbursts and low states is of
great interest, allowing to estimate the physical parameters of the binary
systems (see, e.g., Reig, 2011; Kretschmar et al., 2019; Mushtukov &
Tsygankov, 2022). This type of binaries is a system that consists of a
massive, rapidly rotating Be star exhibiting (Balmer and Paschen) hydrogen
emission lines in the spectrum and a neutron star, the accretion onto which is
the main source of X-ray emission in the system. Some of the BeXRBs exhibit
outbursts during which the luminosity of the sources can reach the Eddington
limit for a neutron star $L\simeq 10^{38}$ erg s-1 or more. There is also a
subclass of BeXRBs that are characterized by a persistent weak X-ray emission
and that do not show a pronounced activity. The luminosity of such systems
does not exceed $\sim 10^{35}$ erg s-1. Persistent X-ray binaries have long
pulsation periods (for example, Li et al., 2012; Tsygankov et al., 2012; Qiu
et al., 2017).

The X-ray pulsar IGR J21343+4738 was discovered by the INTEGRAL observatory
during its observations of the Galactic plane in Cygnus (Krivonos et al.,
2007; Bird et al., 2007). In December 2006 the source IGR J21343+4738 was
observed by the Chandra observatory (Sazonov et al., 2008), which allowed it
to be accurately localized and its optical counterpart to be determined. The
optical spectrum of this source exhibited intense H I and He I absorption
lines, suggesting a B-type star (Sazonov et al., 2008; Bikmaev et al., 2008).
Later, Reig & Zezas (2014) established that the optical counterpart of IGR
J21343+4738 is a B1IVe Be star that has a magnitude V = 14.1 and is at a
distance $\sim\ 8.5$ kpc. The same authors noted that a deep central
absorption between the two H$\alpha$ peaks is also present in the optical
spectrum of the Be star.

In November 2013 the source was observed by the XMM-Newton observatory. The
source was in quiescence with a flux $\simeq 1.4\times 10^{-11}$ erg cm-2 s-1
in the 0.2–12 keV energy band. A timing analysis of the XMM-Newton data
revealed pulsations with a period $\simeq$320 s (Reig & Zezas, 2014).

The orbital parameters of the binary system were estimated by Nikolaeva et al.
(2021) based on long-term photometric and spectroscopic observations with the
Russian–Turkish RTT-150 telescope. These authors found two possible orbital
periods of the neutron star around its companion, approximately 34.26 and
160.8 days, and the corresponding eccentricities, 0.36 and 0.38.

In December 2020, during the second sky survey, the Mikhail Pavlinsky ART-XC
telescope onboard the SRG observatory recorded a significant increase in the
flux from IGR J21343+4738 (Semena et al., 2020), $\sim 5\times 10^{-11}$
erg/cm2/s in the 4–12 keV energy band, compared to the flux detected in the
previous sky survey $\sim 1.3\times 10^{-11}$ erg/cm2/s. The observations of
the source in a wide energy range by the NuSTAR and Swift observatories
followed next.

In this paper we perform a detailed analysis of the data from all three
observatories to measure the timing and spectral characteristics of the source
IGR J21343+4738 and to estimate its physical parameters.

## OBSERVATIONS AND DATA ANALYSIS

The source IGR J21343+4738 was observed by the NuSTAR observatory on December
17, 2020, (MJD 59200) with an exposure time of about 27 ks (ObsID
90601339002). To investigate the pulsar in a wide energy range, it was
observed simultaneously by the NuSTAR (Harrison et al., 2013) and Neil Gehrels
Swift (Gehrels et al., 2004; Burrows et al., 2005) observatories. The data of
these observatories were taken from the HEASARC archive
(http://heasarc.gsfc.nasa.gov/).

NuSTAR (Nuclear Spectroscopic Telescope ARray) – is an observatory sensitive
to hard X-ray emission in the energy range from 3 to 79 keV. The observatory
consists of two identical co-aligned X-ray telescopes (FPMA and FPMB) with a
spectral resolution of 0.4 keV at 10 keV and about 0.9 keV at 60 keV (Harrison
et al., 2013). The data were processed with the standard NuSTARDAS v1.9.7
software provided as part of the HEASOFT v6.30.1 package with CALDB v20220426
calibrations. To obtain the energy spectra of the source and its light curves,
the data were processed with the NUPIPELINE and NUPRODUCTS procedures. The
data from each of the two modules for the source were chosen from circular
regions of radius 50, centered on the source. The background spectra and light
curves were extracted from a region of radius 70 located on the same chip away
from the source. To expand the investigated energy range, we also used the
spectral observations of IGR J21343+4738 with Swift/XRT in the 0.3–10 keV
energy band. The observations were carried out simultaneously with the NuSTAR
observatory with an exposure time of 1.6 ks. The energy spectra were obtained
by the online services (see Evans et al., 2009) provided by the UK Swift
Science Data Centre at the University of
Leicester111http://www.swift.ac.uk/user_objects/. The observations were
carried out in the photon counting (PC) mode. The data for the source were
extracted from a circular region of radius 47, centered on the source. All
energy spectra of the source were binned in energy by 25 counts per bin to use
the $\chi^{2}$ statistic for our spectral analysis. The spectra were fitted
with the XSPEC v12.12.1 package (Arnaud et al., 1999).

