#  Human-AI Collaborative Taxonomy Construction:
A Case Study in Profession-Specific Writing Assistants

Minhwa Lee  University of MinnesotaTwin CitiesMinnesotaUSA<EMAIL_ADDRESS>,
Zae Myung Kim  University of MinnesotaTwin CitiesMinnesotaUSA<EMAIL_ADDRESS>,  Vivek A. Khetan  Accenture LabsSan FranciscoCaliforniaUSA
<EMAIL_ADDRESS>and  Dongyeop Kang  University of MinnesotaTwin
CitiesMinnesotaUSA<EMAIL_ADDRESS>
(2024)

###### Abstract.

Large Language Models (LLMs) have assisted humans in several writing tasks,
including text revision and story generation. However, their effectiveness in
supporting domain-specific writing, particularly in business contexts, is
relatively less explored. Our formative study with industry professionals
revealed the limitations in current LLMs’ understanding of the nuances in such
domain-specific writing. To address this gap, we propose an approach of human-
AI collaborative taxonomy development to perform as a guideline for domain-
specific writing assistants. This method integrates iterative feedback from
domain experts and multiple interactions between these experts and LLMs to
refine the taxonomy. Through larger-scale experiments, we aim to validate this
methodology and thus improve LLM-powered writing assistance, tailoring it to
meet the unique requirements of different stakeholder needs.

Human-AI Collaboration, Taxonomy, AI-assisted Writing

††copyright: acmlicensed††journalyear: 2024††doi: XXXXXXX.XXXXXXX††conference:
Make sure to enter the correct conference title from your rights confirmation
email; May 2024; Hawaii, USA††copyright: none††ccs: Computing methodologies
Natural language generation††ccs: Human-centered computing Collaborative and
social computing design and evaluation methods Figure 1. An end-to-end
pipeline of our three-step Human-AI collaborative taxonomy construction
process. For each step, we portray several design implications for better
human-AI interaction strategies that were described in Section 3.

##  1\. Introduction

The emergence of large language models (LLMs) has significantly enriched the
writing support tools, covering text revisions (Du et al., 2022a; Raheja et
al., 2023; Schick et al., 2022) to creative content generation (Mirowski et
al., 2022; Chakrabarty et al., 2022; Ippolito et al., 2022). However, the
deployment of LLMs in tailored writing assistance to specific stakeholder
needs (e.g., legal writing) remains relatively unexplored. This oversight
highlights a concern about the reliability of AI-driven tools in meeting the
nuanced demands of domain-specific writing. Also, commercial offerings, such
as the company Writer’s LLM-powered writing assistants for business sectors
(e.g., Palmyra (team, 2023)), are not widely accessible. This raises questions
about the essential steps to develop publicly available AI writing assistants
that cater to different stakeholders’ unique needs and knowledge.

Motivated by these challenges, we conducted a formative study with current
business professionals, which revealed LLM’s inadequate understanding of
profession-level writing. The study also found a huge need to categorize a
vast array of domain-specific conventions and requirements in writing tasks
and make a structured taxonomy of such context.

Therefore, we propose a novel approach of “human-AI collaborative taxonomy
construction” for the use of domain-specific writing support. Such taxonomy
construction can serve as a foundational step in developing AI writing
assistants that can precisely interpret and generate text that aligns with the
varied expectations of different professions. Also, based on the rigorous
business expert validation procedures and a variety of human-AI interaction
strategies, our approach can bolster the reliability of AI writing assistants
customized to specific domains. For the rest of the paper, we interchangeably
use the terms ‘domain-specific writing’ and ‘profession-specific writing’.

##  2\. Formative Study: Challenges of Using LLMs as Domain-specific Writing
Assistants

To assess the effectiveness of LLMs as domain-specific writing assistants, we
conducted an initial formative study111This research has been approved by the
Institutional Review Board (IRB) of the first author’s institution. with two
professionals from the marketing and human resources (HR) sectors, each with
over five years of field experience. These participants, referred to as P1 and
P2, were asked to (1) provide inputs to GPT-4 to generate a writing template
relevant to their field; (2) revise the template; and (3) categorize each edit
according to predefined revision types outlined in a taxonomy (Du et al.,
2022b), along with providing rationales behind their edits. The qualitative
data was analyzed using thematic analysis and open coding techniques. For a
detailed description of the study procedures, please refer to Appendix A.

Figure 2. The distribution of revision intentions annotated by the study
participants across six writing templates generated by GPT-4.

###  2.1. Limited Understanding of Nuances in Business Writing by LLMs

We found that GPT-4’s output often fails to align with the stylistic and
linguistic expectations due to a lack of knowledge about domain-specific
writing. First, the participants commonly criticized clarity, especially its
verbosity (Figure 2): “The paragraph is too lengthy and I would like to get to
the point right away.” (P1) and “… need to get driven to the CTA (Call-to-
Action) as soon as possible… they don’t want long emails..” (P2). Second, the
study participants highlighted (1) lack of coherency and (2) style changes.
They commonly showed concern in GPT-4’s lack of precision in word choice and
organization structure that could misinterpret or dilute the intended delivery
in a business context: “The word ‘essential’ is subject to interpretation.
Saying ‘absolutely necessary’ might suggest the company is in such dire
straits that people might leave the organization….” (P1) and “Needs to call
out sections to get the readers’ attention. This is also the general style of
a proposal.” (P2). Third, all participants mentioned that GPT-4 created its
own narratives that were not asked in the prompt and did not reflect objective
details requested by the participant in the output. And finally, the
participants who marked the “Other” category (Figure 2) pointed out LLMs’ lack
of understanding about the objective of a writing task (i.e., P2 mentioned
“..a minute is to highlight what was discussed and action steps. Needs to be
easy to read and call out sections a reader may care about.”).

From this formative study, we observed that current LLMs often lack nuanced
comprehension of domain-specific expectations unless provided with detailed
guidelines for their generated writing. Consequently, we identified the need
to develop a more sophisticated taxonomy of writing specific to various
business domains. This taxonomy will serve as a guideline, enhancing the
pipeline for model training and enabling more tailored revision suggestions in
domain-specific writing contexts.

##  3\. Human-AI Collaborative Taxonomy Construction

In this section, we propose a novel task of “human-AI collaborative taxonomy
construction” as an application to better develop a domain-specific AI writing
assistant, following the standard procedures of taxonomy construction
(Nickerson et al., 2013). Grounded on the design implications of LLM-assisted
qualitative analysis process (Gao et al., 2023; Zhang et al., 2023; Katz et
al., 2023; Xiao et al., 2023; Jiang et al., 2021; Yan et al., 2023), we
suggest the three-step approach with several design ideas (Figure 1). Also, we
present the promising results from our partial empirical testing of this
three-step approach using a simplified scenario, as described in Appendix B.

###  3.1. STEP 1: Taxonomy Generation

This process harnesses the capabilities of LLMs to generate all components of
taxonomy from scratch222LLMs are trained on extensive and diverse datasets
encompassing vast world knowledge (Liu et al., 2024), hence we task them with
generating elements of the taxonomy., given the absence of domain-specific
data or an existing taxonomy, which are typically required in traditional
construction processes (Nickerson et al., 2013). Based on the user inputs
about a certain domain and sophistical designed output guidelines in the
prompt design (Mirowski et al., 2022; Wei et al., 2023), we hierarchically
generate each level of taxonomy. Additionally, to address concerns of
transparency and explainability in AI (Dwivedi et al., 2023; Gomez et al.,
2023; Gebreegziabher et al., 2023), LLMs are prompted to provide reasoning for
each generated element, increasing trustworthiness in the output.

