# 101 Billion Arabic Words Dataset

name=Manel Aloui  name=Hasna Chouikhi  name=Ghaith Chaabane  name=Haithem
Kchaou  name=Chehir Dhaouadi

###### Abstract

In recent years, Large Language Models (LLMs) have revolutionized the field of
natural language processing, showcasing an impressive rise predominantly in
English-centric domains. These advancements have set a global benchmark,
inspiring significant efforts toward developing Arabic LLMs capable of
understanding and generating the Arabic language with remarkable accuracy.
Despite these advancements, a critical challenge persists: the potential bias
in Arabic LLMs, primarily attributed to their reliance on datasets comprising
English data that has been translated into Arabic. This reliance not only
compromises the authenticity of the generated content but also reflects a
broader issue—the scarcity of original quality Arabic linguistic data. This
study aims to address the data scarcity in the Arab world and to encourage the
development of Arabic Language Models that are true to both the linguistic and
nuances of the region. We undertook a large-scale data mining project,
extracting a substantial volume of text from the Common Crawl WET files,
specifically targeting Arabic content. The extracted data underwent a rigorous
cleaning and deduplication process, using innovative techniques to ensure the
integrity and uniqueness of the dataset. The result is the 101 Billion Arabic
Words Dataset, the largest Arabic dataset available to date, which can
significantly contribute to the development of authentic Arabic LLMs. This
study not only highlights the potential for creating linguistically and
culturally accurate Arabic LLMs but also sets a precedent for future research
in enhancing the authenticity of Arabic language models.

[*]<EMAIL_ADDRESS>Clusterlab Team

##  1 Introduction

The emergence of Large Language Models (LLMs) has significantly advanced the
field of natural language processing (NLP), transforming how machines
understand and generate human-like text [Yao et al., 2024]. These models,
trained on extensive datasets [El-Nouby et al., 2021], have primarily enhanced
English language processing, leading to an increase in English-centric
applications and research [Valmeekam et al., 2022]. However, the development
of LLMs for other languages, such as Arabic, has not kept pace, highlighting a
need for dedicated datasets [Singh & et al., 2024; Slim & Melouah, 2024] and
models trained from scratch to accommodate the unique linguistic features of
these languages [Maxwell & Hughes, 2006; Joshi et al., 2019].

The dominance of English in the computational linguistics landscape has led to
a technological and cultural imbalance [Robinson et al., 2023; Held et al.,
2023], with languages like Arabic receiving less attention. Despite its rich
literary heritage and over 400 million speakers, Arabic has been less
prioritized due to a reliance on multilingual models and perceived limitations
in data availability, compromising the cultural and linguistic integrity of
Arabic on digital platforms. [Abdelali et al., 2024]. The literature reveals a
gap in dedicated Arabic language resources [Muraoka et al., 2023], with most
studies defaulting to multilingual LLMs that inherently carry biases from
dominant languages like English, thus weakening the Arabic language’s unique
attributes [Magueresse et al., 2020].

To counter these challenges, our study introduces the 101 Billion Arabic Words
Dataset, encompassing over 101 billion words of pure Arabic content. This vast
dataset challenges the existing narrative of data scarcity and aims to
underpin the development of authentic Arabic LLMs. Driven by the necessity to
bridge the technological divide and promote linguistic equity, our research
seeks to equip the Arabic NLP domain [Wang et al., 2023; Abburi et al., 2023;
Li et al., 2024; Jin et al., 2024] with a dataset that truly reflects the
language’s cultural and lexical depth.

We aim to provide a comprehensive Arabic corpus that not only refutes the data
scarcity argument [Sengupta & et al., 2023] but also serves as a catalyst for
developing Arabic-centric LLMs, thus supporting linguistic and cultural
integrity in NLP technologies [Lancaster, 2024]. The introduction of the 101
Billion Arabic Words Dataset is a significant step toward rectifying the
imbalance in language technology [Jung & van der Plas, 2024], offering a
resource that enhances the development of Arabic-specific LLMs and promotes
linguistic diversity and cultural authenticity.

The subsequent sections explore the methodology implemented in constructing
the dataset, covering stages such as data acquisition, cleaning, and
preprocessing techniques. Each step is detailed to provide insight into the
processes involved in ensuring the dataset’s quality and reliability. A
dedicated section is allocated to discuss the limitations encountered
throughout our approach, acknowledging the complexities and challenges we
faced during the dataset collection while also highlighting potential avenues
for future work. The paper concludes by emphasizing the role of the dataset in
advancing culturally and linguistically authentic Arabic language models. Our
endeavor is driven by the aspiration to stimulate more inclusive natural
language processing innovations, thereby propelling progress within the Arabic
linguistic community.

##  2 Related work

The scarcity of Arabic datasets has prompted several initiatives aimed at
gathering data to support progress in Arabic Natural Language Processing (NLP)
research. Among such initiatives, the KIND dataset [Yamani et al., 2024]
emerges as a significant effort toward collecting nuanced dialectal data
through social collaboration, a method diverging from the common practice of
relying on social media extractions. Similarly, the introduction of the ArQuAD
dataset [Obeidat et al., 2024], a large-scale, expert-annotated corpus for
Arabic machine reading comprehension, highlights the critical need for
substantial, high-quality datasets to propel Arabic NLP forward.

The scarcity of datasets tailored to the Arabic language is not the sole
challenge; the cultural relevance of these datasets also plays a pivotal role
in the effective training of Large Language Models (LLMs). CIDAR [Alyafeai et
al., 2024] addresses this gap by offering an instruction dataset that mirrors
the diverse cultures across the Arab region, stepping away from the English-
centric datasets that dominate the field. However, most of these emerging
datasets, including ArabicaQA [Abdallah et al., 2024] and CIDAR [Alyafeai et
al., 2024], predominantly serve the purpose of fine-tuning existing models
rather than facilitating the development of new models from the ground up.

This didn’t prevent some researchers from exploring monolingual models, as
seen in the AraMUS project [Alghamdi et al., 2023]. Despite these efforts, the
challenge of acquiring sufficient monolingual data persists, often compelling
researchers to resort to English or multilingual datasets as a stopgap
solution.

This reliance is exemplified in the creation of Jais and Jais-chat models
[Sengupta et al., 2023], which, despite their Arabic-centric foundation,
incorporate a mix of Arabic and English texts in their pre-training phase.
This approach underscores a prevalent belief in the field—that high-quality
Arabic dataset, particularly from sources like Common
Crawl111https://commoncrawl.org, is hard to come by. Contrasting this belief,
the RefinedWeb [Penedo et al., 2023] dataset demonstrates that with
appropriate filtering and deduplication, web data alone can yield powerful
models, challenging the traditional reliance on curated corpora for training
performant models with broad generalization abilities. In a parallel endeavor,
we have developed an Arabic dataset following the principles highlighted by
the RefinedWeb initiative. Our work not only confirms the potential of web-
derived data in building models but also showcases the adaptability of these
methods specifically for Arabic, indicating a meaningful advancement in
creating more accessible and scalable data sourcing strategies for language
model training in the Arabic linguistic domain.

##  3 101 Billion Arabic Words Dataset

In this research, we explore the extensive Common Crawl archive to curate the
101 Billion Arabic Words Dataset, analyzing 39,502 compressed WET files and
extracting 0.8 petabytes (PB) of data. Common Crawl, known for its broad
spectrum of content in multiple languages, often contains a mix of accurate
and inaccurate information due to its comprehensive web crawling. The archive
is updated monthly, providing data in raw (WARC), UTF-8 text (WET), and
metadata (WAT) formats, each serving different data needs with minimal
redundancy across collections. Given its 13-year history of unrestricted web
crawling, the Common Crawl’s text quality is varied. Our approach to crafting
the 101 billion Arabic words dataset centered on meticulously selecting,
filtering, and refining Arabic content from these WET files to optimize a
dataset for NLP applications, thereby enhancing the resources available for
Arabic language processing.

###  3.1 Collection Process

In order to compile our dataset, we extracted a sub-corpus from the Common
Crawl WET files, spanning from week 39 of 2021 to week 27 of 2022. This sub-
corpus includes six distinct data splits. Each data split contains, on
average, around 90,000 segments, with typically 33,292 websites, of which
about 250 are in the Arabic language.

The collection process followed the following protocol:

  1. 1.

Select the ID: yyyy-mm-dd representing the year, month and day for the split.

  2. 2.

Download the WET file path

  3. 3.

Open the gzip file reader.

  4. 4.

Download files.

  5. 5.

Extract only Arabic websites.

  6. 6.

Store the record in the format described below.

The resulting output from this process is formatted in JSONL, where each line
represents a distinct JSON document. The corpus is distributed in this format,
with each document containing the following fields in Figure 1.

Figure 1: JSONL file fields.

###  3.2 Cleaning and preprocessing technique

In the process of creating the 101 Billion Arabic Words Dataset, we benefited
from Yamane’s formula as a means to select a limited portion of the dataset
for manual inspection. It was useful in gaining insights into the inherent
noisiness of the raw text, revealing a number of issues that could potentially
compromise the integrity and usability of the dataset. These issues included
the presence of random HTML tags inserted between paragraphs, the use of
special character Unicode, the existence of empty lines and multiple spaces,
and the inclusion of adult websites and other inappropriate content. Such
issues not only detract from the overall quality of the dataset but also raise
concerns regarding its safety and suitability for natural language processing
(NLP) tasks.

To address the challenges mentioned above and ensure the dependability and
integrity of the dataset, we developed a dataset cleaning and preprocessing
pipeline. This pipeline consisted of a series of carefully designed measures
and procedures aimed at eliminating noise, rectifying errors, and enhancing
the overall quality of the textual data.

  * •

Sample Extraction
Taro Yamane’s formula (1964) provides a practical approach to estimating
sample size in survey sampling, particularly when dealing with large
populations. In the context of a dataset comprising 116,652,000,000 documents,
applying Yamane’s formula with a desired margin of error of 1% yields a
calculated sample size of approximately 10,000 documents. This calculated
sample size represented a carefully determined subset of the population
allowing us to draw meaningful conclusions about the entire dataset. By
incorporating the population size and desired margin of error into the
formula, we could efficiently design and determine the appropriate sample size
needed to achieve reliable results. With a sample size of 10,000 documents, we
could effectively collect and analyze data while ensuring statistical
validity.

Yamane’s formula is:

$n=\frac{N}{1+N(e)^{2}}$

Given:

    * –

Population size (N) = 116,652,000,000 documents

    * –

Desired margin of error (e) = 1% (0.01)

Using Yamane’s formula, we extracted a representative subset of 10000
documents for manual inspection allowing us to identify issues such as the
presence of random HTML tags, special character Unicode, empty lines and
multiple spaces, adult websites, and inappropriate content.

####  3.2.1 Filtering

We provide a visual in Figure 2 representation of the filtering Pipeline to
remove noise, rectify errors, and ensure data integrity. It is crucial for NLP
tasks by eliminating irrelevant content.

Figure 2: Dataset cleaning pipeline

  1. a)

URLs filtering:

Given the prevalence of low-quality machine-generated spam or adult content in
Common Crawl, filtering URLs presented a significant challenge [Penedo et al.,
2023]. To address this, we initiated the process by excluding URLs containing
predefined inappropriate terms to filter out texts associated with such URLs.
We started by using a predefined text file containing a list of inappropriate
terms to scan URLs for any matches. This allowed us to identify URLs
potentially containing objectionable content. Then, we used the Rust crate
rustrict v 0.7.24 for further validation. This approach enabled a thorough
examination of the URLs, applying additional criteria or rules beyond simply
matching against the predefined list of bad words. This two-step process is
ensured through the filtering of URLs.

  2. b)

URLs Deduplication:

Duplicate data can significantly detract from the quality of Large Language
Models (LLMs), making URL deduplication a critical preprocessing step for
maintaining the integrity and coherence of Arabic datasets.
To achieve this, we adopted an approach involving the use of hash
representations of each URL along with the instance UUID. This technique
enabled us to identify duplicated URLs accurately and precisely. By leveraging
this method, we eliminated duplicate URLs effectively, reducing redundancy and
improving both data storage and retrieval processes. Such measures are
essential in establishing a solid foundation for subsequent NLP tasks,
ensuring that models developed from the dataset are built on accurate and
reliable data.

####  3.2.2 Basic Text Cleaning

  1. a)

Removing Short and long Sequences

To refine the dataset’s quality and balance, we filtered out both brief and
overly long sequences. This step played a crucial role in reducing noise and
filtering out unnecessary elements from the corpus, making it better suited
for NLP applications like text classification and sentiment analysis.
Additionally, it helped prevent data imbalance, creating a more representative
and well-structured dataset for subsequent processes.

  2. b)

Cleaning of HTML Tags and Unicode

The process of cleaning the dataset involved removing special characters,
particularly non-standard Unicode characters that might cause errors in text
processing. These characters fall outside the standard UTF-8 character set and
were removed to ensure accurate and consistent text analysis. For example,
characters like "\u+25A1" were omitted due to their lack of meaningful
information.
The Mozilla readability library, also referred to as the reader module
embedded in the most used browser, played a key role in extracting the
essential content from web pages. This module extracts plain text from web
pages, excluding social media citations and HTML tags. Social media citations
often contain formatting specific to the platform, which can vary across
different sources. Likewise, HTML tags, while essential for organizing web
pages, can introduce discrepancies in the dataset. By eliminating these
elements, we standardized the data in a way that is more conducive to
analysis.

