Understanding Political Communication and Political Communicators on Twitch

Sangyeon Kim1,2*

1 Observatory on Social Media, Indiana University, Bloomington, IN, USA

2 Luddy School of Informatics, Computing, and Engineering, Indiana University,
Bloomington, IN, USA

*<EMAIL_ADDRESS>
## Abstract

As new technologies rapidly reshape patterns of political communication,
platforms like Twitch are transforming how people consume political
information. This entertainment-oriented live streaming platform allows us to
observe the impact of technologies such as “live-streaming” and “streaming-
chat” on political communication. Despite its entertainment focus, Twitch
hosts a variety of political actors, including politicians and pundits. This
study explores Twitch politics by addressing three main questions: 1) Who are
the political Twitch streamers? 2) What content is covered in political
streams? 3) How do audiences of political streams interact with each other? To
identify political streamers, I leveraged the Twitch API and supervised
machine-learning techniques, identifying 574 political streamers. I used topic
modeling to analyze the content of political streams, revealing seven broad
categories of political topics and a unique pattern of communication involving
context-specific “emotes.” Additionally, I created user-reference networks to
examine interaction patterns, finding that a small number of users dominate
the communication network. This research contributes to our understanding of
how new social media technologies influence political communication,
particularly among younger audiences.

## Introduction

As new technologies in the social media environment are rapidly reshaping
patterns of political communication [1], how people consume political
information via social media changes dramatically. Twitch, the entertainment-
oriented live streaming platform, provides an environment where we can observe
the role of new technologies like “live-streaming” and “streaming-chat” in
shaping patterns of political communication. Contrary to popular belief,
various political actors ranging from politicians to political pundits use the
platform politically. Alexandria Ocasio-Cortez, an American politician serving
as the U.S. Representative for New York’s 14th congressional district since
2019, has live-streamed on Twitch several times since 2020.

The popularity of Twitch streamer Hasanabi’s live streaming of the 2020 US
presidential election also garnered attention from journalists [2]. The
success of individual broadcasters like Hasanabi compared to traditional news
media outlets like Fox News raises questions about why people choose to
consume news content provided by individuals on platforms like Twitch.

Besides, the unique technological features of Twitch, including real-time
interaction, video and audio focus, and streaming chat, establish a distinct
relationship between content creators and audiences, necessitating political
communication research on the platform. This difference could affect the
patterns of political communication on the platform, as content creators on
Twitch would have more credibility and power due to their ability to interact
with their audience in real-time [3, 4, 5]. The active usage of the streaming
chat function grants more credibility by enabling reciprocal interaction among
content providers and audiences. As they could communicate with the one who
produces political messages, the audience would regard the political streamers
as more relatable and accountable compared to political figures on other
social media platforms [6]. At the same time, Twitch streamers rely heavily on
their audience for financial revenue and content creation, more so than other
social media platforms. The lack of active reactions from viewers during a
political stream on Twitch can make it less appealing [7]. Overall, the
platform’s real-time interactive nature, video and audio orientation, and
streaming chat shape the environment of political communication by
conditioning the credibility of political messages and granting audiences
considerable agency over content production. By studying Twitch, we can
understand better how these technological changes in social media can affect
political communication in new social media.

The rise in political activities on the platform also calls for more
systematic research on Twitch politics. Despite Twitch being an entertainment-
oriented platform, political activities are increasing, with streamers
speaking up on political issues such as Black Lives Matter [8, 9] and Capitol
riots [10]. Streamers who devote most of their broadcasting time to political
issues are also present on Twitch. Another interesting feature is the
composition of the users. As the most of platforms’ content is mainly video-
game related, most of the users in the platform tend to be younger compared to
other major social media platforms, such as Twitter and Facebook. While more
than 70% of users fall into the 16-34 age group, there is still some portion
of the older population [11]. While 17% of users are in the 35-44 age group,
10% of users are in the age group above 45. As generational dynamics have been
salient in recent American politics and other countries, such as South Korea
or Japan, investigation of political communication inside the social media
platforms where the composition of users is largely skewed toward younger
generations can expand our understanding of generational politics and
political communication in general.