###  3.2. STEP 2: Taxonomy Validation

To mitigate concerns about the dependence on artificially generated taxonomy
as a beginning step of taxonomy construction, we perform multiple rounds of
rigorous human expert validations and improvements, via dialogue-based user
interaction. Inspired by several NLP works that proposed multiple LLM
workarounds for a single task (Wang et al., 2023; Welleck et al., 2022;
Saunders et al., 2022), our work suggests a novel approach of “LLMs-as-
Mediators” as a human-AI interaction design during this validation process.
Here, we employ multiple LLMs with different roles: (1) ‘Interviewer LLM’ for
asking questions and getting expert replies over multiple iterations through
dialogue and (2) ‘Creator LLM’ for generating the improved version of the
taxonomy based on the received expert feedback from ‘Interviewer LLM’.

###  3.3. Step 3: Taxonomy Merging & Testing

To foster greater consensus among researchers, we propose a method where LLMs
aggregate and merge multiple finalized outputs from several experts within the
same domain. Moreover, LLMs will ensure that the elements of the combined
output are comprehensive and non-overlapping. By leveraging LLMs, we aim to
mitigate individual biases and enhance the reliability of the generated
taxonomy. Subsequently, following established practices in taxonomy evaluation
processes (Nickerson et al., 2013; Shah et al., 2024), both human experts and
LLMs will independently annotate writing templates using the constructed
taxonomy. We will assess the inter-coder reliability (ICR) between human
coders and between LLM and expert annotations. High ICR scores between both
sets of annotators would confirm the substantial reliability of the taxonomy
for the specific domain.

##  4\. Future Plans

Our plan involves developing a user-friendly web application with two main
components: (1) a hierarchical taxonomy display and (2) a chat dialogue
interface with an Interviewer LLM. Through iterative dialogue, users will
validate and refine the taxonomy, while recent prompting techniques like ReAct
(Yao et al., 2023) and ART (Paranjape et al., 2023) will define desired LLM
behaviors. Furthermore, we plan to conduct the experiments with open-source
models (e.g., Mistral (Jiang et al., 2023), LLaMA (Touvron et al., 2023), OLMo
(Groeneveld et al., 2024), etc.) in addition to GPT-4. Lastly, we will recruit
domain experts via a freelancing platform (e.g.,
Upwork333https://www.upwork.com/) for multiple human studies to ensure the
effectiveness of our approach.

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##  Appendix A More About Formative Study

###  A.1. Recruitment Process

We recruited 2 industry professionals (referred to as P1 and P2) via the
U.S.-based freelancing platform ‘Upwork.’ Note that the study was approved by
the Institutional Review Board of the first author’s institution. When
recruiting the participants, we set the eligibility of participation as
follows: (1) at least 3 years of working experience in their business sector;
(2) fluent in English (Native Speaker Preferred); and (3) prior experience in
several business-specific writing tasks such as email, proposals, and meeting
minutes. In addition to meeting the three requirements, we also asked the
applicants to answer the screening question: “Please describe your level of
experience in your work group, including specific project details.”. Based on
the manual review of the responses, we invited the two participants (P1 and
P2) who met all the requirements of our study. After the successful completion
of the study, the participants received a compensation of 20 USD for their
participation in a 1-hour duration of our formative study. Table 1 shows the
detailed background of the study participants we recruited.

| Gender | Residence | Working Domain | Years of Experience | English Proficiency | Disability Status
---|---|---|---|---|---|---
P1 | Male | United States | Human Resources (HR) | 14 | Native | No
P2 | Female | United States | Marketing | 8 | Native | No

Table 1. Detailed background information of the formative study participants.

###  A.2. Experiment Procedures

The formative study involved three steps. First, the participant provided
their background information: (1) last name (for data file identifier purposes
only); (2) domain group; (3) the type of writing task (e.g., meeting minutes,
emails, and project proposals); and (4) the content of the writing in fully
complete English sentences. Based on these user inputs, the GPT-4 integrated
into the interface generated a writing template. When the generated writing
was present, the participants were asked to revise the writing and to click
the button to store all the revisions securely.

Next, the participants were prompted to move to the next page of the
interface, where their revisions were visualized with the additions in red and
the deletions in strikethrough. This page was prepared to let the participants
check whether their revisions were properly stored and visualized; if not, we
asked them to go back to the previous step and provide revisions again.

Lastly, after they confirmed that their revisions were successfully stored and
visualized, we let the participants move to the last page of the interface.
Here, they were given a list of instructions on how to annotate their
sentence-level edits with the provided types of revision intentions that were
established by a previous work (Du et al., 2022b). After they completed the
annotation of every edit they made in the GPT-4 generated writing, they were
asked to leave the interface by closing the tab of the interface in their
local search engine (e.g., Chrome, Safari). Our interface was developed to
store all information produced during the study into our password-protected
server - (1) revision history with the annotations; (2) the original GPT-4
writing; and (3) the user inputs. Please note that we used the last name
information only for data identifier purposes (i.e., renaming the data file
with the last name) and did not collect or use any personally identifiable
information during the study.

###  A.3. Interface Development

We used Streamlit and Python to develop a web interface for the formative
study process. Then, we made the web interface publicly accessible by hosting
it on a website using ngrok. This enabled the participants to access the
interface using their local search engine. The code repository is available at
the following GitHub repository: https://github.com/minnesotanlp/co-taxonomy.
Figure 3 is a screenshot of the interface shown to the participants for the
first step described in Appendix A.2.

Figure 3. The interface for the first step, where a participant provided their
background information that GPT-4 then used to generate a writing template.

##  Appendix B Initial Results

We have conducted empirical testing of our human-AI collaborative taxonomy
construction process, partially executing STEP 1 and 2 using a simplified
scenario focused on developing a taxonomy of text revision intentions,
specifically for email writing within the legal domain.

Intention | Description | Examples
---|---|---
Legal Argument Strengthening |  Adding supporting legal precedents to reinforce an argument. |  | The case we are handling has similarities with other cases.
---
$\rightarrow$ The case we are handling is similar to Smith v. Jones,
where the court held a comparable view on contractual obligations.
| Our argument in this lawsuit is strong.
---
$\rightarrow$ Our argument, bolstered by the precedent set in Brown v. Board
of Education,
is particularly strong in advocating for equal rights.
Integrating additional factual evidence to solidify a legal stance. |  | Our client’s position in this matter is legally sound.
---
$\rightarrow$ Our client’s position is legally sound, supported by the
financial records
and witness statements provided.
| This case is straightforward.
---
$\rightarrow$ This case is straightforward, as evidenced by the detailed
timeline
of events and corroborating emails.
Enhancing the persuasiveness of the argument by refining logical reasoning. |  | We believe our client is not liable.
---
$\rightarrow$ Our client is not liable, as logically, the responsibility falls
on the contractor, given the terms of the agreement.
| This case should be dismissed.
---
$\rightarrow$ This case should be dismissed, considering the lack of
causation between our client’s actions and the alleged damages
Incorporating expert testimony to bolster legal claims. |  | Our stance on the patent infringement is valid.
---
$\rightarrow$ Our stance is strengthened by the expert testimony of Dr. Smith,
a renowned patent specialist.
| The damages claimed are excessive.
---
$\rightarrow$ The damages claimed are excessive, as per the assessment of
leading industry expert John Doe.

Table 2. An example of the GPT-4 generated taxonomy of text revision for legal
domain, through STEP 1. The proposed revisions by the model are indicated by
the arrows.