  3. c)

Empty Lines and Repeated Spaces

We removed empty lines and extra spaces to refine the dataset and improve its
readability. The process of cleaning empty lines and repeated spaces involved
identifying and eliminating consecutive line breaks and spaces between words.
This was possible using regular expressions and string manipulation techniques
to ensure that the resulting dataset is well-structured and consistent.

  4. d)

Normalizing Unicode and Dediacritization

We refined our dataset through Unicode normalization and dediacritization,
standardizing character representations and simplifying Arabic text,
respectively. These processes not only enhanced dataset consistency and
improved readability but also ensured uniformity in text processing across
diverse encoding schemes. By normalizing Unicode, we improved integration with
numerous NLP tools, promoting cross-platform compatibility. Furthermore,
dediacritization optimized text for computational tasks and broadened access
to Arabic language resources, enhancing engagement and collaboration in NLP
applications.

###  3.3 Deduplication

We used MinHash to effectively deduplicate at the document level, creating
hash values for parts of documents (known as "shingles")[Penedo et al., 2023]
to spot nearly identical content. We also used signature-based methods at the
paragraph level, generating unique signatures for sentences to help identify
and eliminate repeated text. This approach was instrumental in generating
compact document summaries, enabling us to quickly spot and eliminate
duplicates. We enhanced this process by leveraging Rust’s type system and
memory safety to filter out duplicate unique identifiers (UUIDs).

Our initial dataset comprised 13.2 billion web pages from 440,000 gzip sub
splits of Common Crawl, with around 110 million being Arabic websites. This
large and diverse collection was then carefully refined. By focusing on URL-
level uniqueness, we reduced the dataset to 99 million unique Arabic web
pages. Post-deduplication, we condensed our dataset to 89.1 million web pages,
amounting 0.4 terabytes (400 GB).

###  3.4 Tools

For normalization and dediacritization, we used the Camel tools library,
version 1.5.2 [Obeid et al., 2020],which is a comprehensive open-source Python
Toolkit for Arabic Natural Language Preprocessing created by NYU Abu Dhabi.
This toolkit includes a wide range of functionalities such as preprocessing,
morphological modeling, dialect identification, named entity recognition, and
sentiment analysis, which were important in our text processing efforts.

For the basic cleaning of our dataset, we leveraged the capabilities of Tnkeeh
v0.0.9 [Alyafeai & Al-Shaibani, 2020]. This tool is tailored for the
preprocessing of Arabic text and offers a suite of features to improve text
quality and uniformity. Tnkeeh helps with various text cleaning tasks, such as
Unicode normalization and HTML tag removal, providing a groundwork for
subsequent data processing stages.
For our dataset preprocessing pipeline, we incorporated Rust to ensure memory
safety and consistency checks, key for the computationally intensive NLP tasks
at hand. To elevate our processing speed to unmatched levels, we used
distributed computing techniques via Redis, The Redis pub/sub system
facilitated the orchestration of task allocation and resource management,
optimizing our preprocessing operations. Additionally, Redis’s in-memory
storage capabilities granted us immediate access to intermediate data,
avoiding the slowdowns associated with disk I/O operations and substantially
boosting our workflow’s efficiency. This method ensured low latency, enabling
our pipeline to swiftly access data needed frequently and proceed without
delays through subsequent processing stages. This approach allowed us to cut
down our preprocessing time by 40 times compared to traditional techniques.

###  3.5 Environments

To streamline the integration and processing of our dataset, we configured
essential components to establish a conducive environment. This setup was
crafted to promote seamless interaction between our dataset and processing
tools, facilitating efficient data handling and analysis. Our method entailed
the selection of compatible software tools, the enhancement of hardware
resources, and the implementation of dependable data management practices.
This resulted in the creation of a cohesive system that effectively supported
the complex requirements of large-scale data processing. Our architectural
design ensured the dataset’s accessibility and usability for a wide array of
computational tasks, thereby boosting the efficiency and productivity of our
research efforts.

  * •

Virtual Machine:
We leveraged a virtual machine environment, providing scalability and
flexibility to accommodate varying computational demands while maintaining
cost-effectiveness. We opted for AWS EC2 instances (see Table 1).

Instance Type | vCPU | Memory (RAM) | Storage | Number of instances | Region
---|---|---|---|---|---
g5.4xlarge | 16 | 64 GiB | 19 TB | 1 | us-east-1
t2.2xlarge | 8 | 32 GiB | 4 TB | 10 | us-east-1

Table 1: Virtual Machine Configurations.

  * •

Operating System:
We configured the virtual machine with Ubuntu 22.04 LTS (64-bit) as the
operating system.

  * •

GPU Acceleration:
We used GPU resources to accelerate data processing and analysis, enabling
quicker insights and model training for large-scale datasets. To promote R&D
activities, we made use of an A10G GPU equipped with 24 VRAM, providing
adequate computational power for complex tasks.

  * •

Programming Language:
We installed Python v3.10 and Rust v1.77.1 to support data processing and
high-performance computing tasks. We also used Anaconda distribution to manage
Python packages, providing an extensive ecosystem for data manipulation and
analysis. We also used Rust for specific performance-critical operations to
enhance processing efficiency in the cleaning pipeline.

  * •

Cloud Storage:
We configured our AWS storage volumes to ensure reliable performance during
data handling and processing activities. We allocated the volumes to 16,000
Input/Output Operations Per Second (IOPS), a specification carefully chosen to
boost the speed of communication between the storage units and the
computational resources. This high IOPS setting was crucial for reducing
latency in data-intensive operations, thus contributing to a more responsive
computational environment.

To manage potential throughput bottlenecks, we configured the system’s
throughput setting to 1000 Mebibytes per second (MiB/s) to ensure better
distribution of bandwidth across various processes. This prevents any single
task from monopolizing the system’s data transfer capabilities, thereby
maintaining optimal performance and stability. This method of resource
allocation contributes to enhancing overall system efficiency and supports the
consistent execution of multiple simultaneous processes.

  * •

Object Storage:
In the context of optimizing network performance, the placement of EC2
instances and S3 buckets within the same AWS region plays critical role.
Firstly, reduced latency occurs as data transfer happens within AWS’s internal
network infrastructure, minimizing geographical dis- tances and reliance on
the public internet. This physical proximity ensures minimal delays in data
transmission. Secondly, AWS offers a network backbone that facilitates high-
bandwidth connectivity, enabling faster data transfers compared to inter-
region or public internet con- nections. Thirdly, AWS eliminates data transfer
charges for communication between EC2 instances and S3 buckets within the same
region, resulting in potential cost savings. Lastly, optimized network routing
within a region, including low latency between Availability Zones, further
enhances communication speed between services. Collectively, these factors
contribute to the fast and efficient communication between EC2 and S3 when
configured in the same AWS region, making it a recommended practice for
enhancing performance and reducing costs in AWS architectures.

##  4 101 Billion Arabic Words Dataset Analysis

  * •

Analysis of URL Filtering Efficiency:
Our findings indicated a successful URL filtering, with only 4% of the total
URLs classified as undesirable or potentially harmful. This achievement
reflects the effectiveness of our filtering algorithm in accurately discerning
and excluding URLs that do not align with the research criteria, thereby
maintaining the dataset’s quality and relevance.

  * •

Geographic Distribution of Filtered URLs:
Investigating the domain name distribution provided insight into the
geographical origins of the content within our dataset. Predominantly, the
filtered URLs from Arabic domains originated in Saudi Arabia, accounting for
the highest proportion within the top 10% of our dataset. This finding not
only underscores the strong online presence of Saudi Arabian domains but also
indicates the importance of focusing on this region for more refined content
filtering in future studies.

Figure 3: Distribution of filtered URLs, beginning with the initial collected
dataset and narrowing down to the final dataset of 101 billion Arabic words
(10% Arabic domains)

These findings are graphically represented in Figure 3, which illustrates the
distribution of filtered URLs across different geographic regions, with
particular emphasis on the prevalence of Saudi Arabian domains. The graphical
representation in Figure 4 illustrates the geographic distribution of filtered
URLs, emphasizing the significant contribution of Saudi Arabian domains. This
visualization aids in comprehending the extent of regional influence on the
dataset’s composition.

Figure 4: Geographical Distribution of Arabic Websites Categorized by Country
Domain Names

  * •

Content Distribution Across Domains:

Figure 5 describes the content distribution among various domains,
highlighting a noticeable scarcity of content from North African Arab
countries. This imbalance points to the necessity for more sophisticated
content filtering strategies that consider the regional disparities in digital
content availability and representation.

Figure 5: Content distribution across various domains.

##  5 Limitations

Despite employing state-of-the-art techniques for preprocessing and assembling
our dataset, a significant limitation of our research remains the lack of
direct evaluation of the dataset’s cleanliness and effectiveness. The most
robust method to thoroughly assess these qualities involves training a model
using the dataset as the primary corpus. This step would offer definitive
evidence of the dataset’s capacity to support the development of an effective
Arabic Large Language Model that is culturally attuned. Due to computational
constraints, we were unable to proceed with this crucial phase of model
training. Consequently, the performance and applicability of our dataset for
various NLP tasks remain untested through practical model training
experiences. Acknowledging this gap is essential, as it highlights an
important area for future work where, with sufficient computational resources,
the dataset can be comprehensively evaluated to confirm its quality and
usefulness for addressing a wide array of NLP challenges.

Ensuring the appropriateness of our dataset’s content presented a significant
challenge in our project. It was essential to eliminate harmful, biased, or
inappropriate content for ethical utilization. Nonetheless, relying solely on
standard URL filtering techniques may have limitations in capturing all
sensitive subjects comprehensively.

##  6 Conclusion

Our paper introduced the 101 Billion Arabic Words Dataset, a curated corpus
that is drawn from Arabic web content. By making this dataset accessible on
HuggingFace, we extend an invitation to the international NLP community to
explore its rich content, with the hope that it will drive forward research
and stimulate the development of new Arabic language models. This initiative
is expected to fill the gap in the availability of high-quality, large-scale
Arabic language resources, thereby accelerating progress in Arabic NLP. Future
work will focus on analyzing the dataset’s diversity to systematically
document the range of topics and narratives that reflect the richness of
Arabic culture and language. Through this initiative, we aim to broaden the
scope of available Arabic language resources, thus supporting the creation of
more sophisticated and accurate language models.