To study the patterns of political communication in Twitch which has been
understudied despite its theoretical and practical significance, I aim to
answer three questions on Twitch politics in this paper by using various
computational methods.

  1. 1.

Who are political Twitch streamers? As Twitch is an extremely entertainment-
oriented platform, the identification of political actors is essential to
study Twitch politics. I found 574 political streamers using Twitch API and
supervised machine learning techniques.

  2. 2.

What contents are covered in the political streams? After the identification
of political streamers, what kinds of political content are covered in the
streams should be studied as we do not know what kind of political content is
present in the platform and how political streamers cover it. By collecting
chat posts of political streamers and fitting topic models, I found political
topics covered in the platform can be categorized into seven broad categories
and a Twitch-specific pattern of political communication — the usage of
context-specific “emotes (Twitch version of emoji)”.

  3. 3.

How do the political streamers and their audiences interact with each other?
As a live streaming platform with a very easy-to-use streaming chat function,
Twitch facilitates interaction among streamers and audiences. The patterns of
political communication among Twitch users, including both streamers and
audiences, cannot be properly understood without properly capturing
communication networks. I create reference networks of audiences of political
streams to observe how they interact with one another and found most of the
networks have a structure of opinion leadership.

In the next section, I would introduce Twitch since the platform itself can be
unfamiliar to some readers. Then, I describe why should we study Twitch
politics. Next, I introduce why political actors choose Twitch as their means
to deliver political messages with an emphasis on streaming chat. After that,
I introduce three questions to understand Twitch politics and suggest the
answers to those questions step by step.

##  1 What is Twitch?

Twitch is about a ten-year-old platform with 140 million unique worldwide
monthly visitors and 41.5 million unique US monthly visitors in 2021 [11]. The
popularity of the platform looks more impressive when we compare it with
traditional media: its monthly viewership in 2018 are comparable to those of
some traditional cable TV networks in the US [12].

Fig 1: Screenshots of Twitch stream (Captured on 22/03/20)

Figure 1 shows the screenshots of Twitch streams. The upper image shows a
screenshot of the typical gaming streaming of a Twitch streamer. He uses a
microphone and webcam to show their appearance and voice while streaming their
gaming screen, with a chat on the side for viewers to post messages. They
sometimes watch videos or read texts with their audience and discuss the
material together, as the lower image shows.

Several scholars from multiple disciplines have investigated Twitch from
different angles ranging from relatively more macro-level approaches [13, 14]
to studies that focus on technology-oriented traits [15, 16] or Twitch
affordances [17, 18, 19], such as streaming chats [20, 21] and monetization
[22]. Macro-level studies on Twitch as a platform economy point out that the
flourishing of the platform has been possible because of both demand and
supply for streaming [13, 14].

Other scholars examine the platform more in detail. Some focus on the Twitch
technologies themselves [16] and its influence on user network [15], while
others address the importance of social affordances of Twitch [18, 19] arguing
the social structure surrounding those technologies also matters. Sjöblom et
al. (2019) thoroughly examines Twitch’s affordances while stressing the
important role of webcams and audio as they are playing a huge role to
establish bonds between streamers and their audiences. Similarly, Jackson
(2020) also addresses the power of the synchronous nature that strengthens the
perception of intimacy toward streamers, which is the root of their digital
persona. Out of various Twitch affordances, the streaming chat function has
received the most scholarly attention [17, 20, 21]. Streaming chat is
essential for audience engagement in live streaming and allows viewers to
communicate with the streamer and other participants in the same stream,
creating a community that can act as a virtual third place for Twitch users.
Affordances related to monetization have been seriously considered by scholars
as well [22, 19] as they are the basic building block that buttresses the
Twitch platform economy by providing monetary incentives to pursue streaming
as a professional career [13, 14].