Table 2 showcases an example of the generated taxonomy elements across all
levels, featuring descriptions and examples pertinent to the legal domain,
ensuring clarity and relevance. During STEP 2, the ‘Interviewer LLM’ engaged
one of the authors, posing the question: “What do you think can be added as an
additional description category if not comprehensive?”. Upon the author’s
suggestion to include provisions for addressing counterarguments in legal
arguments, the Interviewer LLM proposed the addition of a “Addressing
Counterarguments” category, along with a rationale emphasizing proactive
identification of potential weaknesses or counterarguments to legal positions.
Although these results are pending expert validation, they signify the promise
of our approach. We plan to further validate this methodology through larger-
scale experiments and incorporate advanced multi-step prompting techniques
such as ReACT (Yao et al., 2023) or ART (Paranjape et al., 2023).

###  B.1. Hierarchical Prompt Design (STEP 1)

We present the designed prompts for GPT-4 to generate all levels of taxonomy
(according to STEP 1), using a hierarchical prompt chaining technique inspired
by Wei et al. (2023); Mirowski et al. (2022). Listing B.2 present the prompt
for the ‘Intention’ category generation.

###  B.2. Questions for Interviewer LLM (STEP 2)

For the dialogue-based validation during STEP 2, we brainstormed several
questions for ‘Interviewer LLM’ to check the following aspects of the
‘Description’ of the taxonomy. These aspects were partially drawn from Shah et
al. (2024) that suggested some evaluation criteria for the LLM-generated
taxonomy of user intents in the information retrieval (IR) domain.

  * •

Consistency: “Do all descriptions for label not overlap in the meaning? If
not, which description seems ambiguous/duplicate?”

  * •

Clarity: “Do the descriptions provide clear understanding? If not, which one
is unclear and how should it be improved?”

  * •

Practicality: “Is there any description that is not typically considered in
your domain? Which one?”

  * •

Comprehensiveness: “What do you think can be added as an additional
description category if not comprehensive?”

[!ht]

⬇

f”’

You’re a helpful assistant helping a user generate the labels of edit
intentions in their writing task of {email}.

The user is a business professional in the following domain: {legal domain}.

\par### Output Guidelines:

\par- Suppose that the user revises their original text of {email} for better
communications within their domain group (e.g., a marketing person for other
team members in the marketing team; an HR worker writing for the HR team,
etc.)

- Please consider intentions of revising contents, specifically for the domain of {legal}.

- In addition to domain-specific content-related taxonomy, include general syntactic revision intentions, such as grammar corrections, style coherency, clarity, etc.

- Please generate labels of feasible scenarios as much as you can. It should be more than 10 labels at least.

- For each generated intention, prepend the token <label> and append </label>.

- Please consider that all those generated labels must be mutually exclusive (e.g., no overlapping between labels).

- Please consider that all those generated labels must be collectively exhaustive (e.g., cover every possible case of revision intentions in practice).

- Once you finish generating all possible labels of revision intention of {email} for the domain of {legal}, end your response with the tag <end>.

\par\par### Example

\parGiven the domain of marketing and the writing task of email, the example
looks as follows:

\par“‘

<label> Target Engagement </label>

<label> Visual Content Integration </label>

<label> Data-Driven Insights Presentation </label>

….

<end>

“‘

### Output

”’

A prompt for GPT-4 to generate the ‘Intention’ category.


# Baryon Acoustic Oscillations from Integrated Neutral Gas Observations: an
instrument to observe the 21cm hydrogen line in the redshift range 0.13 $<$ z
$<$ 0.45 - status update