## References

  * Abburi et al. [2023] Harika Abburi et al.  Generative ai text classification using ensemble llm approaches.  _arXiv preprint arXiv:2309.07755_ , 2023.
  * Abdallah et al. [2024] Abdelrahman Abdallah, Mahmoud Kasem, Mahmoud Abdalla, Mohamed Mahmoud, Mohamed Elkasaby, Yasser Elbendary, and Adam Jatowt.  Arabicaqa: A comprehensive dataset for arabic question answering.  _arXiv preprint arXiv:2403.17848_ , 2024.
  * Abdelali et al. [2024] Ahmed Abdelali et al.  Larabench: Benchmarking arabic ai with large language models.  2024\.
  * Alghamdi et al. [2023] Asaad Alghamdi, Xinyu Duan, Wei Jiang, Zhenhai Wang, Yimeng Wu, Qingrong Xia, Zhefeng Wang, Yi Zheng, Mehdi Rezagholizadeh, Baoxing Huai, et al.  Aramus: Pushing the limits of data and model scale for arabic natural language processing.  _arXiv preprint arXiv:2306.06800_ , 2023.
  * Alyafeai & Al-Shaibani [2020] Zaid Alyafeai and Maged Al-Shaibani.  ARBML: Democritizing Arabic natural language processing tools.  In Eunjeong L. Park, Masato Hagiwara, Dmitrijs Milajevs, Nelson F. Liu, Geeticka Chauhan, and Liling Tan (eds.), _Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)_ , pp. 8–13, Online, November 2020. Association for Computational Linguistics.  10.18653/v1/2020.nlposs-1.2.  URL https://aclanthology.org/2020.nlposs-1.2.
  * Alyafeai et al. [2024] Zaid Alyafeai, Khalid Almubarak, Ahmed Ashraf, Deema Alnuhait, Saied Alshahrani, Gubran A. Q. Abdulrahman, Gamil Ahmed, Qais Gawah, Zead Saleh, Mustafa Ghaleb, Yousef Ali, and Maged S. Al-Shaibani.  Cidar: Culturally relevant instruction dataset for arabic, 2024.
  * El-Nouby et al. [2021] Alaaeldin El-Nouby et al.  Are large-scale datasets necessary for self-supervised pre-training?  2021\.
  * Held et al. [2023] William Held et al.  A material lens on coloniality in nlp.  _arXiv preprint arXiv:2311.08391_ , 2023.
  * Jin et al. [2024] Hanlei Jin et al.  A comprehensive survey on process-oriented automatic text summarization with exploration of llm-based methods.  _arXiv preprint arXiv:2403.02901_ , 2024.
  * Joshi et al. [2019] Pratik Joshi, Christain Barnes, Sebastin Santy, Simran Khanuja, Sanket Shah, Anirudh Srinivasan, Satwik Bhattamishra, Sunayana Sitaram, Monojit Choudhury, and Kalika Bali.  Unsung challenges of building and deploying language technologies for low resource language communities.  pp. 211–219, 2019.  URL https://aclanthology.org/2019.icon-1.25.
  * Jung & van der Plas [2024] Vincent Jung and Lonneke van der Plas.  Understanding the effects of language-specific class imbalance in multilingual fine-tuning.  2024\.
  * Lancaster [2024] Thomas Lancaster.  A large language model supported synthesis of contemporary academic integrity research trends.  2024\.
  * Li et al. [2024] Zhenyu Li et al.  Flexkbqa: A flexible llm-powered framework for few-shot knowledge base question answering.  38(17), 2024.
  * Magueresse et al. [2020] Alexandre Magueresse, Vincent Carles, and Evan Heetderks.  Low-resource languages: A review of past work and future challenges.  _arXiv preprint arXiv:2006.07264_ , 2020.
  * Maxwell & Hughes [2006] Mike Maxwell and Baden Hughes.  Frontiers in linguistic annotation for lower-density languages.  2006\.
  * Muraoka et al. [2023] Masayasu Muraoka et al.  Cross-lingual transfer of large language model by visually-derived supervision toward low-resource languages.  2023\.
  * Obeid et al. [2020] Ossama Obeid, Nasser Zalmout, Salam Khalifa, Dima Taji, Mai Oudah, Bashar Alhafni, Go Inoue, Fadhl Eryani, Alexander Erdmann, and Nizar Habash.  CAMeL tools: An open source python toolkit for Arabic natural language processing.  In Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, and Stelios Piperidis (eds.), _Proceedings of the Twelfth Language Resources and Evaluation Conference_ , pp. 7022–7032, Marseille, France, May 2020. European Language Resources Association.  ISBN 979-10-95546-34-4.  URL https://aclanthology.org/2020.lrec-1.868.
  * Obeidat et al. [2024] Rasha Obeidat, Marwa Al-Harbi, Mahmoud Al-Ayyoub, and Luay Alawneh.  Arquad: An expert-annotated arabic machine reading comprehension dataset.  _Cognitive Computation_ , pp. 1–20, 2024.
  * Penedo et al. [2023] Guilherme Penedo, Quentin Malartic, Daniel Hesslow, Ruxandra Cojocaru, Alessandro Cappelli, Hamza Alobeidli, Baptiste Pannier, Ebtesam Almazrouei, and Julien Launay.  The refinedweb dataset for falcon llm: outperforming curated corpora with web data, and web data only.  _arXiv preprint arXiv:2306.01116_ , 2023.
  * Pushkarna et al. [2022] Mahima Pushkarna, Andrew Zaldivar, and Oddur Kjartansson.  Data cards: Purposeful and transparent dataset documentation for responsible ai.  2022\.  10.1145/3531146.3533231.  URL https://doi.org/10.1145/3531146.3533231.
  * Robinson et al. [2023] Nathaniel R. Robinson et al.  Chatgpt mt: Competitive for high-(but not low-) resource languages.  _arXiv preprint arXiv:2309.07423_ , 2023.
  * Sengupta & et al. [2023] Neha Sengupta and et al.  Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models.  2023\.
  * Sengupta et al. [2023] Neha Sengupta, Sunil Kumar Sahu, Bokang Jia, Satheesh Katipomu, Haonan Li, Fajri Koto, Osama Mohammed Afzal, Samta Kamboj, Onkar Pandit, Rahul Pal, et al.  Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models.  _arXiv preprint arXiv:2308.16149_ , 2023.
  * Singh & et al. [2024] Shivalika Singh and et al.  Aya dataset: An open-access collection for multilingual instruction tuning.  2024\.
  * Slim & Melouah [2024] Amel Slim and Ahlem Melouah.  Low resource arabic dialects transformer neural machine translation improvement through incremental transfer of shared linguistic features.  _Arabian Journal for Science and Engineering_ , pp. 1–17, 2024.
  * Valmeekam et al. [2022] Karthik Valmeekam et al.  Large language models still can’t plan (a benchmark for llms on planning and reasoning about change).  _arXiv preprint arXiv:2206.10498_ , 2022.
  * Wang et al. [2023] Xinyi Wang et al.  Large language models are implicitly topic models: Explaining and finding good demonstrations for in-context learning.  2023\.
  * Yamani et al. [2024] Asma Yamani, Raghad Alziyady, Reem AlYami, Salma Albelali, Leina Albelali, Jawharah Almulhim, Amjad Alsulami, Motaz Alfarraj, and Rabeah Al-Zaidy.  The kind dataset: A social collaboration approach for nuanced dialect data collection.  In _Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop_ , pp. 32–43, 2024.
  * Yao et al. [2024] Yifan Yao et al.  A survey on large language model (llm) security and privacy: The good, the bad, and the ugly.  pp. 100211, 2024.

##  Appendix A 101 Billion Arabic Words Data Card

Data Card for the 101 Billion Arabic Words Dataset, following the framework
introduced by [Pushkarna et al., 2022], we present the dataset card for the
101 Billion Arabic Words Dataset.

The 101 Billion Arabic Words Dataset is an Arabic language-centric dataset
curated by a clusterlab. The dataset contains a total of 101 billion words for
large language model training and fine-tuning. • Curated by: 5 contributors
from Clusterlab • Language(s): Modern Standard Arabic • License: Apache 2.0
https://www.apache.org/licenses/LICENSE-2.0 • Repository:
https://huggingface.co/datasets/ClusterlabAi/101_billion_arabic_words_dataset
Authorship

Publishing Organization:
Clusterlab Industry Type:
Startup - Tech Contact Details:
https://clusterlab.ai/

Data Points The dataset comprises 116 million data points, providing a
comprehensive and strong foundation for detailed analysis and insights.
Motivations & Intentions The dataset was collected and gathered with the
primary aim of training Arabic Language Models (LLMs) to achieve exceptional
performance. It exemplifies high quality, showcasing an expansive collection
of 101 billion words carefully tailored to accommodate the nuances of the
Arabic language. Provenance

Methods Used
We initially gathered data from specified sources, primarily Common Crawl, and
extracted Arabic content from WET files using Rust. Then, we applied our
preprocessing pipeline, which included text cleaning and deduplication (Figure
2). Methodology Details
Source: mainly Common Crawl Dates of Collection: September 2021 - July 2022

Dataset Version and Maintenance

Maintenance Status
Actively Maintained Version Details
Current version: 1.0 Maintenance Plan
Updates will be periodically made available.


CVPR 2021 Workshop on 3D Vision and Robotics

# Data-driven 6D Pose Tracking
by Calibrating Image Residuals in Synthetic Domains

Bowen Wen
Rutgers University
<EMAIL_ADDRESS>Chaitanya Mitash
Amazon
<EMAIL_ADDRESS>Kostas Bekris
Rutgers University
<EMAIL_ADDRESS>
###### Abstract

Tracking the 6D pose of objects in video sequences is important for robot
manipulation. This work presents $se(3)$-TrackNet, a data-driven optimization
approach for long-term, 6D pose tracking. It aims to identify the optimal
relative pose given the current RGB-D observation and a synthetic image
conditioned on the previous best estimate and the object’s model. The key
contribution in this context is a novel neural network architecture, which
appropriately disentangles the feature encoding to help reduce domain shift,
and an effective 3D orientation representation via Lie Algebra. Consequently,
even when the network is trained solely with synthetic data can work
effectively over real images. Comprehensive experiments over multiple
benchmarks show $se(3)$-TrackNet achieves consistently robust estimates and
outperforms alternatives, even though they have been trained with real images.
The approach runs in real time at 90.9Hz. Code, data and supplementary video
for this project are available at111This work has been previously accepted to
appear at IEEE IROS 2020 [27]. This is a CVPRW’21 paper on 3D Vision and
Robotics. https://github.com/wenbowen123/iros20-6d-pose-tracking

##  1 Introduction

Robotic tasks, such as object manipulation, often require to track the pose of
an object. Pose estimation from a single snapshot can initiate a manipulation
pipeline [30, 26]. Purposeful manipulation, however, such as placement [15,
17] frequently involves dynamic object pose change. Some pose estimation
approaches are relatively fast and can re-estimate pose from scratch for every
frame [25, 26, 22, 28]. This can be redundant, however, and often leads to
less coherent estimations over consecutive frames, which negatively impact
manipulation.

Temporal tracking of object poses over sequences of images can greatly improve
speed while maintain or even improve pose quality [27, 29, 20, 21, 14].
Nevertheless, many traditional methods that depend on hand-crafted likelihood
functions and features, require extensive hyper-parameter tuning when adapted
to novel object categories or environments. On the other hand, data-driven
techniques [12, 7] require real-world training data, which are difficult to
acquire and label in the context of 6D poses.

Figure 1: Top: Performance w.r.t. computation time evaluated on the YCB-Video
dataset according to the area under the curve (AUC) metric for the ADD and
ADD-S objectives [30]. The proposed approach achieves more accurate tracking
and is significantly faster than alternatives. Bottom: Pose predicted by the
$se(3)$-TrackNet without any re-initialization, which is able to recover from
complete occlusion.

This work proposes a data-driven optimization strategy to keep long-term track
of an object’s 6D pose robustly. The contributions are the following:

1\. A novel deep neural network for 6D object pose tracking, where a smart
feature-encoding disentanglement technique enables more efficient sim-to-real
transfer.

2\. A Lie Algebra representation of 3D orientations, which allows effective
learning of the residual pose transforms given a proper loss function.

3\. A training pipeline over synthetic data that employs domain randomization
[24] in the context of pose tracking.

4\. A novel benchmarking dataset for 6D pose tracking in the context of
multiple different robotics manipulation tasks, with groundtruth 6D pose
annotation.

Experiments indicate that the proposed network achieves state-of-art results
on the YCB-Video benchmark without re-initialization in contrast to prior work
[12, 4]. It is also significantly faster at 90.9Hz, as shown in Fig. 1.

##  2 Related Work

6D Object Pose Estimation: Learning-based techniques have shown promise in
directly regressing the 6D object pose from image data [16, 10, 22, 13, 8].
Nevertheless, given the complexity of the 6D challenge, a large amount of pose
annotated training data is required to achieve satisfactory results. This is
often more challenging than labeling for object classification or detection.
Given that a pose is re-estimated in every frame, estimation techniques often
trade-off speed for accuracy [30] while still insufficient for real-time
application. This is desirable for manipulation that often involves dynamic
scene updates. In contrast, $se(3)$-TrackNet exploits temporal information to
achieve higher accuracy and faster response than state-of-art single-image
pose estimation methods while using only synthetic data for training.

6D Object Pose Tracking: For setups where CAD object models are available,
prior approaches [3, 29, 10] harness GPUs for probabilistic filtering. They
often require, however, hand-crafted feature design and carefully tuned hyper-
parameters, which do not generalize easily. Recent work [4] proposed a data-
driven Rao-Blackwellized particle filter, achieving promising results on the
YCB Video benchmark. It suffers, however, from severe occlusions and requires
re-initialization from costly pose estimation. Another effort computes
relative transformations between consecutive frames by optimizing for the
discrepancy between consecutive observations [20, 19, 11, 31, 23]. The most
related effort to the current paper leverages the FlowNetSimple network [6]
for learning 6D pose tracking. It requires, however, occasional re-
initialization and has to be trained at least partially with real data.

##  3 Approach of $se(3)$-TrackNet

The objective is to compute the 6D pose of an object $T_{t}\in SE(3)$ at any
time $t>0$, given as input:

  * $\bullet$

a 3D CAD model of the object,

  * $\bullet$

its initial pose, $T_{0}\in SE(3)$, computed by any single-image based 6D pose
estimation technique, and a

  * $\bullet$

sequence of RGB-D images $O_{\tau},\tau\in\\{0,1,...,t-1\\}$ from previous
time stamps and the current observation $O_{t}$.

This work proposes a data-driven optimization technique to track the 6D object
pose. The cost function for the optimization is encoded and learned by a novel
neural network architecture, trained with only synthetically generated data.
In every time step, it computes a relative pose $\Delta T_{\tau}$ from the
previous frame as indicated in Fig. 2.

Figure 2: Overview: At any given time $t$ the current observation $O_{t}$ and
the rendering $R_{t-1}$ of the object model based on the previously computed
pose $T_{t-1}$ are passed to the $se(3)$-TrackNet via two separate feature
encoders $\phi_{B}$ and $\phi_{A}$ respectively. Both inputs are synthetic
during training while at test time, the input to $\phi_{B}$ is a real observed
image. The encoders’ outputs are concatenated and used to predict the relative
pose $\Delta T_{t}$ between the two images, with decoupled translation and
rotational. $\Delta T_{t}$ is then combined with $T_{t-1}$ to compute $T_{t}$.
The same process repeats along the video.

###  3.1 Tracking on $SE(3)$ Manifolds with Residuals

Optimization in this domain operates over cost functions defined for the
object poses $\bar{\xi},\xi$ that measure the discrepancy $\epsilon$ between
the features extracted from the images:

$\varepsilon=\rho(\phi_{I_{1}}(\bar{\xi})-\phi_{I_{2}}(\xi)),$

where $\rho$ is a predefined robust loss function, and $\phi(\cdot)$ can be
direct pixel intensity values, such as in [5], point-to-point discrepancy or
its variations [1], pre-designed features [9] or the combinations from any of
the above [19].