The political dynamics on Twitch have been studied with a focus on gender
politics [23, 24, 25, 26, 27] and the role of “emotes” in live streaming
political events [28]. Research has highlighted the presence of misogynistic
elements within the video game live streaming environment, influenced by
policies on sexual content [25] and community guidelines regarding the
appearance of female streamers [23, 27]. These factors contribute to the
derogation of female gaming streamers [24] and impose additional demands for
emotional labor related to affection [23, 26]. Furthermore, the use of
“emotes” in the context of political events has been examined, revealing their
use as a tool for distracting other viewers [28].

Building upon the existing literature, this study aims to comprehensively
identify political actors on Twitch and analyze their behavior, particularly
focusing on the dynamics within streaming chats. By doing so, it seeks to
provide a deeper understanding of the political landscape on Twitch,
highlighting its significance and the implications of these interactions,
which will be further explored in the following section.

##  2 Why Twitch?

Why should social scientists care about Twitch, a platform primarily known for
its entertainment orientation? While there has been some research on Twitch
politics, its political dimensions deserve further exploration. In this
section, I will introduce a few episodes that underscore the importance of
studying Twitch politics. The first and most famous episode is a series of
streams by Alexandria Ocasio-Cortez, also known as AOC. Alexandria Ocasio-
Cortez’s series of gaming streams on Twitch in October 2020, which aimed to
get out the vote for the incoming presidential election, was a huge success,
with peak viewership of over 400,000 viewers [29]. A later gaming stream with
Canadian MP Jagmeet Singh and other Twitch streamers raised $200,000 for
charity [30, 31]. Meanwhile, in her latest stream on Twitch, Alexandria
Ocasio-Cortez discussed a political issue - the GameStop stock and Robinhood
app issue - with guests including Alexis Goldstein, Alexis Ohanian, and
TheStockGuy. They expressed their opinions on the issue and the financial
system in general, with AOC advocating for systematic financial reform in the
US. The stream was successful, with around 200,000 peak viewers.

Other than politicians, there are other political actors in Twitch: Twitch
streamers who stream political content. The best example of a renowned
political streamer would be “HasanAbi”. His US 2020 presidential election
marathon stream on November 3rd, 2020 for 16 hours had 225,000 viewers at its
peak [2]. Viewership of his election day Twitch stream was even comparable to
the major media outlets when we use “Total Hours Watched” as a criterion to
compare what media outlets people have chosen on election day: the stream’s
portion (4.9%) is almost similar to that of Fox News (6.5%) [32]. The huge
success of the election day stream by HasanAbi invited the attention of
various news media outlets on the potential of Twitch as a political conduit,
along with the huge success of AOC’s few streams. As he clearly states in the
interview with New York Times, his intention of the Twitch stream has been
mainly political - he wanted some platform to congregate people every day and
deliver his political opinion [2]. Even entertainment-oriented streamers
sometimes speak up about political issues, with a notable example being the
Black Lives Matter protests in mid-2020. Some streamers expressed their
opinions during live streams or produced videos related to the issue, while
others held fundraising for Black Lives Matter [8]. Twitch politics is not
confined to political streamers or viewers, but a broader pool of users and
streamers on the platform.

Theoretically, studying Twitch politics grants a novel opportunity to
understand how new technologies in the social media environment affect the
patterns of political communication. Twitch, an entertainment-focused live
streaming platform, offers an environment for us to study how emerging
technologies such as “live-streaming” and “streaming-chat” influence the
dynamics of political communication.

##  3 What makes political content creators choose it?: The importance of
streaming chat function and systematic dependence on audiences

People who want to create political content on a streaming platform may choose
Twitch because it provides an easy environment to start streaming. Creating
video content for YouTube can be efficient compared to producing text content
[4], and live streaming on Twitch is even more efficient because the time
spent for streaming is the same as the length of the show. The user-friendly
interface of Twitch and the availability of the app “Twitch Studio” make it
easy for anyone to start streaming using their PC or mobile phone. The only
requirements are an electronic device to run the program and objects to show
viewers, primarily the streamer themselves.