Carlos. A. Wuensche Divisão de Astrofísica, Instituto Nacional de Pesquisas
Espaciais. Av. dos Astronautas 1758, Jd. da Granja, CEP 12227-010, São José
dos Campos - SP, Brazil  Elcio Abdalla Instituto de Física, Universidade de
São Paulo, R. do Matão, 1371 - Butantã, São Paulo - SP, 05508-09, Brazil.
Filipe B. Abdalla Department of Physics and Electronics, Rhodes University, PO
Box 94, Grahamstown, 6140, South Africa  Divisão de Astrofísica, Instituto
Nacional de Pesquisas Espaciais. Av. dos Astronautas 1758, Jd. da Granja, CEP
12227-010, São José dos Campos - SP, Brazil  Instituto de Física, Universidade
de São Paulo, R. do Matão, 1371 - Butantã, São Paulo - SP, 05508-09, Brazil.
University College London, Gower Street, London,WC1E 6BT, UK  Luciano Barosi
Unidade Acadêmica de Física, Universidade Federal de Campina Grande, R.
Aprígio Veloso, 58429-900 - Bodocongó, Campina Grande - PB, Brazil  Bin Wang
School of Aeronautics and Astronautics, Shanghai Jiao Tong University,
Shanghai 200240, China  Center for Gravitation and Cosmology, College of
Physical Science and Technology, Yangzhou University, 225009, Yangzhou, China
Rui An Department of Physics and Astronomy, University of Southern California,
Los Angeles, CA, 90089, USA  School of Aeronautics and Astronautics, Shanghai
Jiao Tong University, Shanghai 200240, China  João A. M. Barreto Instituto de
Física, Universidade de São Paulo, R. do Matão, 1371 - Butantã, São Paulo -
SP, 05508-09, Brazil.  Richard Battye Jodrell Bank Centre for Astrophysics,
Department of Physics and Astronomy, The University of Manchester, Oxford
Road, Manchester, M13 9PL, U.K.  Francisco A. Brito Departamento de Física,
Universidade Federal da Paraíba, Caixa Postal 5008, 58051-970 João Pessoa,
Paraíba, Brazil  Unidade Acadêmica de Física, Universidade Federal de Campina
Grande, R. Aprígio Veloso, 58429-900 - Bodocongó, Campina Grande - PB, Brazil
Ian Browne Jodrell Bank Centre for Astrophysics, Department of Physics and
Astronomy, The University of Manchester, Oxford Road, Manchester, M13 9PL,
U.K.  Daniel Correia Instituto de Física, Universidade de São Paulo, R. do
Matão, 1371 - Butantã, São Paulo - SP, 05508-09, Brazil.  André A. Costa
Center for Gravitation and Cosmology, College of Physical Science and
Technology, Yangzhou University, 225009, Yangzhou, China  Jacques Delabrouille
[  Department of Astronomy, School of Physical Sciences, University of Science
and Technology of China, Hefei, Anhui 230026  Laboratoire Astroparticule et
Cosmologie (APC), CNRS/IN2P3, Université Paris Diderot, 75205 Paris Cedex 13,
France  Clive Dickinson Jodrell Bank Centre for Astrophysics, Department of
Physics and Astronomy, The University of Manchester, Oxford Road, Manchester,
M13 9PL, U.K.  Chang Feng School of Astronomy and Space Science, University of
Science and Technology of China, Hefei, 230026, China  Elisa G. M. Ferreira
Max-Planck-Institut fr Astrophysik, Karl-Schwarzschild-Str. 1, 85748 Garching,
Germany  Karin Fornazier University College London, Gower Street, London,WC1E
6BT, UK  Instituto de Física, Universidade de São Paulo, R. do Matão, 1371 -
Butantã, São Paulo - SP, 05508-09, Brazil.  Giancarlo de Gasperis INFN Sez. di
Roma 2, Via della Ricerca Scientifica, 1 I-00133 Roma, Italy  Dipartimento di
Fisica, Università degli Studi di Roma Tor Vergata, Via della Ricerca
Scientifica, 1 I-00133 Roma, Italy  Priscila Gutierrez Instituto de Física,
Universidade de São Paulo, R. do Matão, 1371 - Butantã, São Paulo - SP,
05508-09, Brazil.  Stuart Harper Jodrell Bank Centre for Astrophysics,
Department of Physics and Astronomy, The University of Manchester, Oxford
Road, Manchester, M13 9PL, U.K.  Ricardo G. Landim Technische Universität
München, Physik-Department, James-Franck-Strasse 1, 85748 Garching, Germany
Vincenzo Liccardo Jodrell Bank Centre for Astrophysics, Department of Physics
and Astronomy, The University of Manchester, Oxford Road, Manchester, M13 9PL,
U.K.  Divisão de Astrofísica, Instituto Nacional de Pesquisas Espaciais. Av.
dos Astronautas 1758, Jd. da Granja, CEP 12227-010, São José dos Campos - SP,
Brazil  Yin-Zhe Ma NAOC-UKZN Computational Astrophysics Centre (NUCAC),
University of KwaZulu-Natal, Durban, 4000, South Africa  School of Chemistry
and Physics, University of KwaZulu-Natal, Westville Campus, Private Bag
X54001, Durban 4000, South Africa  Telmo Machado Divisão de Astrofísica,
Instituto Nacional de Pesquisas Espaciais. Av. dos Astronautas 1758, Jd. da
Granja, CEP 12227-010, São José dos Campos - SP, Brazil  Bruno Maffei IAS,
Université Paris-Saclay, 91405 Orsay Cedex, France  Alessandro Marins
Instituto de Física, Universidade de São Paulo, R. do Matão, 1371 - Butantã,
São Paulo - SP, 05508-09, Brazil.  Milena M. M. Mendes Instituto de Física,
Universidade de São Paulo, R. do Matão, 1371 - Butantã, São Paulo - SP,
05508-09, Brazil.  Eduardo Mericia Divisão de Astrofísica, Instituto Nacional
de Pesquisas Espaciais. Av. dos Astronautas 1758, Jd. da Granja, CEP
12227-010, São José dos Campos - SP, Brazil  Christian Monstein ETH Zürich,
Institute for Particle Physics and Astrophysics, HIT J13.2, Wolfgang-Pauli-
Strasse 27, 8093 Zürich, Switzerland  Pablo Motta Instituto de Física,
Universidade de São Paulo, R. do Matão, 1371 - Butantã, São Paulo - SP,
05508-09, Brazil.  Camila Novaes Divisão de Astrofísica, Instituto Nacional de
Pesquisas Espaciais. Av. dos Astronautas 1758, Jd. da Granja, CEP 12227-010,
São José dos Campos - SP, Brazil  Carlos H. N. Otobone Instituto de Física,
Universidade de São Paulo, R. do Matão, 1371 - Butantã, São Paulo - SP,
05508-09, Brazil.  Michael Peel Instituto de Astrofísica de Canarias, 38200 La
Laguna, Tenerife, Canary Islands, Spain  Departamento de Astrofísica,
Universidad de La Laguna (ULL), 38206 La Laguna, Tenerife, Spain  Amílcar R.
Queiroz Unidade Acadêmica de Física, Universidade Federal de Campina Grande,
R. Aprígio Veloso, 58429-900 - Bodocongó, Campina Grande - PB, Brazil
Christopher Radcliffe Phase2 Microwave Ltd., Unit 1a, Boulton Rd, Pin Green
Ind. Est., Stevenage, SG1 4QX, UK  Mathieu Remazeilles Jodrell Bank Centre for
Astrophysics, Department of Physics and Astronomy, The University of
Manchester, Oxford Road, Manchester, M13 9PL, U.K.  Rafael M. G. Ribeiro
Instituto de Física, Universidade de São Paulo, R. do Matão, 1371 - Butantã,
São Paulo - SP, 05508-09, Brazil.  Yu Sang Center for Gravitation and
Cosmology, College of Physical Science and Technology, Yangzhou University,
225009, Yangzhou, China  Juliana F. R. Santos Instituto de Física,
Universidade de São Paulo, R. do Matão, 1371 - Butantã, São Paulo - SP,
05508-09, Brazil.  Larissa Santos Center for Gravitation and Cosmology,
College of Physical Science and Technology, Yangzhou University, 225009,
Yangzhou, China  Marcelo V. Santos Unidade Acadêmica de Física, Universidade
Federal de Campina Grande, R. Aprígio Veloso, 58429-900 - Bodocongó, Campina
Grande - PB, Brazil  Chenxi Shan Center for Gravitation and Cosmology, College
of Physical Science and Technology, Yangzhou University, 225009, Yangzhou,
China  Gustavo B. Silva Instituto de Física, Universidade de São Paulo, R. do
Matão, 1371 - Butantã, São Paulo - SP, 05508-09, Brazil.  Frederico Vieira
Divisão de Astrofísica, Instituto Nacional de Pesquisas Espaciais. Av. dos
Astronautas 1758, Jd. da Granja, CEP 12227-010, São José dos Campos - SP,
Brazil  Jordany Vieira Instituto de Física, Universidade de São Paulo, R. do
Matão, 1371 - Butantã, São Paulo - SP, 05508-09, Brazil.  Thyrso Villela
Instituto de Física, Universidade de Brasília, Brasília, DF, Brazil  Divisão
de Astrofísica, Instituto Nacional de Pesquisas Espaciais. Av. dos Astronautas
1758, Jd. da Granja, CEP 12227-010, São José dos Campos - SP, Brazil  Linfeng
Xiao School of Aeronautics and Astronautics, Shanghai Jiao Tong University,
Shanghai 200240, China  Weiqiang Yang Liao Ning Normal University, No.850
Huanghe Road Shahekou District, Dalian,Liaoning, P.R.China 116029  Jiajun
Zhang CTPU-IBS, Theory building, 4th Floor, 55, Expo-ro, Yuseong-gu, Daejeon,
Korea  Xue Zhang Center for Gravitation and Cosmology, College of Physical
Science and Technology, Yangzhou University, 225009, Yangzhou, China  Zenghao
Zhu School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai
200240, P. R. China

###### Abstract

BINGO (BAO from Integrated Neutral Gas Observations) is a unique radio
telescope designed to map the intensity of neutral hydrogen distribution at
cosmological distances, making the first detection of Baryon Acoustic
Oscillations (BAO) in the frequency band 980 MHz - 1260 MHz, corresponding to
a redshift range $0.127<z<0.449$. BAO is one of the most powerful probes of
cosmological parameters and BINGO was designed to detect the BAO signal to a
level that makes it possible to put new constraints on the equation of state
of dark energy. The telescope will be built in Paraíba, Brazil and consists of
two $\thicksim$ 40m mirrors, a feedhorn array of 28 horns, and no moving
parts, working as a drift-scan instrument. It will cover a $15^{\circ}$
declination strip centered at $\sim\delta=-15^{\circ}$, mapping $\sim 5400$
square degrees in the sky. The BINGO consortium is led by University of São
Paulo with co-leadership at National Institute for Space Research and Campina
Grande Federal University (Brazil). Telescope subsystems have already been
fabricated and tested, and the dish and structure fabrication are expected to
start in late 2020, as well as the road and terrain preparation.

21cm cosmology, radio astronomy, baryon acoustic oscillations, BAO

10]IRFU, CEA, Université Paris Saclay, 91191 Gif-sur-Yvette, France

##  1 Introduction

One of the main cosmological challenges in the 21st century is to explain the
present acceleration in the expansion of the universe, first unequivocally
inferred in 1998 by two independent groups measuring supernovae of type Ia,
one led by Perlmutter et al. (1998) and the other by Riess et al. (1998). This
led to the 2011 Nobel Prize in Physics and can be explained by postulating a
negative pressure from a new component, the so-called Dark Energy (hereafter,
DE). In combination with other observations, such as the Cosmic Microwave
Background (CMB), there is little doubt about the existence of such an
accelerated phase and the main focus of observational cosmology is now to try
to determine its detailed properties.