Given the current observation $O_{t}$, and the pose computed in the previous
timestamp $\xi_{t-1}$, the goal of this work is to find a relative
transformation $\Delta\xi$ that takes the object from $\xi_{t-1}$ to the pose
captured by the current observation. This can be formulated as an optimization
problem. Let $R$ denote the image corresponding to the object model’s
rendering at the given pose. Then, the optimal relative transform is:

$\Delta\xi^{*}=\operatorname*{argmin}_{\Delta\xi}\\{\rho(\phi_{O_{t}}(\xi_{t})-\phi_{R}(\xi_{t-1}\boxplus\Delta\xi))\\}\vspace{-0.1in}$

An analytical solution is to perform Gauss-Newton optimization, which often
requires appropriate choice of a robust cost function and hand-crafted
features. Another problem arises when different modalities are involved and
suitable tuning of relative weights struggles to generalize to new scenarios.
Instead, this work proposes a novel neural network architecture that
implicitly learns to calibrate the residual between the features extracted
from the current observation and the rendered image conditioned on previous
pose estimate to resolve the relative transform in the tangent space
$\Delta\xi\in se(3)$.

###  3.2 Neural Network Design

The proposed neural network is shown in Fig. 2. The network takes as input a
pair of images, $I^{t-1}$: rendered from the previous pose estimation, and
$I^{t}$: the current observation. The images are 4-channel RGB-D data. During
training, both inputs are synthetically generated images
$\phi(I_{syn,train}^{t-1};I_{syn,train}^{t})$, while for testing the current
timestamp input comes from a real sensor,
$\phi(I_{syn,test}^{t-1};I_{real,test}^{t})$. The $se(3)$-TrackNet uses two
separate input branches for $I^{t-1}$ and $I^{t}$. The weights of the feature
encoders are not shared so as to disentangle feature encoding. This is
different from related work [12], where the two images are concatenated into a
single input. A shared feature extractor worked in the previous work when both
real and synthetic data are available during training. The property of the
latent space $\phi(I_{syn,train}^{t-1};I_{real,train}^{t})$ could still be
partly preserved when tested on real world test scenarios
$\phi(I_{syn,test}^{t-1};I_{real,test}^{t})$. This representation, however,
does not generalize to training exclusively on synthetic data.

In particular, let’s denote the latent space features trained on purely
synthetic data as $\phi_{A}(I_{syn,train}^{t-1})$ and
$\phi_{B}(I_{syn,train}^{t})$. When tested on real world data, the latent
space features are $\phi_{A}(I_{syn,test}^{t-1})$ and
$\phi_{B}(I_{real,test}^{t})$. By this feature encoding disentanglement,
domain gap reduces to be between $\phi_{B}(I_{syn,train}^{t})$ and
$\phi_{B}(I_{real,test}^{t})$, while $\phi_{A}(I_{syn,train}^{t-1})$ and
$\phi_{A}(I_{syn,test}^{t-1})$ can be effortlessly aligned between the
training and test phase without the need for tackling the domain gap problem.

A relative transform can be predicted by the network via end-to-end training.
The transformation is represented by Lie algebra as $\Delta\xi=(t,w)^{T}\in
se(3)$, where the prediction of $w$ and $t$ are disentangled into separate
branches and trained by $L_{2}$ loss:

$L=\lambda_{1}||w-\bar{w}||_{2}+\lambda_{2}||t-\bar{t}||_{2}\vspace{-0.1in}$

where $\lambda_{1}$ and $\lambda_{2}$ has been simply set to 1 in experiments.
Given $\Delta\xi$, the current pose estimate is computed as
$T^{t}=exp(\Delta\xi)\cdot T^{t-1}$, as described in Sec. 3.1. The details of
the network architecture will be described in the released code.

###  3.3 Synthetic Data Generation via PPDR

Domain randomization provides variability at training time, such that at test
time, the model is able to generalize to real-world data [24]. Prior work
implements the idea of domain randomization by directly sampling object poses
from a predetermined distribution [24, 30, 25], which can lead to unrealistic
penetration or pile configurations, adversely introducing undesired bias to
depth data distribution during learning. This work therefore leverages the
complementary attributes of domain randomization and physically-consistent
simulation for the synthetic data generation process, namely PPDR (Physically
Plausible Domain Randomization). Specifically, object poses are initialized
randomly where collision between objects or distractors could occur, which is
then followed by a number of physics simulation steps so that objects are
separated or fall onto the table without collision. Meanwhile, other complex
or intractable physical properties such as lighting, number of objects,
distractor textures are randomized. For each scene, a pair of data
$I^{t-1}_{syn,train}$ and $I^{t}_{syn,train}$ are generated. It is later
utilized as the input to the network for training. Both images are cropped and
resized into a fixed resolution $176\times 176$ before feeding into the
network - similar to prior work [12].

The next step is to bridge the domain gap of depth data via bidirectional
alignment, operated over the synthetic depth data during training time and
real depth data during test time. Specifically, during training time, random
Gaussian noise and random pixel corruption augmentation steps are applied to
the synthetic depth data $D^{t}_{syn,train}$ at branch B to resemble a real
noisy depth image. In contrast, during test time, bilateral filtering is
carried out on the real depth image so as to smooth sensor noise and fill
holes to be aligned with the synthetic domain.

##  4 Experiments

This section evaluates the proposed approach and compares against state-of-
the-art 6D pose tracking methods as well as single-image pose estimation
methods on a public benchmark. It also introduces a new benchmark developed as
part of this work, which corresponds to robot manipulation scenarios.
Experiments are conducted on a standard desktop with Intel Xeon(R) E5-1660
<EMAIL_ADDRESS>processor with a NVIDIA Tesla K40c GPU. Both quantitative and
qualitative results demonstrate $se(3)$-TrackNet achieves the state-of-art
results in terms of both accuracy and speed, while using only synthetic
training data.

| DOPE [25] | DenseFusion [26] | PoseCNN+DeepIM [12] | DeepIM tracking [12] | RGF [10] | Wüthrich’s [29] |  $se(3)$-TrackNet | PoseRBPF [4] |  $se(3)$-TrackNet |  $se(3)$-TrackNet
---|---|---|---|---|---|---|---|---|---|---
Modality | RGB | RGBD | RGBD | RGB | Depth | Depth | RGBD | RGBD | RGBD | RGBD
Type | detection | detection | detection | tracking | tracking | tracking | tracking | tracking | tracking | tracking
Initial pose from | - | - | - | groundtruth | groundtruth | groundtruth | groundtruth | PoseCNN | PoseCNN | PoseCNN
Re-initialization (Total) | No | No | No | Yes (290) | No | No | No | Yes (2) | No | Yes (2)
Train data | Syn | Real+Syn | Real+Syn | Real+Syn | - | - | Syn | Syn | Syn | Syn
Objects | ADD | ADD-S | ADD | ADD-S | ADD | ADD-S | ADD | ADD-S | ADD | ADD-S | ADD | ADD-S | ADD | ADD-S | ADD | ADD-S | ADD | ADD-S | ADD | ADD-S
002_master_chef_can |  | - | - | 96.40 | 78.00 | 96.30 | 89.00 | 93.80 | 46.23 | 90.17 | 55.60 | 90.68 | 93.86 | 96.29 | 90.50 | 95.10 | 93.84 | 95.92 | 93.84 | 95.92
003_cracker_box | 55.90 | 69.80 | - | 95.50 | 91.40 | 95.30 | 88.50 | 93.00 | 56.95 | 72.26 | 96.38 | 97.19 | 96.52 | 97.20 | 88.20 | 93.00 | 96.42 | 97.12 | 96.42 | 97.12
004_sugar_box | 75.70 | 87.10 | - | 97.5 | 97.60 | 98.20 | 94.30 | 96.30 | 50.38 | 72.65 | 97.14 | 97.94 | 97.58 | 98.14 | 92.90 | 95.50 | 97.56 | 98.13 | 97.56 | 98.13
005_tomato_soup_can | 76.10 | 85.10 | - | 94.60 | 90.30 | 94.80 | 89.10 | 93.20 | 72.44 | 91.60 | 64.74 | 89.55 | 94.96 | 97.17 | 90.00 | 93.80 | 94.81 | 97.10 | 94.81 | 97.10
006_mustard_bottle | 81.90 | 90.90 | - | 97.20 | 97.10 | 98.00 | 92.00 | 95.10 | 87.71 | 98.19 | 97.12 | 97.95 | 95.76 | 97.37 | 91.90 | 96.30 | 95.73 | 97.36 | 95.73 | 97.36
007_tuna_fish_can | - | - | - | 96.60 | 92.20 | 98.00 | 92.00 | 96.40 | 28.67 | 52.93 | 69.14 | 93.32 | 86.46 | 91.09 | 91.10 | 95.30 | 86.46 | 91.08 | 86.46 | 91.08
008_pudding_box | - | - | - | 96.50 | 83.50 | 90.60 | 80.10 | 88.30 | 12.69 | 17.98 | 96.85 | 97.89 | 97.93 | 98.39 | 85.80 | 92.00 | 97.90 | 98.37 | 97.90 | 98.37
009_gelatin_box | - | - | - | 98.10 | 98.00 | 98.50 | 92.00 | 94.40 | 49.10 | 70.72 | 97.46 | 98.37 | 97.81 | 98.42 | 96.30 | 97.50 | 97.74 | 98.46 | 97.74 | 98.46
010_potted_meat_can | 39.40 | 52.40 | - | 91.30 | 82.20 | 90.30 | 78.00 | 88.90 | 44.09 | 45.57 | 83.71 | 86.69 | 77.81 | 84.16 | 68.70 | 77.90 | 36.45 | 60.28 | 74.51 | 82.38
011_banana | - | - | - | 96.60 | 94.90 | 97.60 | 81.00 | 90.50 | 93.33 | 97.74 | 86.27 | 96.07 | 94.90 | 97.18 | 74.20 | 86.90 | 40.04 | 78.81 | 84.62 | 95.15
019_pitcher_base | - | - | - | 97.10 | 97.40 | 97.90 | 90.40 | 94.70 | 97.93 | 98.18 | 97.30 | 97.74 | 96.75 | 97.45 | 86.80 | 94.20 | 96.71 | 97.43 | 96.71 | 97.43
021_bleach_cleanser | - | - | - | 95.80 | 91.60 | 96.90 | 81.70 | 90.50 | 95.87 | 97.28 | 95.23 | 97.16 | 95.94 | 97.25 | 86.00 | 93.00 | 95.89 | 97.23 | 95.89 | 97.23
024_bowl | - | - | - | 88.20 | 8.10 | 87.00 | 38.80 | 90.60 | 24.25 | 82.40 | 30.37 | 97.15 | 80.91 | 94.46 | 25.50 | 94.20 | 39.12 | 95.56 | 39.12 | 95.56
025_mug | - | - | - | 97.10 | 94.20 | 97.60 | 83.20 | 92.00 | 59.99 | 71.18 | 83.15 | 93.35 | 91.53 | 96.88 | 90.90 | 97.10 | 91.56 | 96.88 | 91.56 | 96.88
035_power_drill | - | - | - | 96.00 | 97.20 | 97.90 | 85.40 | 92.30 | 97.94 | 98.35 | 97.09 | 97.82 | 96.42 | 97.40 | 93.90 | 96.10 | 96.38 | 97.38 | 96.38 | 97.38
036_wood_block | - | - | - | 89.70 | 81.10 | 91.50 | 44.30 | 75.40 | 45.68 | 62.51 | 95.48 | 96.87 | 95.16 | 96.70 | 20.10 | 89.10 | 33.91 | 95.92 | 33.91 | 95.92
037_scissors | - | - | - | 95.20 | 92.70 | 96.00 | 70.30 | 84.50 | 20.94 | 38.60 | 4.17 | 16.20 | 95.68 | 97.55 | 76.10 | 85.60 | 95.67 | 97.54 | 95.67 | 97.54
040_large_marker | - | - | - | 97.50 | 88.90 | 98.20 | 80.40 | 91.20 | 12.17 | 18.90 | 35.58 | 53.02 | 92.15 | 95.99 | 92.00 | 97.10 | 89.01 | 94.23 | 89.01 | 94.23
051_large_clamp | - | - | - | 72.90 | 54.20 | 77.90 | 73.90 | 84.10 | 62.84 | 80.12 | 61.25 | 72.35 | 94.71 | 96.93 | 48.50 | 94.80 | 71.60 | 96.88 | 71.60 | 96.88
052_extra_large_clamp | - | - | - | 69.80 | 36.50 | 77.80 | 49.30 | 90.30 | 67.48 | 69.65 | 93.73 | 96.58 | 91.74 | 95.76 | 40.30 | 90.10 | 64.58 | 95.80 | 64.58 | 95.80
061_foam_brick | - | - | - | 92.50 | 48.20 | 97.60 | 91.60 | 95.50 | 69.99 | 86.55 | 96.76 | 98.11 | 93.65 | 96.71 | 81.10 | 95.70 | 40.66 | 94.67 | 40.66 | 94.67
ALL | - | - | - | 93.10 | 80.70 | 94.00 | 79.30 | 91.00 | 59.18 | 74.29 | 78.01 | 90.21 | 93.05 | 95.71 | 80.80 | 93.30 | 84.46 | 93.87 | 87.81 | 95.52
Speed (fps) | 4.31 | 16.67 | 0.09 | 12.00 | 11.76 | 12.93 | 90.90 | 5.00 | 90.90 | 90.90

Figure 3:  Left: Comparing the performance of $se(3)$-TrackNet (Gray) with
state-of-the-art techniques on the YCB Video. The approach significantly
outperforms the competing approaches over the ADD metric, which considers
semantic information during pose evaluation. It also achieves the highest
success rate over the ADD-S metric both in cases of initialization with the
ground-truth pose and when initialized with the output of PoseCNN [30]
(rightmost two columns). Top-right: Qualitative results for tracking the
”large-clamp” object in the YCB-Video dataset. Bottom-right: Tracking results
for ”bleach-cleanser” being manipulated by a vacuum gripper in the YCBInEOAT
dataset.