As the previous literature thoroughly examines, the existence of various ways
to pursue financial revenue also makes Twitch more attractive [22, 19]. Twitch
offers direct revenue streams such as subscriptions, viewer donations through
Twitch currency called Bits, and advertisement revenue. Streamers can also
earn money through external sources such as sponsored product placement and
third-party donation platforms. Although not all streamers can earn a stable
income, the potential for financial rewards is a factor that attracts users to
the platform. Hence, Twitch provides an ideal platform for political content
creators to brand themselves as micro-celebrities. By using webcams and
microphones [19] to create a personal connection with their audience, they can
establish credibility [3] and foster a sense of community through parasocial
relationships [33]. This connection can be further strengthened by including
links to other social media platforms in their profiles [19], enhancing their
overall engagement with the audience. The chat function on Twitch enables the
communication between streamers and viewers, creating a space for mass
communication that was not possible in traditional media outlets [34]. Viewers
can instantly react to what the streamer says or does by using Twitch emotes
or normal languages, which helps create a relatable and accountable
relationship between the streamer and the audience [6].

Twitch’s real-time chat function provides an attractive feedback loop for
political content creators and audiences to have a discussion on political
topics in real-time, which can be very attractive to political content
creators. While platforms like Twitter and Facebook allow for discussion on
political topics, there is usually a time lag between users’ responses.
However, Twitch’s chat function allows political content creators to express
their opinions in real-time while engaging with their audience, giving the
impression of a live discussion. This engagement can attract potential viewers
interested in expressing their opinions on contentious political topics.
Overall, the effective streaming chat function which enables political
streamers to have a real-time discussion with their audiences can be the most
important feature of Twitch politics. Exploring the role of streaming chat on
political communication in the social media context can have some implications
for the political communication literature as we little know how it affects
inter-user communication patterns.

The other important trait of Twitch is that the creation of the content is
also dependent on viewers at the same time. Not to mention the audience is a
source of revenue that streamers are making [22], their content creation
itself is also inherently dependent on viewers and their chat posts because of
the real-time nature of live streaming [7]. Viewers’ reactions during live
streaming are crucial for political content creators, as their content tends
to be talk show-oriented and requires smooth communication with viewers
through streaming chat. Without active participation from viewers, political
streaming cannot be successful and will only be a manifestation of the
creator’s political opinion. The audience users of Twitch have powerful agency
and their participation is essential for the success of streamers. This
highlights the need to study the audiences of political streams and their
interactions with each other, not just the political streamers.

##  4 Three Questions: Who are they, What do they broadcast and How do they
interact?

In this paper, I answer three questions on Twitch politics. First, who are the
political streamers on the platform? To study Twitch politics systematically,
it is imperative to identify political streamers first from the population of
streamers, who are mostly non-political. I have retrieved extensive lists of
streamers who may stream political content and their information using Twitch
API. Based on the information, I have leveraged supervised machine-learning
techniques to find political streamers from the retrieved lists.

Second, what do they broadcast during their streaming? It is important to
conduct a study on the types of political content that are discussed in
streams, since we are unaware of the political content that exists on the
platform and how it is addressed by political streamers. Are the topics that
are widely covered by traditional media also get covered by streamers? Or, do
they shed a light on other topics that are not fully covered by other media
outlets? Observation of their topic choices can help us to comprehend the
basic patterns of Twitch politics. I will answer the question by conducting
various text analyses on the originally collected streaming chat post data.

Third, how do political actors, both political streamers and their audiences,
interact with one another? In order to fully comprehend the political
communication behaviors of Twitch users, which includes both streamers and
their audiences, it is necessary to accurately capture the communication
networks. I would focus on inter-audience communication in a stream that is
done by mentioning others’ usernames. How do Twitch users in political chat
rooms behave in terms of communicating with each other via streaming chat? By
constructing and analyzing reference networks of each political stream, I will
study political communication networks in Twitch political streams.

##  5 Question 1: Who are the political streamers?

In this section, I find political streamers out of extensive lists of
streamers that are retrieved via Twitch API using the supervised machine
learning method. In the first subsection, I briefly introduce Twitch API and
how I have utilized it to get extensive lists of streamers. Then, I introduce
the coding scheme to classify political streamers and the classified list of
political streamers.