Among the various programs to measure those properties, Baryonic Acoustic
Oscillations (BAO) are recognised as one of the most powerful probes of the
properties of DE (e.g., Albrecht et al. (2006); Weinberg et al. (2013)). BAO
are a signature in the matter distribution from the recombination epoch and
are imprinted in the distribution of galaxies at redshifts less than that of
the epoch of reionization ($z\lesssim 0.5$). In the context of the standard
cosmological model ($\Lambda\textrm{CDM}$ model), BAO can be identified as a
small but detectable excess of galaxies with separations of order of
$150~{}\textrm{Mpc.h}^{-1}$ (Eisenstein et al. (2005); Anderson et al. (2014);
Planck Collaboration et al. (2018)). This excess is the imprint of the
acoustic oscillations generated during CMB times and its linear scale is known
from basic physics. Consequently, a measure of its angular scale can be used
to determine the distance up to a given redshift. The same oscillations
produce the familiar peaks observed in the CMB anisotropy power spectrum.

To date, BAOs have only been detected by performing large galaxy redshift
surveys in the optical waveband, where they are used as tracers of the
underlying total matter distribution. It is important they are confirmed in
other wavebands and measured over a wide range of redshifts. Since optical
surveys demand more time to detect individual galaxies and cover a relatively
small fraction of the sky, just a few percent of the local volume has been
mapped with such a type of technique. The radio band provides a complementary
observational window for studying BAO, and the natural radio tracer is the
redshifted 21cm emission line. However, the volume emissivity associated with
this line is low, meaning that detecting individual galaxies up to $z\sim 0.5$
requires a very substantial collecting area. It has been proposed that mapping
the Universe measuring the collective 21cm emission of the underlying matter
distribution, using a technique known as intensity mapping, hereafter IM (Loeb
& Wyithe, 2008; Masui et al., 2013; Switzer et al., 2013; Peterson et al.,
2006), can be many times more efficient than optical surveys (e.g., Chang et
al. (2008); Loeb & Wyithe (2008)).

The BINGO (BAO from Integrated Neutral Gas Observations) telescope is a new
instrument designed specifically for observing BAO in frequency band around
$1~{}{\rm GHz}$ and to provide a new insight into the Universe at
$0.13\lesssim z\lesssim 0.45$ with a dedicated instrument. The optical
configuration consists of a compact, two 40m diameter, static dishes with an
exceptionally wide field-of-view ($\sim 14.75^{\circ}\times 6.0^{\circ}$) and
28 feed horns in the focal plane. BAO observations will be carried through IM
HI surveys (see, e.g., Peterson et al. (2006)). BINGO’s main scientific goal
is to claim the first BAO detection in radio in this redshift range, mapping
the three dimensional distribution of HI, yielding a fundamental contribution
to the study of DE, with observations spanning several years. No detection of
BAOs in radio has been claimed so far, making BINGO an interesting and
appealing instrument in the years to come. Moreover, in view of the
observation strategy and with some adjustment in its digital backend, BINGO
will also be capable to detect transient phenomena at very short time scales
($\lesssim 1~{}{\rm ms}$), such as pulsars and Fast Radio Bursts (FRB, Lorimer
et al. (2007); Cordes & Chatterjee (2019)).

Earlier versions of the BINGO telescope concept are described in Battye et al.
(2013, 2016); Wuensche & the BINGO Collaboration (2019) and a comprehensive
analysis of foregrounds and $1/f$ noise that can limit the BINGO performance
was published by Bigot-Sazy et al. (2015). The Project Design Review that
occurred in July 2019 fixed the telescope location in the hills of “Serra do
Urubu” in the municipality of Aguiar, Paraíba, Brazil (Lon:
$38^{\circ}~{}16^{\prime}~{}4.8^{\prime\prime}$ W, Lat:
$7^{\circ}~{}2^{\prime}~{}27,6^{\prime\prime}$S). It presents a very clean
radio environment, as reported by Peel et al. (2019), which is one of the most
critical requirements for high-quality measurements.

The main institutions in the BINGO consortium are: University of São Paulo
(USP), National Institute for Space Research (INPE) and Campina Grande Federal
University (UFCG), in Brazil, University of Manchester and University College
London (United Kingdom), and Yang Zhou University (China). The BINGO project
was presented in the BRICS Astronomy Working Group 2019 and this paper
contains the status of the project as of July 2020.

##  2 Science Goals

Recent observational results have been useful in shedding new light onto
aspects of dark matter and DE, which comprise about 95% of the mass of the
Universe, and it is expected that maybe a new physical picture emerges once we
find more about the structure of the Dark Sector (Abdalla et al., 2010). BAO
studies are one of the most powerful probes of the properties of DE (see,
e.g., Albrecht et al. (2006); Weinberg et al. (2013)) and have only been
probed by optical galaxy redshift surveys (see, e.g., Eisenstein et al.
(2005); Percival et al. (2010); Tojeiro et al. (2014); Anderson et al. (2014);
Reid et al. (2015)).

Constraints on various existing DE models (see, e.g., (Wang et al., 2005,
2007; Feng et al., 2008; Micheletti et al., 2009; He et al., 2009, 2011;
Ferreira et al., 2017)) coming from BAO measurements break the degeneracies
between cosmological parameters that are present when inferring them from the
CMB observations. This potentially allows to constrain extensions of the
standard cosmological model which are usually degenerate with the Hubble
constant.

Following the first detection of BAOs using SDSS surveys Eisenstein et al.
(2005), BAOs have been measured with several new generation galaxy and quasar
redshift surveys. In particular, the recent measurements with the BOSS survey
achieve a precision in the BAO scale of a few percent in the redshift range
$z=[0,2]$ (Anderson et al., 2014; Tojeiro et al., 2014; Reid et al., 2015).
New large experiments are being designed or are already under construction to
refine BAO measurements in the optical band, such as DESI (Aghamousa et al.,
2016a, b), Euclid (Refregier et al., 2010) and WFIRST (Levi et al., 2011;
Green et al., 2011), as well as LSST for imaging (Tyson et al., 2002). Their
goal is to achieve a precision below the percent level at higher redshifts.
Nevertheless, all of them are large endeavours and the first light of any of
them is not expected before the mid-2020’s decade.

This is why it is so important that the current results are confirmed in other
wavebands and measured over a wide range of redshifts. The radio band provides
a unique and complementary observational window for the understanding of DE
via measurements of integrated redshifted 21cm hydrogen emission line (Abdalla
et al., 2010). HI redshift data allow us to construct a unique three
dimensional map of the mass distribution, with a different perspective
compared to the data obtained with optical telescopes. Indeed, HI is expected
to be one of the best tracers of the total matter (Padmanabhan et al., 2015).

The combination of BAO radio measurements with other data sets (for instance,
CMB and optical BAO) can be a powerful strategy to put more stringent
constraints in the cosmological parameters analysis. Fig. 1 shows the forecast
of the constraints from BINGO, combined with other data sets, for the time
dependent parametrisation the equation of state of the DE with $w_{0}$ and
$w_{a}$ ($1^{st}$ time derivative of $w_{0}$) for various data sets. The
constraints were computed using the Fisher matrix approach as described by
Bull et al. (2015).