###  4.1 Datasets

YCB-Video Dataset This dataset [30] captures 92 RGB-D video sequences over 21
YCB Objects [2] arranged on table-tops. Objects’ groundtruth 6D poses are
annotated in every frame. The evaluation closely follows the protocols adopted
in comparison methods [30, 4, 12, 26] and reports the AUC (Area Under Curve)
results on the keyframes in 12 video test sequences evaluated by the metrics
of $ADD=\frac{1}{m}\sum_{x\in M}||Rx+T-(\hat{R}x+\hat{T})||$ which performs
exact model matching, and $ADD-S=\frac{1}{m}\sum_{x_{1}\in M}\min_{x_{2}\in
M}||Rx_{1}+T-(\hat{R}x_{2}+\hat{T})||$ [30].

YCBInEOAT Dataset In order to evaluate the 6D tracking performance in the
setup of dynamic moving object, in this work, a novel dataset, referred to as
”YCBInEOAT Dataset”, is developed in the context of robotic manipulation,
where various robot end-effectors are included: a vacuum gripper, a Robotiq
2F-85 gripper, and a Yale T42 Hand [18]. The manipulation sequences consider 5
YCB objects outlined in Table 1. Each video sequence is collected from a real
manipulation performed with a dual-arm Yaskawa Motoman SDA10f. In general,
there are 3 types of manipulation tasks performed: (1) single arm pick-and-
place, (2) within-hand manipulation, and (3) pick to hand-off between arms to
placement. RGB-D images are captured by an Azure Kinect sensor mounted
statically on the robot with a frequency of 20 to 30 Hz. Similar to the YCB-
Video, ADD and ADD-S metrics are adopted for evaluation. Ground-truth 6D
object poses in camera’s frame have been accurately annotated _manually_ for
each frame of the video.

###  4.2 Results on YCB-Video

Fig. 3 present the evaluation over the YCB-Video dataset. Although this
dataset contains real world annotated training data, $se(3)$-TrackNet does not
use any of them but is trained solely on synthetic data generated by
aforementioned pipeline. It is compared with other state-of-art 6D object pose
detection approaches [30, 25, 12, 26] and 6D pose tracking approaches [4, 12,
10, 29], where publicly available source code222https://github.com/bayesian-
object-tracking/dbot is used to evaluate [10, 29], while other results are
adopted from the respective publications. All the compared tracking methods
except PoseRBPF are using ground-truth pose for initialization. PoseRBPF [4]
is the only one that is initialized using predicted poses from PoseCNN [30].
For fairness, two additional experiments using the same initial pose as
PoseRBPF333We thank the authors for providing the initial poses they used in
their original paper [4]. are performed and presented in the rightmost two
columns of Table I, one is without any re-initialization, and the other allows
re-initialization by PoseCNN twice (same as in PoseRBPF) after heavy
occlusions. The prior work [12] was originally proposed to refine the pose
output from any 6D pose estimation detection method, but also extends to RGB-
based tracking. It has to be re-initialized by PoseCNN when the last 10 frames
have an average rotation greater than 10 degrees or an average translation
greater than 1 cm, which happens every 340 frames on average as reported [12].

###  4.3 Results on YCBInEOAT-Dataset

Objects | RGF [10] | Wüthrich’s [29] |  $se(3)$-TrackNet
---|---|---|---
| ADD | ADD-S | ADD | ADD-S | ADD | ADD-S
003_cracker_box | 34.78 | 55.44 | 79.00 | 88.13 | 90.76 | 94.06
021_bleach_cleanser | 29.40 | 45.03 | 61.47 | 68.96 | 89.58 | 94.44
004_sugar_box | 15.82 | 16.87 | 86.78 | 92.75 | 92.43 | 94.80
005_tomato_soup_can | 15.13 | 26.44 | 63.71 | 93.17 | 93.40 | 96.95
006_mustard_bottle | 56.49 | 60.17 | 91.31 | 95.31 | 97.00 | 97.92
ALL | 29.98 | 39.90 | 78.28 | 89.18 | 92.66 | 95.53

Table 1: Results evaluated on YCBInEOAT-dataset by AUC (Area Under Curve) for
ADD and ADD-S.

Table 1 shows the quantitative results evaluated by the area under the curve
for ADD and ADD-S on the developed YCBInEOAT dataset. On this benchmark, the
tracking approaches with publicly available source code could be directly
evaluated [29, 10]. Pose is initialized with ground-truth in the first frame
and no re-initialization is allowed. Example qualitative results are
demonstrated by Fig. 3. Abrupt motions, extreme rotations and slippage within
the end-effector expose challenges for 6D object pose tracking. Nevertheless,
$se(3)$-TrackNet is sufficiently robust to perform long-term reliable pose
tracking.

###  4.4 Ablation Study

Criteria | ADD | ADD-S
---|---|---
Proposed | 94.71 | 96.93
No physics | 91.88 | 95.76
No depth | 75.65 | 87.22
Shared encoder | 0.28 | 0.28
Quaternion | 93.58 | 96.39
Shape-Match Loss | 1.93 | 5.48

An ablation study investigates the importance of different modules of the
proposed approach. It is performed for the large clamp object from the YCB-
Video dataset and is presented in the accompanying table. The initial pose is
given by ground-truth and no re-initialization is allowed. No physics implies
that physics simulation is removed during synthetic data generation. For No
depth, the depth modality is removed in both training and inference stage.
Shared encoder means $I^{t}$ and $I^{t-1}$ are passed to the same feature
encoder. Quaternion replaces the proposed rotation representation by
quaternion. Shape-Match Loss [30] does not require the specification of
symmetries. However, it loses track of the object early in the video.

## References

  * [1] Paul J Besl and Neil D McKay.  Method for registration of 3-d shapes.  In Sensor fusion IV: control paradigms and data structures. International Society for Optics and Photonics, 1992.
  * [2] Berk Calli, Arjun Singh, Aaron Walsman, Siddhartha Srinivasa, Pieter Abbeel, and Aaron M Dollar.  The ycb object and model set: Towards common benchmarks for manipulation research.  In IEEE ICAR 2015.
  * [3] Changhyun Choi and Henrik I Christensen.  Rgb-d object tracking: A particle filter approach on gpu.  In IROS, 2013.
  * [4] Xinke Deng, Arsalan Mousavian, Yu Xiang, Fei Xia, Timothy Bretl, and Dieter Fox.  Poserbpf: A rao–blackwellized particle filter for 6-d object pose tracking.  IEEE Transactions on Robotics, 2021.
  * [5] Jakob Engel, Vladlen Koltun, and Daniel Cremers.  Direct sparse odometry.  IEEE T-PAMI 2017.
  * [6] Philipp Fischer, Alexey Dosovitskiy, Eddy Ilg, Philip Häusser, Caner Hazırbaş, Vladimir Golkov, Patrick Van der Smagt, Daniel Cremers, and Thomas Brox.  Flownet: Learning optical flow with convolutional networks.  ICCV, 2015.
  * [7] Mathieu Garon and Jean-François Lalonde.  Deep 6-dof tracking.  IEEE transactions on visualization and computer graphics, 23(11):2410–2418, 2017.
  * [8] Yisheng He, Wei Sun, Haibin Huang, Jianran Liu, Haoqiang Fan, and Jian Sun.  Pvn3d: A deep point-wise 3d keypoints voting network for 6dof pose estimation.  In CVPR, pages 11632–11641, 2020.
  * [9] Paul Hebert, Nicolas Hudson, Jeremy Ma, Thomas Howard, Thomas Fuchs, Max Bajracharya, and Joel Burdick.  Combined shape, appearance and silhouette for simultaneous manipulator and object tracking.  In ICRA 2012.
  * [10] Jan Issac, Manuel Wüthrich, Cristina Garcia Cifuentes, Jeannette Bohg, Sebastian Trimpe, and Stefan Schaal.  Depth-based object tracking using a robust gaussian filter.  In ICRA, 2016.
  * [11] David Joseph Tan, Federico Tombari, Slobodan Ilic, and Nassir Navab.  A versatile learning-based 3d temporal tracker: Scalable, robust, online.  In ICCV 2015.
  * [12] Yi Li, Gu Wang, Xiangyang Ji, Yu Xiang, and Dieter Fox.  Deepim: Deep iterative matching for 6d pose estimation.  In ECCV 2018.
  * [13] Zhigang Li, Gu Wang, and Xiangyang Ji.  Cdpn: Coordinates-based disentangled pose network for real-time rgb-based 6-dof object pose estimation.  In ICCV 2019.
  * [14] Leon Magnus, Svenja Menzenbach, Max Siebenborn, Niklas Funk, Boris Belousov, and Georgia Chalvatzaki.  Object tracking for robotic assembly dataset creation and tracking evaluation.
  * [15] Chaitanya Mitash, Rahul Shome, Bowen Wen, Abdeslam Boularias, and Kostas Bekris.  Task-driven perception and manipulation for constrained placement of unknown objects.  IEEE Robotics and Automation Letters, 2020.
  * [16] Chaitanya Mitash, Bowen Wen, Kostas Bekris, and Abdeslam Boularias.  Scene-level pose estimation for multiple instances of densely packed objects.  CoRL, 2019.
  * [17] Andrew S Morgan, Bowen Wen, Junchi Liang, Abdeslam Boularias, Aaron M Dollar, and Kostas Bekris.  Vision-driven compliant manipulation for reliable, high-precision assembly tasks.  RSS, 2021.
  * [18] Lael Odhner, Raymond R Ma, and Aaron Dollar.  Open-loop precision grasping with underactuated hands inspired by a human manipulation strategy.  IEEE Transactions on Automation Science and Engineering 2013.
  * [19] Karl Pauwels and Danica Kragic.  Simtrack: A simulation-based framework for scalable real-time object pose detection and tracking.  In IROS 2015.
  * [20] Tanner Schmidt, Richard A Newcombe, and Dieter Fox.  Dart: Dense articulated real-time tracking.  In RSS 2014.
  * [21] Manuel Stoiber, Martin Pfanne, Klaus H Strobl, Rudolph Triebel, and Alin Albu-Schaeffer.  A sparse gaussian approach to region-based 6dof object tracking.  In Proceedings of the Asian Conference on Computer Vision, 2020\.
  * [22] Martin Sundermeyer, Zoltan-Csaba Marton, Maximilian Durner, Manuel Brucker, and Rudolph Triebel.  Implicit 3d orientation learning for 6d object detection from rgb images.  In ECCV 2018.
  * [23] Henning Tjaden, Ulrich Schwanecke, Elmar Schömer, and Daniel Cremers.  A region-based gauss-newton approach to real-time monocular multiple object tracking.  TPAMI 2018.
  * [24] Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, and Pieter Abbeel.  Domain randomization for transferring deep neural networks from simulation to the real world.  In IROS 2017.
  * [25] Jonathan Tremblay, Thang To, Balakumar Sundaralingam, Yu Xiang, Dieter Fox, and Stan Birchfield.  Deep object pose estimation for semantic robotic grasping of household objects.  CoRL, 2018.
  * [26] Chen Wang, Danfei Xu, Yuke Zhu, Roberto Martín-Martín, Cewu Lu, Li Fei-Fei, and Silvio Savarese.  Densefusion: 6d object pose estimation by iterative dense fusion.  CVPR, 2019.
  * [27] B. Wen, C. Mitash, B. Ren, and K. E. Bekris.  se(3)-tracknet: Data-driven 6d pose tracking by calibrating image residuals in synthetic domains.  In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, 10/2020 2020.
  * [28] Bowen Wen, Chaitanya Mitash, Sruthi Soorian, Andrew Kimmel, Avishai Sintov, and Kostas E Bekris.  Robust, occlusion-aware pose estimation for objects grasped by adaptive hands.  ICRA, 2020.
  * [29] Manuel Wüthrich, Peter Pastor, Mrinal Kalakrishnan, Jeannette Bohg, and Stefan Schaal.  Probabilistic object tracking using a range camera.  IROS 2013.
  * [30] Yu Xiang, Tanner Schmidt, Venkatraman Narayanan, and Dieter Fox.  Posecnn: A convolutional neural network for 6d object pose estimation in cluttered scenes.  RSS, 2018.
  * [31] Leisheng Zhong and Li Zhang.  A robust monocular 3d object tracking method combining statistical and photometric constraints.  IJCV 2019.