###  5.1 Using Twitch API to retrieve extensive streamer lists

I have collected a week of streaming records from 21-08-31 00:25 EST to
21-09-08 16:44 EST of Twitch broadcasting with the three game-names, “Just
Chatting”, “Talk Shows and Podcasts”, and “Politics”, which are identified
through the process fully described in S1, using Twitch API. The streaming
records contain various information including user name, user id, streaming
title, etc. I identified 53,550 unique ids for “Just Chatting”, 5,853 for
“Talk Shows and Podcasts” and 324 for “Politics”. As I have also collected
Twitch profiles of those streamers as well, I merged broadcasting titles of
streamings for a week and text information in their streaming profiles to
create text data of all streamers.

###  5.2 Finding political streamers using supervised machine learning
techniques

I have used a supervised machine-learning technique to identify political
streamers from the user data I have collected. The political streamers are
defined by the following coding rules refer:

  1. 1.

If a streamer has broadcasted political content at least once (i.e. “Texas
Abortion Law is a shame”), I coded her as a political streamer.

  2. 2.

If a streamer profile shows that she broadcasts about or is at least
interested in politics (i.e. “I sometimes talk about politics”), I coded her
as a political streamer.

  3. 3.

If a streamer explicitly identifies their political interest or partisanship
on her profile (i.e. “I am Leftist”, “This is conservative podcast”), I coded
her as a political streamer.

  4. 4.

If a broadcasting title or streamer profile contains a representative term or
hashtags of specific political movements (i.e. “#BLM”, “#FreePalestine”), I
coded her as a political streamer.

Fig 2: Venn Diagram of Twitch Streamer Data

As I already know that there would not be as many political streamers in the
list of “Just Chatting” compared to “Talk Shows and Podcasts” and “Politics”
as described in S1, I adopt the following strategy to produce training sets
with enough target values, political streamers, to ensure the model gets
trained properly. First, I have hand-coded all components from the “Talk Shows
and Podcasts” and “Politics” sets in Figure 2. Then, I extracted the top 30
words from the combined text data of political streamers found in the first
step and filtered out nonessential words, such as articles and be-verbs, and
other common words that are widely believed to be frequently used by most
Twitch streamers (i.e. stream, live, etc.). I have used these keywords as a
filter to sort out observations that are more likely to be coded as political
from “Just Chatting” data. The process gives the “Sampled” set with 8,037
streamers that is right inside of the “Just Chatting” set in Figure 2. I hand-
coded all of these streamers and identified 225 political accounts. The whole
process gives me a total of 14,250 hand-coded train data with 550 political
streamers. By using the labeled data, I fitted the supervised machine learning
classifier and was able to identify 574 political streamers in total. Details
about the machine learning model training process are described in S2.

##  6 Question 2: What do political streamers broadcast?

###  6.1 Collecting Chat Posts from political streamers’ live streaming using
’chat bots’ - Descriptive Figures

As a first step to studying political discussion of identified political
streamers, I have used chatbots that get connected to each stream and exist in
the channel until it gets offline [17] to collect chat posts from streams
provided by political streamers I have identified. The bots allowed me to
download all chat posts in the stream’s associated IRC (Internet Relay Chat)
with various information, ranging from the text of chat posts to the user-name
of the sender [17]. Using the pipeline, I have collected chat posts of
political streamers from 2021-12-11 to 2022-03-25.

Due to the time lag between the identification process of political streamers
and actual chat post data collection, there has been some loss in the number
of political streamers. I was able to collect chat posts of 478 political
streamers out of a total of 574, which means the survival rate is above 83%.
The reasons for the loss can be diverse. Some streamers might have quit
streaming at all, or some might just want to cease it for a while.

Fig 3: Frequency of streamings by political streamers

Figure 3 illustrates the daily frequency of political streamers starting their
streams during the data collection period, which spanned from December 11,
2021, to March 25, 2022. Since this period covers 105 days, the maximum value
in the plot is naturally 105. The distribution is right-skewed, indicating
that while some streamers were very active, others were less so. Specifically,
342 political streamers streamed on more than 10 days, and 228 of them
streamed on more than 30 days over the three-month period. This suggests that
most of the data captures regular interactions between viewers and streamers,
although some streamers broadcasted infrequently.