The source IGR J21343+4738 was observed several times during the Spectrum-
Roentgen-Gamma (SRG, see Sunyaev et al., 2021) all-sky survey. In this paper
we used data from the Mikhail Pavlinsky ART-XC telescope (Pavlinsky et al.,
2021) — an X-ray telescope with grazing incidence optics that operates in the
hard X-ray energy range 4–30 keV and allows the positions of sources in the
sky to be determined with an accuracy of $\approx 15"$, and their timing and
spectral analyses to be performed. From December 2019 to March 2022 the
telescope executed an all-sky survey program during which each position in the
sky was observed every six months. For four observations of IGR J21343+4738
performed during the all-sky survey the flux in the 4–12 keV energy band was
measured using standard data processing procedures from the ARTPRODUCTS V0.9
package with CALDB 20200401 calibration data. The fluxes were extracted from a
2 aperture. Since the effective exposure time is short and the number of
recorded photons is limited, it is difficult to perform detailed timing and
spectral analyses.

## RESULTS

###  0.1 Timing Analysis of the Emission from IGR J21343+4738

To properly determine the timing characteristics of the source, the photon
arrival times were first corrected for the Solar System barycenter using the
standard barycorr tool from the HEASOFT package. Since there are no known
orbital parameters, no correction for the orbital motion was made. The
background was subtracted from the NuSTAR light curves with a resolution of
0.1 s and then the light curves of the two NuSTAR modules were combined using
the lcmath tool (XRONOS v6.0). The pulsation period was determined using the
epoch-folding technique implemented in the efsearch tool from the HEASOFT
package. The pulsation period determined from the NuSTAR data was 322.71 $\pm$
0.04 s. The error in the period was estimated using Monte Carlo simulations of
the light curve (for more details, see Boldin et al., 2013).

Figure 1: The pulse profiles of IGR J21343+4738 in four energy bands from the
NuSTAR data in December 2020 (ObsID 90601339002).

We have researched the pulse profile in various energy bands: 3–10, 10–20,
20–40, and 40–79 keV. The light curves were constructed from the NuSTAR data
and were folded with the previously determined period of 322.71 s. Figure 1
presents the pulse profiles for the source. It is clearly seen that the
profile hardly changes with energy. A primary peak is recorded at phases
0.3–0.5, which is preserved up to 40 keV. A second small peak is also observed
up to 40 keV near phase 0.15–0.2. It can be seen that above 40 keV there is no
significant pulsed signal, which is most likely due to the low flux from the
source and, as a consequence, the shortage of statistics in the hard energy
channels. A similar pulse shape was also observed at softer energies based on
the XMM-Newton observations (Reig & Zezas, 2014).

The pulsed fraction was calculated as the ratio
($F_{max}-F_{min}$)/($F_{max}+F_{min}$), where $F_{max}$ and $F_{min}$ – are
the maximum and minimum fluxes in the background-corrected pulse profile in
the chosen energy band consisting of 16 bins. The derived dependence (Fig. 2)
demonstrates an increase in the pulsed fraction with energy, which is typical
for X-ray pulsars (Lutovinov & Tsygankov, 2009).

Figure 2: Pulsed fraction for IGR J21343+4738 versus energy based on the
NuSTAR data (ObsID 90601339002).

###  0.2 Spectral Analysis of the Source IGR J21343+4738

The average energy spectrum of IGR J21343+4738 from the NuSTAR and Swift/XRT
data is shown in Fig. 3 a. To describe the continuum of the source, we applied
several models that are used to describe the spectra of X-ray pulsars in the
XSPEC v12.12.1 package (Arnaud et al., 1999). To compare our results with the
previous study by Reig & Zezas (2014), we chose a continuum model in the form
of a power law with a high-energy exponential cutoff (powerlaw*highcut), which
also demonstrated the best quality of the fit. To take into account the
interstellar absorption, we added the tbabs component to the model. The
obtained absorption $N_{\rm H}\simeq 2.97\times 10^{22}$ cm-2 exceeds the
Galactic absorption toward the source $\sim 3.24\times 10^{21}$ cm-2 (HI4PI
Collaboration et al., 2016), suggesting the presence of additional absorption
in the system itself. Reig & Zezas (2014) used a model with the addition of a
component in the form of blackbody radiation. However, since its temperature
is 0.11 keV, there is no need to include this component in our model due to
the insufficient statistics in the soft energy bands. To take into account the
possible differences in the effective area calibrations for the NuSTAR (FPMA,
FPMB) and Swift/XRT instruments and the incomplete simultaneity of the
source’s observations by different observatories, we introduced normalization
coefficients (see Table 1). The remaining model parameters for different
instruments were fixed between themselves. As in our timing analysis, the
average spectrum is statistically significant up to 40 keV and, therefore, the
parameters given below were obtained in the energy range 3–40 keV.