The plot shows the improvement in the constraints obtained with the
combination of simulated results from BINGO and CHIME (Newburgh et al., 2014)
, compared to the current constraints given by the combination of the results
of the Planck and WMAP satellites (Planck Collaboration et al., 2014, 2016,
2018; Bennett et al., 2013), with the optical galaxy surveys (BOSS (Anderson
et al., 2014; Tojeiro et al., 2014), WiggleZ (Kazin et al., 2014) and 6dF
(Beutler et al., 2011)). For constant $w$, the measurement would lead to an
accuracy on $w$ of $\lesssim 8\%$ when including BINGO together with CHIME and
Planck.

Tight constraints can be obtained with radio data alone (BAO from CHIME and
BINGO plus Planck), providing a completely independent measurement from
optical surveys. The improvement from the red to the yellow contour is due to
having two independent measurements at different redshifts, showing the
importance of BINGO. We have made estimates of the projected errors of the DE
parameters assuming that all the other cosmological parameters are fixed.
BINGO will be competitive and complementary to optical surveys, which are
limited by different systematic errors.

Figure 1: Constraints on DE equation of state, including forecasts from CHIME
and BINGO. Note the tight error elipse obtained with combined radio data (BAO
from CHIME and BINGO + Planck).

The combination of BINGO and Planck only also place good constraints in the
values of the equation of state of DE. In figure 2 we compare the $68\%$ and
$95\%$ confidence levels constraints on the parameters of the DE equation of
state using BINGO alone and BINGO plus Planck. The constraints improve with
increasing number of redshift bins. Those figures can be compared with the
optimal case results from (Olivari et al., 2018). Our marginalised $68\%$ c.l.
constraints on the DE equation of state parameters are $2.4\%$ for $w_{0}$ and
$\sigma_{w_{a}}=0.11$. Olivari et al. obtained $4\%$ for a constant equation
of state model.

Figure 2:  $68\%$ and $95\%$ confidence level constraints on the DE equation
of state parameters using BINGO and BINGO + Planck. The results are show for
two different number of redshift bins (Costa et al., 2021).

A more detailed work discussing the forecast methods under development for the
BINGO data analysis is presented in Costa et al. (2021).

###  2.1 HI intensity mapping

Together with other experiments, IM radio surveys can become a very promising
complementary route to study BAOs. Indeed, optical experiments are required to
gather data with very high angular resolution (of the order of 1 arcsecond) to
detect individual galaxies, while the BAO scale is typically of the order of a
degree. On the other hand, radio BAO experiments have angular resolutions
which are well matched to the BAO scale and thus offer a very low cost-
effective approach. IM experiments are a more direct tracer of dark matter
being less sensitive to “peak statistics” bias. Moreover, radio experiments
can use other elements/molecules (CO, $H-{\alpha}$) besides neutral hydrogen
(Kovetz et al., 2017), and are subject to different astrophysical effects and
systematics when compared to optical surveys. The combination of their results
with those obtained from optical surveys can significantly reduce systematic
effects during cosmological parameter analysis.

BINGO will also use IM to map the three-dimensional distribution of HI, with
observations spanning a few years. Also, an experiment such as BINGO will pave
the way to the extension of the use of 21cm IM to high redshifts, opening a
new window on cosmological epochs, which cannot be probed in the optical or
near-infrared bands. Extensive simulations of the expected BAO signal and how
it can be extracted are currently in progress, with results to be published in
the near future.

At frequencies below a few GHz the future of radio astronomy will be dominated
by SKA (Square Kilometre Array) project (Maartens et al., 2015), that should
begin operating by mid-2020’s. Its design has been strongly driven to probe
the HI 21cm line from its rest frequency (1420 MHz) down to 50 MHz. The great
importance of the IM technique is highlighted by the decision to change the
baseline design of the SKA Phase 1 to enable a IM approach (Square Kilometre
Array Cosmology Science Working Group et al., 2020). Albeit SKA is designed to
work mainly in interferometric mode, the proposed IM observations are intended
to work in autocorrelation mode; i.e. using each dish separately, so that SKA
and BINGO operation modes will be somewhat similar. Each BINGO beam will be
equivalent to a single SKA dish, so that the hardware and software know-how
acquired during BINGO development will be readily available to take advantage
of the much larger signal to noise ratio achieved by SKA (Bull et al., 2015;
Abdalla et al., 2015).

##  3 The instrument

Interferometers are the most suitable instruments to perform IM in the angular
resolution of $\sim 0.5^{\circ}$, covering very large areas of the sky.
However, they also require expensive hardware and sophisticated electronics
and time-keeping systems to make the necessary correlations. A number of
approaches has been proposed to conduct IM surveys using interferometer arrays
rather than a single dish (see, for instance, the papers from van Bemmel et
al. (2012); Pober et al. (2013); Baker et al. (2011)) and a detailed
discussion about advantages and disadvantages for both was published by Bull
et al. (2015).

Single-dish telescopes with stable receivers can be a low-cost, good
efficiency approach to BAO _IM_ studies at redshifts ($z\lesssim 0.5$) Battye
et al. (2012) and BINGO belongs to this class. For comparison, we show some of
the IM experiments planned for the near future, or already in operation, in
figure3, with covered area in the sky as a function of redshift. Albeit
operating in the same redshift range of TIANLAI, FAST and SKA telescopes, we
believe BINGO can be still be a strong competitor, producing good quality data
before their relatives.

Figure 3: Comparative plot of sky and redshift coverage for different IM
experiments. CHIME, FAST, Tianlai and COMAP are in operation.

The difficulty in doing a statistically significant BAO detection is due to
the high intensity of the Galactic and extragalactic signals in the BINGO
frequency band ($980\leq\nu\leq 1260{\rm GHz}$ ) . The HI signal is typically
$\thicksim 200~{}\mu K$ whereas the foreground continuum emission from the
Galaxy is $\thicksim 1~{}K$ with spatial fluctuations $\thicksim 100~{}mK$.
Detecting signals of $\thicksim 200~{}\mu K$ with a non-cryogenic receiver
implies that every pixel in our intensity map requires an accumulated
integration time larger than 1 day over the course of the observing campaign,
which is expected to last a few years. The total integration time can be built
up by many returns to the same patch of sky but between these returns the
receiver gains need to be highly alike and achieving this stability is a major
design concern.

Fortunately, the integrated 21cm emission exhibits characteristic variations
as a function of frequency whereas the continuum emission has a very smooth
spectrum; this allows for the two signals to be separated (Chang et al., 2008;
Liu & Tegmark, 2011; Chang et al., 2010; Olivari et al., 2016). However, while
there is a clear-cut statistical signature that allows for the separation of
different components, the design of an instrument to measure the HI
contribution to the detected signal needs to be very careful. It should
contemplate a very clean and symmetric beam, with low sidelobe levels and very
good polarisation purity, to avoid systematic effects that can result in
leakage of the Galactic foreground emission, which is partially linearly
polarised and concentrated towards the Galactic plane, into the HI signal.

A declination strip of $\thicksim 15^{\circ}$, centered at
$\delta\thicksim-15^{\circ}$ aims at minimising the Galactic foreground
contamination and will be the optimal choice for the BINGO survey. The need to
clearly resolve structures of angular sizes corresponding to a linear scale of
around $150~{}Mpc$ at BINGO’s chosen redshift range implies that the required
angular resolution has to be $\thicksim 40^{\prime}$.

The general concept for the BINGO instrument is described in Battye et al.
(2013, 2016) and updated in Wuensche & the BINGO Collaboration (2019). The
current stage of the instrument construction is as follows: 1) horn prototype
is completed, tested and approved for electromagnetic and mechanical
performance; 2) front end, including polariser, transitions, OMT (“magic-
tees”) and rectangular-to-coaxial transitions are completed, tested and
approved for electromagnetic and mechanical performance; 3) the LNA and
receiver chain is completed, passed the initial testing and needs to be
improved for a better system temperature; 4) different LNA types are under
testing to improve the first stage performance; 5) optical design is nearly
completed, with the final dish parameters and focal plane arrangement
simulated and tested in the mission pipeline data reduction software. The main
parameters describing the telescope can be found in Table 1.