Fig 4: Number of chat posts. The x-axis is log transformed while labels show
the raw values.

Figure 4 shows the number of chat posts on each streamer’s channel, both in
total and on a daily basis. The logged scales of both histograms approximate a
normal distribution, with a small number of extreme values on both ends. The
total number of chat posts collected from all channels is 33,649,628. The
channel with the most chat posts has 16,945,559, while another channel has
only 2 posts from a single viewer. The mean and median values are 70,396 and
2,430, respectively. On a daily basis, where the number of chat posts per
channel is divided by the number of streaming days, the mean and median values
are 2,224 and 339, respectively. This suggests that, for a political channel
in the middle of the distribution, we can expect at least 350 chat posts per
stream. This indicates that most political channels receive some level of
audience interaction, reflecting feedback loops in the political streams.

Fig 5: Number of unique users in a channel.The x-axis is log transformed while
labels show the raw values.

Figure 5 illustrates the distribution of unique usernames that have posted at
least one chat post on a channel. The histogram, plotted on a logged scale,
reveals a slightly right-skewed pattern. The most popular channel boasts
267,206 unique users, while the least popular has only one unique user. In
total, there are 646,073 unique users across all channels, with mean and
median values of 1,351 and 70, respectively. The higher mean compared to the
median is largely influenced by extreme values on the right-hand side of the
distribution. Therefore, most of channels are likely to have around 70 unique
users who have posted at least one chat post during its streaming. While a
smaller audience size can foster active political discussions among users and
streamers, establishing parasocial relationships [33] and gaining micro-
celebrity status [3] may be challenging due to their dependence on popularity.

Fig 6: Frequency of chat posts

Figure 6 shows the frequency of chat posts at a user level. The mean chat
frequency of all users in a channel displays a nearly normal distribution,
whereas the maximum chat frequency has a weak left-skew. The mean and median
values for the mean chat frequency are 45.3 and 28.5, respectively, while
those for the maximum chat frequency are 2,475 and 377. These patterns suggest
that most users are participating in the chat repeatedly. However, the
significantly high number of chat posts from a single user suggests the
presence of bots, which are commonly used in Twitch streams.

###  6.2 What is discussed in political Twitch streams?: Topic models of the
chat post data

In this section, I will discuss the content of political streams by conducting
topic modeling on chat post data. Topic modeling, a tool widely used in both
social science and computer science [35, 36], helps us observe general trends
in the text corpus by examining broad categories among chat posts. Through
topic modeling, we can identify topics, which are sets of words that
frequently occur together within documents [36].

Due to the high computational cost, I sampled 5% of chat posts from each
political stream. The number of chat posts collected varies significantly by
stream because of differences in audience size and broadcasting frequency.
Therefore, I sampled 5% of chat posts from each channel instead of sampling at
an aggregate level. The total number of sampled chat posts is 1,682,739. I
identified a topic as political if one or more politically relevant keywords
were among the top 15 keywords within a topic. Based on this criterion, I
found 45 political topics out of a total of 150 topics. This means that 30% of
the topics identified through the analysis are political. A comprehensive list
of political topics is provided in S3.

Table 1: Topic Proportions. Proportion for political chat posts and all chat posts are calculated by dividing the number of topics with the number of political topics, 45, and the total number of topics, 150. Category | Number of topics | Proportion (Political) | Proportion (Total)
---|---|---|---
International issues | 11 | 25% | 7%
US politics | 10 | 21% | 6%
Identity politics | 9 | 21% | 6%
Ideological debate | 7 | 15% | 4%
Politics in general | 4 | 9% | 3%
Public health and politics | 2 | 4% | 1%
Environmental issues | 2 | 4% | 1%

I have categorized political topics into seven categories based on the
political keywords in those topics: 1) International issues, 2) US politics,
3) Identity politics, 4) Ideological debate, 5) Politics in general, 6) Public
health and politics, and 7) Environmental issues. Table 1 shows the number of
topics in each category and the proportion each category represents in the
corpus of political chat posts and all chat posts.