085901.5+010649 | 2757 | $134.7564$ | $+1.1137$ | 33.6 | 97.4 | 0.162 | - | $0.56_{-0.08}^{+0.08}$ | $0.90_{-0.14}^{+0.13}$ | $7.58_{-1.02}^{+1.08}$ | $0.97_{-0.22}^{+0.40}$ | $0.61_{-0.09}^{+0.09}$ | $0.98_{-0.16}^{+0.14}$ | $8.22_{-1.22}^{+1.20}$ | 8.4 | 1.53 |
085913.1+031334 | 485 | $134.8048$ | $+3.2263$ | 15.9 | 16.8 | 0.189 | $1.27_{-0.19}^{+0.35}$ | $1.14_{-0.17}^{+0.15}$ | $1.84_{-0.29}^{+0.26}$ | $10.87_{-1.68}^{+1.43}$ | $1.11_{-0.10}^{+0.09}$ | $1.26_{-0.27}^{+0.22}$ | $2.04_{-0.43}^{+0.36}$ | $12.07_{-2.57}^{+2.09}$ | 12.3 | 1.34 |
085931.9+030839 | 360 | $134.8830$ | $+3.1443$ | 175.1 | 617.4 | 0.196 | $2.62_{-0.36}^{+0.56}$ | $3.27_{-0.17}^{+0.16}$ | $7.27_{-0.49}^{+0.52}$ | $30.69_{-1.69}^{+1.50}$ | $2.37_{-0.31}^{+0.43}$ | $4.12_{-0.21}^{+0.20}$ | $9.16_{-0.62}^{+0.64}$ | $38.61_{-2.01}^{+1.85}$ | 20.2 | 2.65 |
085948.9+041120 | 1350 | $134.9539$ | $+4.1889$ | 22.6 | 43.0 | 0.208 | $1.63_{-0.28}^{+0.49}$ | $1.25_{-0.12}^{+0.13}$ | $2.25_{-0.23}^{+0.25}$ | $9.93_{-0.99}^{+0.97}$ | $1.37_{-0.18}^{+0.26}$ | $1.47_{-0.15}^{+0.15}$ | $2.63_{-0.29}^{+0.28}$ | $11.60_{-1.24}^{+1.17}$ | 10.7 | 1.66 |
090104.4+011643 | 3171 | $135.2686$ | $+1.2788$ | 38.0 | 62.8 | 0.251 | - | $1.65_{-0.18}^{+0.20}$ | $3.21_{-0.46}^{+0.68}$ | $8.71_{-0.99}^{+1.04}$ | $1.61_{-0.36}^{+0.90}$ | $2.03_{-0.20}^{+0.22}$ | $3.94_{-0.53}^{+0.84}$ | $10.76_{-1.09}^{+1.12}$ | 9.7 | 1.68 |
090131.1+030056 | 152 | $135.3800$ | $+3.0157$ | 373.9 | 1266.7 | 0.193 | $3.49_{-0.60}^{+0.98}$ | $4.91_{-0.24}^{+0.34}$ | $12.52_{-0.80}^{+1.11}$ | $48.53_{-2.33}^{+3.36}$ | $3.18_{-0.43}^{+0.77}$ | $6.33_{-0.39}^{+0.54}$ | $16.18_{-1.21}^{+1.70}$ | $62.60_{-3.88}^{+5.46}$ | 26.1 | 3.15 |
090140.9-012132 | 2412 | $135.4207$ | $-1.3591$ | 20.4 | 43.8 | 0.295 | - | $0.92_{-0.14}^{+0.15}$ | $1.56_{-0.23}^{+0.31}$ | $3.24_{-0.48}^{+0.51}$ | $1.21_{-0.35}^{+0.55}$ | $1.57_{-0.23}^{+0.25}$ | $2.66_{-0.41}^{+0.45}$ | $5.54_{-0.79}^{+0.79}$ | 9.0 | 2.26 |
090153.9-012209 | 2048 | $135.4748$ | $-1.3694$ | 87.9 | 163.9 | 0.295 | $2.38_{-0.56}^{+1.10}$ | $1.58_{-0.19}^{+0.21}$ | $3.46_{-0.48}^{+0.57}$ | $5.87_{-0.69}^{+0.77}$ | $2.06_{-0.43}^{+0.96}$ | $2.01_{-0.27}^{+0.24}$ | $4.41_{-0.64}^{+0.73}$ | $7.50_{-0.98}^{+0.90}$ | 10.5 | 1.54 |
090255.2+030220 | 5489 | $135.7300$ | $+3.0389$ | 48.9 | 66.8 | 0.200 | - | $0.90_{-0.11}^{+0.11}$ | $1.74_{-0.22}^{+0.25}$ | $7.87_{-0.94}^{+0.98}$ | $1.60_{-0.20}^{+0.37}$ | $1.29_{-0.14}^{+0.13}$ | $2.47_{-0.28}^{+0.35}$ | $11.21_{-1.23}^{+1.22}$ | 9.1 | 2.41 |
090430.7+042648 | 1075 | $136.1282$ | $+4.4468$ | 37.7 | 209.8 | 0.457 | $2.79_{-0.58}^{+1.45}$ | $5.89_{-0.82}^{+0.83}$ | $12.56_{-1.72}^{+2.01}$ | $7.86_{-1.08}^{+1.07}$ | $2.23_{-0.40}^{+0.62}$ | $6.57_{-0.87}^{+0.92}$ | $14.20_{-2.07}^{+2.12}$ | $8.77_{-1.18}^{+1.25}$ | 10.3 | 1.04 |
090540.0+043440 | 354 | $136.4171$ | $+4.5780$ | 231.4 | 678.2 | 0.24 | $3.87_{-0.89}^{+1.62}$ | $6.76_{-0.44}^{+0.45}$ | $18.20_{-1.91}^{+2.94}$ | $43.49_{-2.89}^{+2.81}$ | $3.87_{-0.92}^{+1.86}$ | $8.85_{-0.55}^{+0.51}$ | $23.74_{-2.31}^{+4.06}$ | $56.87_{-3.57}^{+3.11}$ | 23.8 | 2.65 |
090601.0+000055 | 3259 | $136.5043$ | $+0.0154$ | 18.6 | 92.2 | 0.200 | - | $0.85_{-0.11}^{+0.10}$ | $1.92_{-0.29}^{+0.42}$ | $7.68_{-0.98}^{+0.90}$ | $2.15_{-0.48}^{+1.22}$ | $1.20_{-0.18}^{+0.15}$ | $2.70_{-0.47}^{+0.63}$ | $10.81_{-1.65}^{+1.37}$ | 9.1 | 2.55 |
090628.9-012938 | 2424 | $136.6207$ | $-1.4941$ | 46.1 | 119.0 | 0.431 | $2.46_{-0.54}^{+1.49}$ | $2.80_{-0.33}^{+0.37}$ | $6.94_{-1.02}^{+2.00}$ | $4.51_{-0.50}^{+0.56}$ | - | $3.90_{-0.40}^{+0.47}$ | $9.71_{-1.38}^{+2.70}$ | $6.28_{-0.61}^{+0.75}$ | 10.6 | 1.56 |
090656.3+044717 | 12153 | $136.7347$ | $+4.7882$ | 29.0 | 32.2 | 0.12 | $1.58_{-0.21}^{+0.41}$ | $0.52_{-0.05}^{+0.06}$ | $0.95_{-0.10}^{+0.10}$ | $13.76_{-1.41}^{+1.51}$ | $1.40_{-0.16}^{+0.23}$ | $0.86_{-0.08}^{+0.08}$ | $1.57_{-0.15}^{+0.15}$ | $22.74_{-2.07}^{+2.04}$ | 10.6 | 4.28 |
090723.8-011210 | 2680 | $136.8494$ | $-1.2029$ | 32.5 | 107.9 | 0.251 | $2.15_{-0.47}^{+1.25}$ | $1.14_{-0.12}^{+0.14}$ | $2.57_{-0.32}^{+0.42}$ | $6.19_{-0.63}^{+0.74}$ | $2.31_{-0.56}^{+1.01}$ | $1.55_{-0.16}^{+0.17}$ | $3.50_{-0.44}^{+0.54}$ | $8.43_{-0.85}^{+0.93}$ | 9.6 | 1.96 |
090838.0+015226 | 1479 | $137.1585$ | $+1.8741$ | 42.9 | 183.6 | 0.264 | - | $2.06_{-0.19}^{+0.17}$ | $4.64_{-0.54}^{+0.55}$ | $9.97_{-0.97}^{+0.82}$ | $2.49_{-0.58}^{+1.06}$ | $2.79_{-0.25}^{+0.22}$ | $6.30_{-0.73}^{+0.73}$ | $13.46_{-1.17}^{+1.09}$ | 11.6 | 2.24 |
090913.8-001214 | 885 | $137.3079$ | $-0.2040$ | 67.6 | 243.9 | 0.310 | $3.12_{-0.78}^{+1.37}$ | $3.98_{-0.32}^{+0.33}$ | $9.59_{-1.09}^{+1.47}$ | $13.63_{-1.14}^{+1.05}$ | $2.84_{-0.71}^{+1.44}$ | $5.50_{-0.43}^{+0.44}$ | $13.25_{-1.46}^{+1.95}$ | $18.78_{-1.43}^{+1.46}$ | 14.5 | 2.24 |
091033.8+005100 | 510 | $137.6409$ | $+0.8501$ | 45.6 | 416.8 | 0.366 | $2.31_{-0.38}^{+0.64}$ | $3.74_{-0.50}^{+0.54}$ | $8.55_{-1.22}^{+1.26}$ | $8.53_{-1.12}^{+1.24}$ | $2.59_{-0.46}^{+0.52}$ | $4.36_{-0.63}^{+0.66}$ | $9.95_{-1.55}^{+1.48}$ | $9.95_{-1.44}^{+1.51}$ | 11.4 | 1.32 |
091117.1+030441 | 15328 | $137.8216$ | $+3.0782$ | 15.2 | 19.2 | 0.213 | - | $0.57_{-0.08}^{+0.10}$ | $1.34_{-0.21}^{+0.29}$ | $4.48_{-0.63}^{+0.79}$ | $2.53_{-0.60}^{+1.01}$ | $1.06_{-0.13}^{+0.14}$ | $2.47_{-0.34}^{+0.45}$ | $8.30_{-1.00}^{+1.08}$ | 8.1 | 3.55 |
091215.3-021743 | 626 | $138.0641$ | $-2.2956$ | 120.3 | 435.9 | 0.16 | $3.11_{-0.76}^{+1.28}$ | $4.70_{-0.39}^{+0.38}$ | $11.15_{-1.21}^{+1.32}$ | $69.85_{-6.03}^{+5.46}$ | $2.70_{-0.56}^{+0.76}$ | $6.29_{-0.51}^{+0.41}$ | $14.84_{-1.48}^{+1.64}$ | $93.23_{-7.45}^{+6.10}$ | 20.5 | 3.51 |
091315.0+034850 | 593 | $138.3125$ | $+3.8139$ | 88.3 | 393.5 | 0.453 | - | $8.26_{-0.78}^{+0.71}$ | $24.23_{-3.00}^{+6.03}$ | $12.40_{-1.10}^{+1.01}$ | $4.39_{-1.26}^{+2.72}$ | $11.23_{-1.05}^{+0.95}$ | $33.02_{-4.14}^{+8.14}$ | $16.85_{-1.44}^{+1.43}$ | 14.6 | 2.12 |
091322.9+040617 | 9801 | $138.3454$ | $+4.1050$ | 9.6 | 26.9 | 0.088 | $0.83_{-0.14}^{+0.15}$ | $0.17_{-0.03}^{+0.03}$ | $0.29_{-0.07}^{+0.07}$ | $8.42_{-1.74}^{+1.72}$ | - | $0.24_{-0.05}^{+0.06}$ | $0.41_{-0.11}^{+0.12}$ | $12.16_{-2.74}^{+2.73}$ | 7.3 | 3.88 |
091336.6+031723 | 5541 | $138.4026$ | $+3.2899$ | 54.9 | 82.7 | 0.142 | $1.84_{-0.40}^{+0.76}$ | $0.50_{-0.07}^{+0.07}$ | $0.83_{-0.12}^{+0.13}$ | $9.12_{-1.24}^{+1.40}$ | $1.13_{-0.17}^{+0.30}$ | $0.68_{-0.09}^{+0.09}$ | $1.12_{-0.15}^{+0.18}$ | $12.46_{-1.67}^{+1.75}$ | 9.4 | 2.71 |
091351.1-004507 | 1319 | $138.4632$ | $-0.7520$ | 64.6 | 113.1 | 0.294 | $1.58_{-0.19}^{+0.35}$ | $3.01_{-0.26}^{+0.28}$ | $6.47_{-0.72}^{+1.03}$ | $11.24_{-0.92}^{+1.01}$ | $2.02_{-0.36}^{+0.73}$ | $4.05_{-0.33}^{+0.31}$ | $8.71_{-0.90}^{+1.35}$ | $15.17_{-1.18}^{+1.15}$ | 12.4 | 1.96 |
091412.6+001856 | 1844 | $138.5528$ | $+0.3158$ | 55.0 | 87.3 | 0.165 | $1.81_{-0.31}^{+0.54}$ | $0.93_{-0.10}^{+0.10}$ | $2.04_{-0.26}^{+0.36}$ | $12.78_{-1.38}^{+1.45}$ | $2.01_{-0.35}^{+0.67}$ | $1.26_{-0.14}^{+0.14}$ | $2.75_{-0.36}^{+0.47}$ | $17.30_{-1.96}^{+1.92}$ | 11.8 | 2.61 |