Table 1: BINGO telescope parameter list System temperature (K) | 70
---|---
Frequency band (MHz) | 980.00 - 1260.00
Sampling time (Hz) | 10
Instrument noise (mK, 1 second) | 23
Polarizations | 2
Redshift channels | 30
Redshift interval | 0.127 - 0.449
Focal length (m) | 63.2
Primary reflector F/D ratio | 140
Instantaneous focal plane area (sqr. deg.) | $14.75\times 6.0$
Primary mirror (parabola
Major semi-axis (m) | 25.7
Minor semi-axis (m) | 20.0
Secondary mirror (hyperbola)
Major semi-axis (m) | 18.3
Minor semi-axis (m) | 18.0
Pixel solid angle (sqr deg) | 0.504
Optics FWHM (deg @ 1 GHz) | 0.67
Survey area (square deg) | 5323
Total # horns (Phase 1) | 28

###  3.1 The optics

The final optical system consists of an off-axis dual mirror telescope
following the Dragone-Mizuguchi condition. Combined with the use of corrugated
feedhorns, similarly to CMB experiments, such configuration allows for low
sidelobes, low spillover and superb polarisation performance. The focal plane
includes 28 feed horns giving an overall field of view of $14.75^{\circ}\times
6^{\circ}$. The forward gain of each beam formed by the combination of the
feedhorn and the telescope do not vary by more than 1 dB for all the pixels
across the whole focal plane, while keeping low beam aberration and
ellipticity. The current mirror and focal plane configuration are found in
figure 4. Since the smallest operational wavelength is $23.8$cm, the surface
profile of the telescope mirrors should have an $rms$ error $\lesssim~{}15$ mm
to allow for maximum efficiency. The final optical design is described in the
work of Abdalla et al. (2021).

Figure 4: Optical design schematics as presented in the July 2019 CDR (Abdalla
et al., 2021).

Figure 5: Beam pattern response for the Stokes parameters as a function of
celestial coordinates, for the focal plane arrangement of a rectangular horn
arrangement configuration (left) and for a hexagonal horn arrangement
configuration (right). The colour scale on the right refers to intensity (dB)
(Abdalla et al., 2021). Figure 6: Combined beam response for rectangular
arrangement and hexagonal arrangement shown in figure 5 at 1100 MHz (Abdalla
et al., 2021).

Various arrangements for the horns in the focal plane were simulated in order
to achieve an optimal sky coverage with the least distortion of the outer
horns. All the optical analysis was performed with the GRASP package (TICRA -
Reflector Antenna and EM Modelling
Software)111http://https://www.ticra.com/software/grasp/. The beam patterns
shown in figure 5 correspond to the best solutions for the horn arrangement in
the focal plane. The corresponding beam response as a function of declination
for both arrangements are shown in figure 6.

The guiding principle in the design of BINGO is simplicity. All components
should be as simple as possible to minimize costs. Moreover, since there will
be no moving parts, design, operation and instrument modelling will also be
simpler than doing the same tasks for a conventional telescope. Another key
advantage of a simple design is that it is being built in a relatively small
amount of time, allowing for results within a competitive science time window.

###  3.2 Receiver

Each receiver chain contains a correlation system, as shown in fig. 7,
operating with room temperature amplifiers. The receiver is expected to
operate at temperature $T_{sys}\thicksim 70$ K in the frequency interval
$980-1260$ MHz, at ambient temperature. A number of tests were performed at
INPE’s Division of Astrophysics, using two different low noise amplifiers
(LNA). We tested a radiometer chain, with one LNA, one first stage filter and
second stage LNAs. The testing was conducted in the laboratory, with no
specific shielding in the amplifier structure. Table 2 shows the temperature
for each component of the BINGO radio telescope. Thus, the system temperature
is given by $T_{\rm sys}=69.9$ K. To estimate the sensitivity of the BINGO
using this simple radiometer, two ratios are used, one for the minimum
temperature variation that can be detected by the radiometer and another for
the flow density that such temperature represents:

$\Delta T_{\rm min}=\frac{T_{\rm sys}}{\sqrt{B\tau}}$ (1)

$S(\Delta T_{\rm min})=\frac{2k}{A_{\rm eff}}\Delta T_{\rm min}.$ (2)

In eq. (1), B is the frequency operating bandwidth, $\tau$ the integration
time, $k$ is the Boltzmann constant and $A_{\rm eff}$ the effective area of
the radio telescope. In this work, the bandwidth is $2.8\times 10^{8}$ Hz and
the integration time is 1 s. The effective area used considers that 70 % of
the total area of the primary mirror receives electromagnetic waves. For
BINGO, this area is $1134~{}{\rm m}^{2}$. The minimum temperature obtained in
this case is $\Delta T_{\rm min}=23$ mK and the corresponding flux, from eq.
(2) is $S(\Delta T_{min})=0.01$ Jy. After one year of observations, BINGO
should achieve a $40^{\prime}$ pixel noise of $84~{}\mu$K in a 9.33 MHz
frequency channel, with the parameters described in Table 1. A more detailed
discussion of the receiver tests can be found in the M.Sc. dissertation of
Vieira (2020).

Figure 7: Block diagram of the main components of the BINGO correlation receiver. The correlation is achieved by means of the hybrids, which in our case will be waveguide magic tees. Table 2: Temperature contributions of different components Component | T (K)
---|---
CMB | 2.7
Galactic/Extragalactic Signals |  $\sim$ 5,0
Ground spillover | 2.0
Atmosphere | 4.5
Parabolic Mirror | 1.5
Hyperbolic Mirror | 1.5
Horn (measured) | 6.8
Polarizer (measured) | 9.1
Magic tee (measured) | 2.8
Rectangular-to-coax waveguide (measured) | 9.1
LNA (phase matched) | 24.9
Total | $\sim 69.9$

###  3.3 Feedhorns and polariser

BINGO will use specially designed conical corrugated horns to illuminate the
secondary mirror of the telescope. These need to be corrugated in order to
provide the required low sidelobes coupled with very good polarisation
performance. Because of the large focal ratio needed to provide the required
wide field of view, the horns have $\thicksim 1.9$m diameter and $\thicksim
4.3$m length. The electromagnetic design of such horns is well understood and
the first prototype was successfuly developed and tested in the Laboratory of
Integration and Tests at INPE. Insertion and return loss, as well as the beam
profile, were measured for the frequency interval $900-1300$ MHz, in $25$ MHz
steps, for vertical and horizontal polarisations. A summary of the main
properties of the horn prototype is found in Tables 3 and 4 and the main
results of the construction and testing of the feed horns and polariser are
discussed in Wuensche et al. (2020).