Fig 7: Topic Ranking: International issues

The most frequently appearing political topics in Twitch streams are
international issues. These topics also occupy the largest proportion in both
the political chat posts (25%) and all chat posts (7%). Figure 7 provides more
details about the topics on international issues. The prevalence of
international issues on Twitch is largely due to the specific incident that
occurred during the data collection period: the Ukraine invasion by Russia.
Six topics are directly related to the war or Russia. Other topics address
international disputes involving NATO-related countries (“International
disputes”) and the China-Taiwan conflict (“China and Taiwan”).

Fig 8: Topic Ranking: The US politics

As Figure 8 shows, topics related to US politics also appear frequently: the
number of topics is 10 and they account for 21% of political chat posts and 6%
of all chat posts. As Twitch is the most popular in the United States, this
finding is not that surprising. There is a topic about Trump (“Trump and
Gender”) and Biden (“Biden politics”), the two most popular politicians in the
United States. Alexandria Ocasio Cortez was also mentioned in chat posts
(“AOC”). Topics related to controversial violent events, such as Kyle
Rittenhouse incident (“Kyle Rittenhouse”) and police violence (“Florida Police
Violence”), and the Black Lives Matter protest (“BLM”) appear in political
streams as well.

As shown in Figure 8, topics related to US politics appear frequently. There
are 10 topics, accounting for 21% of political chat posts and 6% of all chat
posts. Given that Twitch is most popular in the United States, this finding is
not surprising. Notable topics include discussions about Trump (“Trump and
Gender”) and Biden (“Biden politics”), who are the two most prominent
politicians in the country. Alexandria Ocasio-Cortez is also mentioned
(“AOC”). Additionally, topics related to controversial violent events, such as
the Kyle Rittenhouse incident (“Kyle Rittenhouse”), police violence (“Florida
Police Violence”), and the Black Lives Matter protest (“BLM”), appear in
political streams as well.

Fig 9: Topic Ranking: Identity politics

Interestingly, topics related to identity politics appear almost as frequently
as those related to US politics and international issues. There are 9 topics,
accounting for 21% of political chat posts and 6% of all chat posts. Figure 9
illustrates the identity politics-related topics. The most frequently
appearing topic in the political chat post corpus is related to trans rights
(“Trans Right”). Gender issues are also widely covered, with three topics
related to gender politics. Racism is another significant issue in political
streams, with three topics addressing racism or race-related issues (“Asian
issue”). This finding aligns with the literature, which suggests that Twitch
and other online spaces with streaming chat functions can act as virtual third
places, revealing users’ identities [21], and fostering a sense of community
through parasocial relationships between streamers and audiences [33].
Audiences may feel that Twitch political streams provide a space where they
can reveal their identities and comfortably discuss related issues.

Fig 10: Topic Ranking: Ideological debate

Topics related to ideological standings are also present. There are 7 topics,
comprising 15% of political chat posts and 4% of all chat posts. Figure 10
provides more details about these topics of ideological debate. More than half
of the topics reference leftist ideologies, while only two topics indirectly
relate to conservative or right-wing ideas. This may suggest that right-wing-
oriented political streams engage in self-reinforcing communication by
condemning leftist political enemies. Additionally, the categories ”Public
health and politics” and ”Environmental issues” each have two topics,
accounting for 4% of political chat posts and 1% of all chat posts,
respectively.

Fig 11: Emote usages in political streams

Fig 12: Kkapitalist and Bboomer

One notable aspect is the use of Twitch-specific communication modes,
particularly “emotes,” in political discussions. Twitch emotes are akin to
emojis used in streaming chats. Figure 11 demonstrates that three
emotes—Potchamp, Bboomer, and Kkapitalist—are frequently employed in chat
posts categorized as political. Figure 12 depicts two commonly used emotes,
Kkapitalist and Bboomer, designed to satirize affluent capitalists and Baby
Boomers, respectively. The prevalence of these emotes suggests certain
political inclinations among Twitch users, potentially indicating anti-
capitalist and anti-Boomer sentiments. Alternatively, this usage can be
interpreted with more context. For instance, some users may associate Boomers
with environmental concerns, as keywords related to environmental issues often
co-occur with “Bboomer” in the topic Environmental issue and Bboomer in Figure
11. Overall, political discourse on Twitch utilizes not only text but also
context-specific emotes, which merits further exploration by scholars of
political communication.