091417.7+031159 | 396 | $138.5740$ | $+3.1998$ | 16.7 | 26.9 | 0.231 | - | $1.12_{-0.25}^{+0.28}$ | $1.78_{-0.43}^{+0.45}$ | $6.78_{-1.53}^{+1.77}$ | $0.96_{-0.34}^{+0.34}$ | $1.15_{-0.27}^{+0.33}$ | $1.81_{-0.47}^{+0.52}$ | $6.96_{-1.69}^{+1.99}$ | 13.3 | 0.70 |
091453.6+041613 | 372 | $138.7235$ | $+4.2704$ | 123.9 | 398.6 | 0.143 | $2.81_{-0.35}^{+0.37}$ | $2.23_{-0.27}^{+0.35}$ | $4.91_{-0.63}^{+0.81}$ | $41.80_{-5.07}^{+6.65}$ | $2.15_{-0.24}^{+0.32}$ | $2.66_{-0.27}^{+0.32}$ | $5.83_{-0.60}^{+0.77}$ | $49.81_{-5.06}^{+6.02}$ | 22.7 | 2.91 |
091509.5+051521 | 6244 | $138.7897$ | $+5.2560$ | 48.0 | 70.0 | 0.25 | - | $2.37_{-0.32}^{+0.31}$ | $4.90_{-0.73}^{+0.91}$ | $12.89_{-1.68}^{+1.70}$ | $1.83_{-0.32}^{+0.53}$ | $3.96_{-0.41}^{+0.44}$ | $8.18_{-1.07}^{+1.29}$ | $21.41_{-2.15}^{+2.47}$ | 10.7 | 3.19 |
091610.1-002348 | 534 | $139.0423$ | $-0.3969$ | 282.2 | 623.3 | 0.322 | $3.83_{-0.93}^{+1.77}$ | $8.66_{-0.44}^{+0.45}$ | $25.51_{-2.42}^{+3.43}$ | $28.22_{-1.42}^{+1.41}$ | $4.45_{-1.02}^{+1.58}$ | $15.51_{-0.62}^{+0.64}$ | $45.50_{-4.08}^{+6.41}$ | $50.53_{-1.97}^{+2.01}$ | 27.1 | 3.04 |
091722.4+010118 | 1526 | $139.3435$ | $+1.0218$ | 56.4 | 187.8 | 0.359 | - | $3.68_{-0.41}^{+0.43}$ | $8.44_{-1.12}^{+1.38}$ | $8.85_{-0.95}^{+1.04}$ | $2.61_{-0.63}^{+1.38}$ | $4.67_{-0.46}^{+0.49}$ | $10.67_{-1.24}^{+1.59}$ | $11.21_{-1.05}^{+1.08}$ | 10.9 | 1.60 |
091849.0+021204 | 994 | $139.7042$ | $+2.2013$ | 94.8 | 245.3 | 0.283 | $3.64_{-1.02}^{+1.69}$ | $3.68_{-0.31}^{+0.33}$ | $9.78_{-1.05}^{+1.90}$ | $15.74_{-1.33}^{+1.29}$ | $3.63_{-0.90}^{+1.60}$ | $5.89_{-0.50}^{+0.45}$ | $15.56_{-1.66}^{+3.07}$ | $25.17_{-2.11}^{+1.84}$ | 17.8 | 3.06 |
091858.0+024946 | 7194 | $139.7418$ | $+2.8295$ | 26.3 | 46.3 | 0.199 | - | $0.76_{-0.10}^{+0.09}$ | $1.31_{-0.18}^{+0.18}$ | $6.58_{-0.87}^{+0.74}$ | $1.28_{-0.21}^{+0.30}$ | $1.03_{-0.14}^{+0.12}$ | $1.79_{-0.25}^{+0.24}$ | $8.97_{-1.20}^{+1.08}$ | 8.1 | 2.08 |
092023.2+013444 | 222 | $140.0967$ | $+1.5789$ | 88.4 | 719.6 | 0.71 | $2.26_{-0.36}^{+0.58}$ | $25.58_{-2.45}^{+2.70}$ | $55.97_{-5.56}^{+6.12}$ | $11.97_{-1.16}^{+1.21}$ | $2.44_{-0.35}^{+0.50}$ | $30.67_{-2.98}^{+2.82}$ | $66.88_{-6.51}^{+6.85}$ | $14.30_{-1.33}^{+1.38}$ | 16.5 | 1.39 |
092037.0-011506 | 5739 | $140.1545$ | $-1.2519$ | 34.3 | 63.0 | 0.154 | $1.01_{-0.13}^{+0.15}$ | $0.48_{-0.06}^{+0.06}$ | $0.81_{-0.10}^{+0.11}$ | $7.40_{-0.86}^{+0.89}$ | $1.16_{-0.20}^{+0.30}$ | $0.61_{-0.08}^{+0.08}$ | $1.03_{-0.14}^{+0.15}$ | $9.39_{-1.18}^{+1.26}$ | 8.6 | 2.15 |
092046.2+002849 | 1915 | $140.1926$ | $+0.4803$ | 40.0 | 69.6 | 0.413 | - | $3.71_{-0.36}^{+0.38}$ | $7.70_{-0.87}^{+0.93}$ | $6.21_{-0.57}^{+0.65}$ | $1.97_{-0.36}^{+0.72}$ | $5.56_{-0.50}^{+0.49}$ | $11.54_{-1.18}^{+1.32}$ | $9.31_{-0.81}^{+0.86}$ | 11.4 | 2.64 |
092049.5+024513 | 489 | $140.2063$ | $+2.7538$ | 142.1 | 514.2 | 0.284 | $2.88_{-0.50}^{+0.72}$ | $5.63_{-0.30}^{+0.34}$ | $14.65_{-0.99}^{+1.27}$ | $23.87_{-1.27}^{+1.38}$ | $3.39_{-0.64}^{+0.87}$ | $8.39_{-0.41}^{+0.40}$ | $21.77_{-1.31}^{+1.96}$ | $35.62_{-1.80}^{+1.61}$ | 23.0 | 3.08 |
092121.2+031726 | 100 | $140.3385$ | $+3.2906$ | 478.6 | 1729.7 | 0.333 | $4.81_{-0.83}^{+1.21}$ | $15.28_{-0.63}^{+0.78}$ | $47.50_{-3.72}^{+3.96}$ | $46.65_{-1.95}^{+2.14}$ | $5.25_{-0.87}^{+1.31}$ | $21.43_{-0.77}^{+0.76}$ | $66.28_{-4.78}^{+5.35}$ | $65.20_{-2.15}^{+2.28}$ | 31.6 | 2.80 |
092202.2+034520 | 5347 | $140.5095$ | $+3.7557$ | 43.4 | 66.6 | 0.270 | - | $2.48_{-0.44}^{+0.41}$ | $7.72_{-1.52}^{+1.76}$ | $8.31_{-1.47}^{+1.32}$ | $5.42_{-1.49}^{+3.46}$ | $4.22_{-0.84}^{+0.98}$ | $13.28_{-2.93}^{+3.63}$ | $14.09_{-2.77}^{+3.36}$ | 13.2 | 2.60 |
092209.3+034628 | 367 | $140.5391$ | $+3.7746$ | 369.0 | 783.1 | 0.270 | $4.25_{-0.83}^{+1.25}$ | $5.76_{-0.52}^{+0.53}$ | $18.11_{-2.44}^{+2.28}$ | $28.01_{-2.54}^{+2.45}$ | $5.39_{-1.30}^{+1.80}$ | $7.98_{-1.01}^{+1.02}$ | $24.93_{-3.68}^{+3.98}$ | $38.73_{-4.82}^{+5.01}$ | 23.1 | 2.80 |
092220.4+034806 | 4014 | $140.5852$ | $+3.8017$ | 24.1 | 48.9 | 0.267 | - | $1.24_{-0.19}^{+0.19}$ | $2.87_{-0.51}^{+0.62}$ | $5.90_{-0.86}^{+0.91}$ | $2.31_{-0.55}^{+1.25}$ | $1.53_{-0.26}^{+0.33}$ | $3.52_{-0.67}^{+0.98}$ | $7.27_{-1.27}^{+1.51}$ | 8.5 | 1.48 |
092235.8-002443 | 3133 | $140.6494$ | $-0.4120$ | 30.2 | 49.4 | 0.055 | $1.90_{-0.32}^{+0.59}$ | $0.15_{-0.03}^{+0.04}$ | $0.33_{-0.06}^{+0.08}$ | $20.84_{-3.68}^{+5.01}$ | $2.23_{-0.39}^{+0.56}$ | $0.16_{-0.03}^{+0.05}$ | $0.34_{-0.07}^{+0.11}$ | $21.67_{-4.31}^{+6.25}$ | 11.9 | 2.16 |
092241.9+020719 | 1535 | $140.6749$ | $+2.1222$ | 32.5 | 52.2 | 0.198 | - | $0.79_{-0.13}^{+0.14}$ | $1.37_{-0.22}^{+0.25}$ | $6.91_{-1.10}^{+1.19}$ | $1.29_{-0.20}^{+0.37}$ | $0.93_{-0.15}^{+0.13}$ | $1.61_{-0.25}^{+0.27}$ | $8.15_{-1.29}^{+1.19}$ | 8.6 | 1.60 |
092339.0+052654 | 1894 | $140.9129$ | $+5.4486$ | 30.3 | 158.3 | 0.37 | $2.52_{-0.38}^{+0.52}$ | $35.48_{-10.16}^{+13.00}$ | $98.68_{-28.12}^{+35.92}$ | $81.89_{-23.26}^{+29.79}$ | $3.93_{-0.28}^{+0.46}$ | $36.83_{-9.88}^{+12.25}$ | $101.61_{-27.28}^{+34.04}$ | $84.78_{-22.60}^{+28.09}$ | 16.3 | 1.02 |
092405.0-013059 | 298 | $141.0211$ | $-1.5165$ | 196.6 | 576.5 | 0.337 | - | $3.71_{-0.41}^{+0.49}$ | $10.24_{-1.39}^{+2.25}$ | $10.80_{-1.20}^{+1.36}$ | $3.97_{-1.01}^{+2.54}$ | $4.82_{-0.43}^{+0.53}$ | $13.25_{-1.56}^{+2.90}$ | $14.06_{-1.24}^{+1.47}$ | 16.7 | 1.84 |
092409.4+040057 | 1437 | $141.0393$ | $+4.0160$ | 122.1 | 238.5 | 0.084 | - | $0.36_{-0.03}^{+0.03}$ | $0.56_{-0.04}^{+0.04}$ | $20.24_{-1.43}^{+1.55}$ | $0.92_{-0.07}^{+0.08}$ | $0.40_{-0.03}^{+0.03}$ | $0.63_{-0.05}^{+0.05}$ | $22.72_{-1.92}^{+1.73}$ | 14.8 | 2.81 |
092533.6-014205 | 1483 | $141.3903$ | $-1.7016$ | 51.0 | 150.6 | 0.23 | - | $1.03_{-0.10}^{+0.11}$ | $2.29_{-0.26}^{+0.31}$ | $6.82_{-0.63}^{+0.73}$ | $2.30_{-0.44}^{+0.78}$ | $1.22_{-0.13}^{+0.14}$ | $2.71_{-0.36}^{+0.36}$ | $8.01_{-0.85}^{+0.95}$ | 11.2 | 1.54 |
092546.3-014347 | 566 | $141.4432$ | $-1.7298$ | 197.1 | 480.2 | 0.23 | - | $2.23_{-0.20}^{+0.24}$ | $5.21_{-0.56}^{+0.61}$ | $15.01_{-1.31}^{+1.55}$ | $2.64_{-0.45}^{+0.84}$ | $2.98_{-0.24}^{+0.32}$ | $6.96_{-0.69}^{+0.82}$ | $20.04_{-1.55}^{+2.19}$ | 18.3 | 2.24 |
092629.3+032614 | 2050 | $141.6225$ | $+3.4374$ | 19.7 | 117.5 | 0.088 | $0.90_{-0.07}^{+0.07}$ | $0.24_{-0.03}^{+0.03}$ | $0.38_{-0.06}^{+0.05}$ | $12.24_{-1.79}^{+1.56}$ | $0.94_{-0.07}^{+0.07}$ | $0.30_{-0.05}^{+0.05}$ | $0.47_{-0.07}^{+0.08}$ | $15.24_{-2.38}^{+2.64}$ | 9.3 | 2.49 |
092647.5+050032 | 137 | $141.6980$ | $+5.0091$ | 505.4 | 1395.7 | 0.45 | - | $18.64_{-1.76}^{+1.82}$ | $65.64_{-8.77}^{+8.92}$ | $28.64_{-2.66}^{+2.82}$ | $6.72_{-1.68}^{+2.91}$ | $25.68_{-2.62}^{+2.48}$ | $90.72_{-13.06}^{+11.87}$ | $39.42_{-4.17}^{+3.98}$ | 23.6 | 2.28 |
092648.0+050124 | 6875 | $141.7002$ | $+5.0234$ | 16.0 | 26.5 | 0.45 | $7.04_{-2.29}^{+4.00}$ | $6.57_{-1.35}^{+1.58}$ | $21.70_{-4.80}^{+5.49}$ | $10.04_{-2.07}^{+2.42}$ | $5.77_{-1.16}^{+1.78}$ | $9.45_{-2.08}^{+2.13}$ | $31.16_{-7.13}^{+7.83}$ | $14.47_{-3.31}^{+3.05}$ | 13.3 | 1.84 |