Figure 3: (a) The energy spectrum of IGR J21343+4738 from the NuSTAR data in
December 2020 (ObsID 90601339002) (the green and blue dots are for the FPMA
and FPMB modules, respectively) and the Swift/XRT data (red dots). The solid
line indicates the best-fit model for the spectrum. (b) The deviation of the
observational data from the POWERLAW*HIGHCUT model.

The observed absorbed X-ray flux from the source IGR J21343+4738 in the energy
range 3–40 keV was $F_{x}=3.77_{-0.53}^{+0.09}\times 10^{-11}$ erg cm-2 s-11,
corresponding to a luminosity $L_{x}\simeq 3.3\times 10^{35}$ erg s-1 for a
distance of 8.5 kpc.

As has already been said, the tbabs*powerlaw*highecut model, which showed a
quality of the fit equal to 553.50 per 538 degrees of freedom, gave the best
fit. The model parameters are presented in Table 1. To test the hypothesis
about the presence of a cyclotron feature in the source’s spectrum, we applied
the approach proposed by Tsygankov & Lutovinov (2005). In the energy range
3–40 keV the gabs component from the XSPEC package, an absorption line with a
Gaussian profile, was added to the fitting model. The cyclotron line energy
was changed in the specified energy range with a 5 keV step, while its width
was fixed at 1 keV for line energies below 10 keV and at 2 keV for line
energies above 10 keV. The statistics improvement significance was tested
using the $\chi$-square test. Thus, in the above energy range we detected no
cyclotron lines whose inclusion would improve the quality of the spectral fit
by more than 2$\sigma$, with the upper limit on the line optical depth being
$\tau=0.18$.

This result may suggest that the cyclotron line energy lies outside the energy
range being investigated (3–40 keV), since at higher energies the statistics
is not enough for the detection of a cyclotron line in the source’s spectrum.
Thus, based on the relation for the cyclotron line energy B =
$\frac{E_{cycl}(1+z)}{11.6}\times 10^{12}$ G (Staubert et al., 2019), we can
assume that the magnetic field on the neutron star surface near the poles must
be either less than $2.5\times 10^{11}$ G or greater than $3.4\times 10^{12}$
G.

Table 1: Parameters of the spectrum of IGR J21343+4738 for the Powerlaw*Highecut model Model parameters |  |
---|---|---
$N_{H}$, $10^{22}$ cm-2 | ${2.92_{-1.03}^{+1.13}}$ |
$\Gamma$ | ${1.34\pm{0.13}}$ |
$E_{cutoff}$, keV | ${5.9\pm{0.8}}$ |
$E_{Fold}$, keV | ${22.4_{-3.9}^{+6.3}}$ |
$Flux$ (3 - 40 keV), $10^{-11}$ | $3.77_{-0.3}^{+0.04}$ |
erg cm-2 s-1 |  |
$C_{NuSTAR,FPMB}$ | $1.01\pm{0.02}$ |
$C_{Swift,XRT}$ | $0.86_{-0.14}^{+0.15}$ |
$\chi^{2}$ (d.o.f.) | 553.50(538) |

Taking into account the fact that for a number of X-ray pulsars there are no
cyclotron features in the averaged spectrum of the source, but, at the same
time, they are detected at individual neutron star rotation phases (Molkov et
al., 2019; Molkov et al., 2021), we also carried out phase-resolved
spectroscopy. To search for possible features in the spectra, depending on the
rotation phase, we divided the data into four phase intervals corresponding to
the rising pulse with a small peak (0–0.25), the primary peak (0.25–0.5), the
decaying pulse (0.5–0.75), and the minimum pulse (0.75–1.0) in the light
curve. The spectra of the four phase intervals were fitted by the same
tbabs*powerlaw*highecutmodel as that used in analyzing the averaged spectrum.
The derived model parameters of the phase-resolved spectra hardly change with
phase and have values approximately equal to those of the averaged spectrum.
There are no features associated with the possible presence of a cyclotron
absorption line.