Table 3: Feedhorn beam measurements. Left: horizontal polarisation. Right: vertical polarisation. Minimum rejection was not measured at all frequencies Freq | Directivity | FWHM | Min. | Front to back | Directivity | FWHM | Min. | Front to back
---|---|---|---|---|---|---|---|---
|  |  | rejection | difference |  |  | rejection | difference
(MHz) | (dB) | (deg) | (dB) | (dB) | (dB) | (deg) | (dB) | (dB)
0950 | 22.4 | 14.6 | – | 47.0 | 22.7 | 13.4 | – | 56.2
1000 | 23.1 | 13.8 | 22.5 | 51.7 | 23.0 | 13.5 | 20.5 | 64.7
1050 | 23.5 | 13.1 | 28.2 | 54.2 | 23.5 | 12.9 | 35.9 | 57.2
1100 | 23.9 | 12.4 | 25.6 | 58.2 | 23.9 | 12.4 | 30.5 | 56.2
1150 | 24.3 | 11.8 | 20.0 | 72.8 | 24.4 | 11.7 | 19.7 | 55.4
1200 | 24.7 | 11.1 | 26.4 | 57.2 | 24.6 | 11.1 | 26.0 | 63.6
1250 | 25.2 | 10.3 | – | 56.1 | 24.9 | 10.6 | – | 57.1
Table 4: Horn measurements - summary | Insertion loss | Return loss
---|---|---
| measured | predicted | measured | predicted
Horn |  $-$0.140 | – | 26.50 |  28.00
Polariser |  $-$0.120 |  $-$0.08 | 24.06 | 24.44
WG5 to coax |  $-$0.075 | – | $>30$ | –

###  3.4 Mission simulations

The assessment of the instrument performance and the reliability with which
the cosmological signal can be extracted from the observed data is done
through a _Pipeline_ developed inside the collaboration and used in previous
works (Bigot-Sazy et al., 2015; Harper & Dickinson, 2018). We briefly describe
the data processing and component separation algorithm used to obtain the
BINGO maps we use for our analysis. Details of the algorithm and data
processing can be found in the works of Liccardo et al. (2021) and Fornazier
et al. (2021).

The _Pipeline_ takes input maps of different emission mechanisms, produced by
theoretical models or by observations, as well as various instrument
characteristics (such as such as the number of horns and their positioning in
the focal plane, receiver system temperature, focal length, etc.) and
contamination from the environment. _Pipeline_ outputs are time series which
can be turned into maps that simulate the signal as measured by the instrument
during a defined mission duration.

To detect BAOs in the Hi signal we will need to remove the contributions from
much brighter emissions coming from our Galaxy and extragalactic sources, the
most relevant emissions at $\sim 1~{}{\rm GHz}$ being a combination of
extragalactic point sources and diffuse Galactic synchrotron emission.

The _Pipeline_ operation makes use of a mission simulator, which processes all
the input information described above, producing a time series (TOD), used to
produce the maps at each frequency determined by the band and the number of
channels. Currently, the set of generated maps is processed by a component
separation algorithm to recover the cosmological Hi signal. Future plans are
that this procedure should be integrated in the _Pipeline_ body.

We are currently simulating the instrument performance and testing component
separation methods, such as GNILC, developed for the Planck mission, following
the work from Remazeilles et al. (2011) and Olivari et al. (2016).

A detailed description of the instrument performance and capabilities, as well
as some discussion regarding the separation of components can be found in the
work of Liccardo et al. (2021). A deeper analysis of the component separation
process, especially regarding GNILC, and the construction of a non-gaussianity
estimator module is presented in the work of Fornazier et al. (2021).

##  4 Current status

BINGO is currently funded, with about 70% of the funding already secured,
mostly coming from Brazilian funding agencies (FAPESP, FINEP, MCTIC and CNPq)
and a smaller amount coming from China, through purchase of components and
electronic items for the instrument.

There was a major project review in July 2019, with a committee of five
scientists, including two radio astronomers and and one Hi IM specialist . The
result of this review was very positive, with fourteen recommendations in
total. Out of these fourteen, four of them require the close attention of the
collaboration working groups and started being addressed immediately after the
conclusion of the review. Namely, these four recommendations are: 1) the
definition of the final receiver configuration to be used; 2) the final
optical design; 3) the recommendation of adopting a full polarisation analysis
(or a good motivation to avoid it); and 4) the preparation of an “end-to-end”
pipeline to simulate the mission behaviour well in advance of the beginning of
operations. We have solved items 1) and 3) and item 2) is being completed. We
are presently working intensely in item 4).

##  5 Final remarks

BINGO is a radio telescope designed to claim the first BAO detection, in the
redshift interval $0.13\leq z\leq 0.45$, at radio wavelengths around $1~{}{\rm
GHz}$ and is currently being constructed in Paraíba, Northeastern Brazil,
counting on the local support and expertise of Campina Grande Federal
University. The measured signal is produced by the redshifted 21cm HI line,
detected through an IM survey covering $\sim~{}13\%$ of the sky and will probe
the same redshift interval as the most important optical BAO surveys, but with
different systematics. BINGO will also provide Galactic foreground maps in the
frequency range 980 - 1260 MHz, with its sky coverage overlapping with a
number of radio surveys. It will likely be the only radio instrument operating
in its redshift range for a few years.

BINGO will provide high quality data, covering a wide range of scientific
areas from cosmology to Galactic science. In view of its observation strategy
and with some adjustment in the digital backend, BINGO will also be capable to
detect transient phenomena, such as pulsars and FRB.

## Author contributions

Carlos A. Wuensche and Elcio Abdalla are, respectively, the PI and the general
coordinatior of the project. Filipe Abdalla, Luciano Barosi, Bin Wang, Amilcar
Queiróz, Francisco Brito and Thyrso Villela belong to the management team, who
are working in various aspects of logistics and construction to make BINGO a
reality. Richard Battye, Clive Dickinson and Stuart Harper worked heavily in
the initial stages of BINGO. Mike Peel, Ian Browne and Christian Monstein have
a strong participation in the receiver construction, which is led by Carlos A.
Wuensche; Mike Peel is the first author of the site selection paper and Telmo
Machado has worked in many aspects of the receiver construction. Christopher
Radcliffe produced the filters and the simulations of the BINGO horns and
front end. Jacques Delllabrouile, Mathieu Remazeilles, Larissa Santos, Karin
Fornazier, Eduardo Mericia, Giancarlo de Gasperis and Vincenzo Liccardo are
the contributors to the mission simulation and component separation aspects of
the data analysis. André Costa, Ricardo Landim, Elisa Ferreira, Jianjun Zhang,
Camila Novaes and Yin-Zhe Ma are the main developers of the cosmological
forecast module in the data analysis effort. The optical design was led by
Filipe Abdalla and Bruno Maffei, with the heavy involvement of Alessandro
Marins and Pablo Motta, and the participation of João A. M. Barreto, Daniel
Correia, Priscila Gutierrez, Milena M. M. Mendes, Juliana F. R. Santos, Carlos
H. Otobone, Rafael M. G. Ribeiro, Karin Fornazier and Gustavo B. Silva, who
ran the simulations of the optical system and produced the fits for the horn
positions. The Fast Radio Bursts and the pulsar working group is led by
Ricardo Landim, with the strong participation of Marcelo V. dos Santos,
Frederico Vieira, Jordany Vieira, Chenxi Shan, Yu Sang and Xue Zhang. Rui An,
Chang. Feng, Linfeng Xiao, Weiqiang Yang and Zenghao Zhu are working on
different aspects of the cosmological parameter estimation effort.

C.A.W. acknowledges the CNPq grant 313597/2014-6 and FAPESP Thematic project
grant 2014/07885-0. E.A. acknowledges FAPESP by the grant 2014/07885-0 and
FAPESP and CNPq for various other grants. R.G.L. acknowledges CAPES (process
88881.162206/2017-01) and Alexander von Humboldt Foundation for the financial
support. M.P. acknowledges funding from a FAPESP Young Investigator
fellowship, grant 2015/19936-1. F.A.B acknowledges a CNPq/PRONEX/FAPESQ-PB
(Grant no. 165/2018). C.A.W. and F.B.A. acknowledge the UKRI-FAPESP Visiting
Professor Grant 2019/05687-0. The authors declare they have no conflicts of
interest regarding the contents of this paper.

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