092650.5+035755 | 671 | $141.7106$ | $+3.9654$ | 33.0 | 304.8 | 0.377 | $2.86_{-0.69}^{+1.58}$ | $4.59_{-0.59}^{+0.62}$ | $10.53_{-1.62}^{+1.48}$ | $9.81_{-1.27}^{+1.30}$ | $2.59_{-0.54}^{+1.18}$ | $4.97_{-0.60}^{+0.61}$ | $11.29_{-1.61}^{+1.62}$ | $10.56_{-1.32}^{+1.33}$ | 11.3 | 1.06 |
092735.3+014423 | 266 | $141.8974$ | $+1.7399$ | 32.9 | 43.2 | 0.149 | - | $0.82_{-0.14}^{+0.15}$ | $1.74_{-0.32}^{+0.40}$ | $14.03_{-2.36}^{+2.50}$ | $1.90_{-0.45}^{+0.73}$ | $0.98_{-0.15}^{+0.16}$ | $2.06_{-0.37}^{+0.45}$ | $16.62_{-2.50}^{+2.79}$ | 12.1 | 2.15 |
092737.7+020607 | 1924 | $141.9074$ | $+2.1021$ | 106.4 | 191.5 | 0.419 | - | $4.82_{-0.51}^{+0.50}$ | $11.50_{-1.36}^{+1.32}$ | $8.15_{-0.84}^{+0.85}$ | $2.82_{-0.65}^{+1.00}$ | $7.62_{-0.69}^{+0.65}$ | $18.04_{-1.76}^{+1.97}$ | $12.88_{-1.14}^{+1.05}$ | 11.8 | 2.04 |
092821.2+042149 | 666 | $142.0884$ | $+4.3637$ | 113.0 | 375.3 | 0.23 | $3.41_{-0.94}^{+1.88}$ | $3.19_{-0.33}^{+0.35}$ | $7.10_{-0.83}^{+0.90}$ | $19.97_{-2.03}^{+2.13}$ | $2.45_{-0.48}^{+0.77}$ | $4.08_{-0.46}^{+0.49}$ | $9.09_{-1.16}^{+1.20}$ | $25.53_{-2.82}^{+2.99}$ | 15.7 | 2.22 |
092915.7-001357 | 1549 | $142.3158$ | $-0.2327$ | 31.5 | 181.0 | 0.322 | $1.83_{-0.33}^{+0.49}$ | $2.29_{-0.24}^{+0.26}$ | $4.19_{-0.50}^{+0.67}$ | $6.62_{-0.71}^{+0.77}$ | $1.44_{-0.19}^{+0.34}$ | $2.76_{-0.30}^{+0.36}$ | $5.06_{-0.62}^{+0.87}$ | $8.01_{-0.92}^{+1.04}$ | 9.4 | 1.36 |
092921.7+040040 | 609 | $142.3408$ | $+4.0112$ | 51.8 | 297.4 | 0.50 | $3.39_{-0.98}^{+1.88}$ | $8.34_{-1.32}^{+1.19}$ | $22.82_{-4.00}^{+5.39}$ | $9.77_{-1.49}^{+1.39}$ | - | $9.37_{-1.41}^{+1.26}$ | $25.63_{-4.57}^{+5.75}$ | $10.91_{-1.54}^{+1.52}$ | 11.6 | 1.12 |
092955.8-003403 | 1967 | $142.4829$ | $-0.5676$ | 39.4 | 137.1 | 0.150 | $1.76_{-0.24}^{+0.56}$ | $0.73_{-0.07}^{+0.07}$ | $1.43_{-0.15}^{+0.16}$ | $12.14_{-1.08}^{+1.20}$ | $1.67_{-0.27}^{+0.41}$ | $0.92_{-0.10}^{+0.09}$ | $1.78_{-0.19}^{+0.21}$ | $15.19_{-1.60}^{+1.58}$ | 11.1 | 2.35 |
093003.3+035630 | 288 | $142.5140$ | $+3.9417$ | 82.1 | 593.1 | 0.327 | $2.22_{-0.34}^{+0.62}$ | $6.01_{-0.53}^{+0.55}$ | $14.40_{-1.59}^{+1.67}$ | $18.08_{-1.60}^{+1.66}$ | $2.81_{-0.56}^{+1.22}$ | $6.48_{-0.51}^{+0.55}$ | $15.51_{-1.57}^{+1.64}$ | $19.53_{-1.56}^{+1.59}$ | 15.3 | 1.30 |
093009.0+040144 | 536 | $142.5376$ | $+4.0289$ | 76.4 | 376.0 | 0.328 | $2.59_{-0.54}^{+1.03}$ | $4.61_{-0.45}^{+0.40}$ | $10.65_{-1.31}^{+1.48}$ | $13.68_{-1.33}^{+1.18}$ | $2.66_{-0.67}^{+1.51}$ | $5.04_{-0.44}^{+0.45}$ | $11.66_{-1.29}^{+1.66}$ | $14.93_{-1.31}^{+1.34}$ | 13.0 | 1.30 |
093141.2-004717 | 1705 | $142.9219$ | $-0.7883$ | 13.7 | 30.5 | 0.093 | - | $0.13_{-0.04}^{+0.05}$ | $0.21_{-0.06}^{+0.08}$ | $5.99_{-1.77}^{+2.26}$ | $0.50_{-0.01}^{+0.01}$ | $0.14_{-0.04}^{+0.05}$ | $0.22_{-0.07}^{+0.08}$ | $6.21_{-1.91}^{+2.31}$ | 7.8 | 1.52 |
093149.8-020143 | 6494 | $142.9576$ | $-2.0289$ | 27.5 | 39.9 | 0.13 | $1.01_{-0.09}^{+0.09}$ | $0.18_{-0.03}^{+0.03}$ | $0.29_{-0.04}^{+0.05}$ | $3.77_{-0.53}^{+0.58}$ | $0.97_{-0.09}^{+0.10}$ | $0.23_{-0.05}^{+0.04}$ | $0.37_{-0.07}^{+0.07}$ | $4.86_{-0.98}^{+0.85}$ | 7.5 | 2.16 |
093151.3-002212 | 836 | $142.9638$ | $-0.3701$ | 54.0 | 307.1 | 0.336 | $2.53_{-0.50}^{+0.93}$ | $4.00_{-0.38}^{+0.41}$ | $8.74_{-0.96}^{+1.05}$ | $11.08_{-1.05}^{+1.12}$ | $2.36_{-0.43}^{+0.76}$ | $4.77_{-0.41}^{+0.46}$ | $10.46_{-1.13}^{+1.11}$ | $13.19_{-1.16}^{+1.19}$ | 12.1 | 1.52 |
093403.5-001422 | 900 | $143.5148$ | $-0.2397$ | 79.7 | 279.5 | 0.24 | $1.87_{-0.25}^{+0.37}$ | $2.43_{-0.21}^{+0.24}$ | $4.92_{-0.48}^{+0.67}$ | $14.30_{-1.27}^{+1.39}$ | $1.78_{-0.24}^{+0.47}$ | $3.02_{-0.26}^{+0.28}$ | $6.07_{-0.56}^{+0.88}$ | $17.71_{-1.48}^{+1.66}$ | 13.6 | 2.04 |
093431.3-002309 | 1641 | $143.6304$ | $-0.3860$ | 35.5 | 87.4 | 0.342 | $3.02_{-0.75}^{+1.17}$ | $3.36_{-0.32}^{+0.36}$ | $12.42_{-2.28}^{+1.99}$ | $9.86_{-0.88}^{+1.07}$ | - | $4.85_{-0.42}^{+0.50}$ | $17.91_{-3.25}^{+2.87}$ | $14.26_{-1.25}^{+1.42}$ | 11.8 | 2.18 |
093500.7+005417 | 649 | $143.7532$ | $+0.9048$ | 104.4 | 330.3 | 0.361 | $3.27_{-0.78}^{+1.54}$ | $6.34_{-0.52}^{+0.56}$ | $15.07_{-1.46}^{+1.47}$ | $15.19_{-1.26}^{+1.35}$ | $2.75_{-0.54}^{+0.91}$ | $7.69_{-0.59}^{+0.59}$ | $18.16_{-1.56}^{+1.65}$ | $18.36_{-1.34}^{+1.37}$ | 14.2 | 1.58 |
093513.0+004757 | 213 | $143.8046$ | $+0.7994$ | 332.8 | 836.9 | 0.356 | - | $12.93_{-1.12}^{+1.11}$ | $40.29_{-5.57}^{+6.05}$ | $33.92_{-2.91}^{+2.82}$ | $5.32_{-1.37}^{+2.24}$ | $20.24_{-2.00}^{+1.72}$ | $62.75_{-8.42}^{+9.79}$ | $52.93_{-5.01}^{+4.55}$ | 27.5 | 3.04 |
093520.9+023234 | 82 | $143.8374$ | $+2.5429$ | 175.9 | 1645.8 | 0.516 | $3.39_{-0.54}^{+0.89}$ | $27.87_{-1.85}^{+1.74}$ | $81.36_{-6.89}^{+10.61}$ | $31.01_{-1.96}^{+1.86}$ | $4.38_{-0.88}^{+1.59}$ | $34.33_{-2.33}^{+2.15}$ | $100.27_{-8.49}^{+11.91}$ | $38.12_{-2.39}^{+2.37}$ | 23.5 | 1.89 |
093522.2+032329 | 1242 | $143.8427$ | $+3.3915$ | 86.3 | 248.3 | 0.324 | $2.75_{-0.62}^{+1.20}$ | $3.77_{-0.37}^{+0.34}$ | $8.41_{-0.97}^{+0.90}$ | $11.35_{-1.10}^{+1.01}$ | $2.44_{-0.50}^{+0.97}$ | $4.86_{-0.40}^{+0.44}$ | $10.85_{-1.14}^{+1.28}$ | $14.64_{-1.20}^{+1.26}$ | 12.6 | 1.80 |
093531.4+022710 | 2609 | $143.8811$ | $+2.4530$ | 36.6 | 95.3 | 0.225 | - | $1.04_{-0.13}^{+0.13}$ | $1.99_{-0.32}^{+0.45}$ | $6.98_{-0.87}^{+0.87}$ | $1.49_{-0.62}^{+0.83}$ | $1.21_{-0.16}^{+0.17}$ | $2.31_{-0.36}^{+0.50}$ | $8.12_{-1.06}^{+1.09}$ | 8.4 | 1.50 |
093709.7+014143 | 6251 | $144.2908$ | $+1.6954$ | 27.9 | 50.0 | 0.38 | - | $3.16_{-0.39}^{+0.41}$ | $5.88_{-0.83}^{+0.95}$ | $6.29_{-0.75}^{+0.78}$ | $1.48_{-0.22}^{+0.56}$ | $4.67_{-0.54}^{+0.51}$ | $8.67_{-1.13}^{+1.31}$ | $9.31_{-1.06}^{+0.98}$ | 9.0 | 1.80 |
Table 6: continued. 121212Column 1: Cluster identification. Column 2: Unique
source ID in the main eFEDS source catalog in Brunner et al. (2021). Columns 3
and 4: RA and Dec in units of degrees. Column 5: Extent likelihood. Column 6:
Detection likelihood. RA, Dec, extent likelihood, and detection likelihood are
provided by the eSASS source detection algorithm (see Sect. 2.1). Column 7:
photometric redshifts provided by MCMF (see Sect. 2.3). Values with three
digits are spectroscopic redshifts. Column 8: Temperature in units of keV
within 300 kpc. Column 9: Luminosity in units of $10^{43}\leavevmode\nobreak\
{\rm erg}\leavevmode\nobreak\ {\rm s}^{-1}$ in the soft band (0.5–2 keV)
within 300 kpc. Column 10: Same as column 9 for bolometric (0.01–100 keV)
luminosity. Column 11: Flux in units of $10^{-14}\leavevmode\nobreak\ {\rm
erg}\leavevmode\nobreak\ {\rm s}^{-1}\leavevmode\nobreak\ {\rm cm}^{-2}$
within 300 kpc. Column 12: temperature in units of keV within 500 kpc. Column
13: Luminosity in units of $10^{43}\leavevmode\nobreak\ {\rm
erg}\leavevmode\nobreak\ {\rm s}^{-1}$ in the soft band (0.5–2 keV) within 500
kpc. Column 14: same as column 13 for bolometric (0.01–100 keV) luminosity.
Column 15: Flux in units of $10^{-14}\leavevmode\nobreak\ {\rm
erg}\leavevmode\nobreak\ {\rm s}^{-1}\leavevmode\nobreak\ {\rm cm}^{-2}$
within 500 kpc. Column 16: Maximum S/N in the soft band. Column 17: Radii in
units of arcmin corresponding to the maximum S/N in column 16.