###  0.3 Orbital Period of the Binary System

Nikolaeva et al. (2021) determined two possible orbital periods of the neutron
star around its companion, 34.26 and 160.8 days. To test these assumptions, we
collected all of the available flux measurements for the source with the
instruments onboard the SRG (ART-XC), Swift (XRT), NuSTAR, and XMM-Newton
observatories and constructed long-term light curves in the 4–12 keV energy
band. Then, they were folded with the above periods and fitted by the
corresponding sine waves (Fig. 4). It can be seen from the figure that all of
the flux measurements are acceptably described by the sine wave with the
period of 34.26 days, except for the data point at phase 0.65. The
corresponding measurement was carried out in December 2020 during the source’s
suspected outburst and, therefore, this data point was excluded from the data
series in our further fitting. The model describes the light curve with an
acceptable statistic, $\chi^{2}/d.o.f.$ = 1.6/3, for the period of 34.26 days.
The second model, with the period of 160.8 days, describes the data much more
poorly, with a statistic $\chi^{2}/d.o.f.$ = 48.7/3. Thus, the available X-ray
data are consistent with the assumption about the existence of an orbital
period $\simeq 34.3$ days in the system. However, long-term observations with
X-ray instruments aimed at determining the changes in the source’s pulsation
period associated with the orbital motion are needed for the final conclusions
to be reached.

Figure 4: The light curves of the X-ray pulsar IGR J21343+4738 from the data
of several telescopes folded with a period of 34.26 (a) and 160.8 (b) days.
The fluxes from the source at phases 0.65 (a) and 0.59 (b) (marked by the
dashed line) were obtained in December 2020 during a suspected outburst;
therefore, this data point was excluded from the data series in our fitting.

##  1 CONCLUSIONS

In this paper we analyzed the observational data for the X-ray pulsar IGR
J21343+4738 obtained by the NuSTAR observatory and the Swift/XRT and SRG/ART-
XC telescopes. We showed that the broadband spectrum of the source measured in
December 2020 in a state with a luminosity $L_{x}\simeq 3.3\times 10^{35}$ erg
s-1 could be best described by the continuum model in the form of a power law
with a high-energy exponential cutoff powerlaw*highecut. The component to take
into account the interstellar absorption that exceeds the Galactic absorption
toward the source, suggesting that part of the emission is absorbed inside the
binary system, was included in the model. We also performed phase-resolved
spectroscopy, which showed that the model parameters hardly change with phase.

Our timing analysis revealed X-ray pulsations with a period $\simeq 322.71$ s.
This value is greater than the one measured in 2013 based on XMM-Newton data
by almost 2.5 s, suggesting a neutron star spin down with a mean rate
$\dot{P}/P\simeq 1\times 10^{-3}$ yr-1.

The pulse profile is typical for persistent X-ray binaries with low fluxes
(see, e.g., Reig & Roche, 1999). In the energy range 3–40 keV it hardly
changes with energy and agrees well with the previously measured one in softer
channels. The pulsed fraction has the form of an increasing dependence on
energy typical for X-ray pulsars.

No features that could be interpreted as cyclotron lines were detected in the
spectrum of the source at energies 3–40 keV. Thus, we can assume that the
magnetic field of the source is $B<2.5\times 10^{11}$ G or $B>3.4\times
10^{12}$ G.

The available measurements of the X-ray flux from the source in the 4–12 keV
energy band are consistent with the assumption about the existence of an
orbital period $\simeq 34.3$ days in the system, but long-term observations
are needed for the final conclusions to be reached. Assuming that the pulsar
is close to the equilibrium state, and the accretion proceeds in the Bondi
quasi-spherical subsonic accretion regime (Shakura et al., 2012), the magnetic
field can be independently estimated by the orbital and rotational periods and
the average accretion rate. Such an estimate gives the value $B\sim 2\times
10^{11}$ G for the characteristic wind speed of massive systems $\sim$1
thousand km/s, which is consistent with the upper limit of magnetic field
obtained from spectral analysis.

##  2 ACKNOWLEDGMENTS

We used the data obtained with NuSTAR, a Caltech project, funded by NASA and
operated by NASA/JPL and the data provided by the UK Swift Science Data Centre
(XRT data analysis). We also used the software provided by the High-Energy
Astrophysics Science Archive Research Center (HEASARC). We used data from the
Mikhail Pavlinsky ART-XC telescope onboard the SRG observatory. The SRG
observatory was built by Roskosmos in the interests of the Russian Academy of
Sciences within the framework of the Russian Federal Space Program, with the
participation of the Deutsches Zentrum für Luftund Raumfahrt (DLR). The
Mikhail Pavlinsky ART-XC telescope was built by the Space Research Institute
of the Russian Academy of Sciences in collaboration with the Russian Federal
Nuclear Center (Sarov, Russia). The Marshall Space Flight Center, NASA (USA),
also participates in the project. The SRG spacecraft was designed, built,
launched and is operated by the Lavochkin Association. The science data are
downlinked via the Deep Space Network Antennae in Bear Lakes, Ussurijsk,
Evpatoria, and Baykonur, funded by Roskosmos. This work was supported by RSF
grant no. 19-12-00423.

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