##  7 Question 3: How do they interact?

###  7.1 Reference network in chat posts within a political stream

Using the collected chat post data, I have constructed ’reference’ networks of
chat posts from each political streamer to observe user interactions. In
Twitch streaming chats, users often refer to streamers or other users for
various purposes, such as sending emotes, asking related questions, or
engaging in casual conversation. This section explores how Twitch users in
political streams communicate with each other through streaming chat and
examines the types of content they share. Specifically, I illustrate the
structure of user reference networks within political streams and highlight
insights gained from analyzing these interactions.

I identified all unique users who posted at least one chat post in each
channel and considered them as nodes for reference networks. The edges in
these networks were established through the following steps: 1) Identifying
chat posts containing ‘$@$’ symbols. 2) Extracting the subsequent words after
the symbol as references to other users. 3) Establishing directed edges from
the user who posted the chat to the mentioned user if their name matched the
extracted word. Out of 478 active political streamers, I created 279 directed
reference networks for channels where at least one chat post mentioned another
user within the same stream.

####  7.1.1 Network Descriptive Statistics

Fig 13: Ratio of isolates

The first thing that should be noted is reference networks mostly have a
number of isolates. Figure 13 shows the distribution of the ratio of isolates
in 279 mention networks. It shows a nearly normal distribution of values with
a mean above 0.5. This shows that almost half of the users in reference
networks do not ever post a chat with a mention (‘$@$’). The pattern is not
that surprising as most users publish chat posts to comment about the ongoing
streamings they are watching, which does not need to mention somebody.

One notable observation is that reference networks typically include a
significant number of isolates. Figure 13 illustrates the distribution of
isolate ratios across 279 mention networks. The distribution follows a nearly
normal pattern with a mean slightly above 0.5, indicating that nearly half of
the users in these networks never post a chat with a mention (‘$@$’). This
pattern is understandable since many users contribute chat posts solely to
comment on ongoing streams they are watching, without needing to mention
others. However, in approximately 40 political streams, over 60% of their
interactions involve references to other viewers or streamers. This suggests
highly engaged discussions in certain political streams, akin to the very
intimate discussions often found in smaller Twitch streams [34]. This also
aligns with the earlier finding that most channels likely involve around 70
users who have posted at least one chat post (see Figure 5).

Fig 14: Distribution of mean node degrees. The x-axis is log transformed while
labels show the raw values.

Fig 15: Distribution of max node degrees. The x-axis is log transformed while
labels show the raw values.

Then, how many chat posts mentioning other users do Twitch users send and
receive? The histograms depicting the mean degree of all nodes in each mention
network are presented in Figure 14. The distribution of logged values
indicates a nearly normal pattern, with a mean of 7.9 and the highest mean
degree reaching 84.3. Figure 15 further illustrates distributions of maximum
node degree, focusing separately on in-degree and out-degree. A notable
observation is that the maximum out-degree values are generally higher than
those of in-degree. This suggests that a few prolific users, who frequently
mention others, significantly influence the overall degree distribution.
Specifically, the peak values in the maximum all-degree histogram (upper left)
align closely with those in the maximum out-degree histogram (bottom),
indicating their substantial impact.

The mean value of the maximum all degree is 5130.5, with a median of 125,
indicating significant skewness due to a few exceptionally active users, as
evident from the histograms. These users, often channel owners or moderators,
play roles such as answering questions during streams or enforcing streaming
rules, contributing extensively to the network’s edges. They can be considered
opinion leaders within these reference networks, owing to their
disproportionately large number of connections.

Fig 16: Distribution of reciprocity scores

The reciprocity and modularity scores of all reference networks are depicted
in Figure 16. The reciprocity score represents the proportion of reciprocal
ties within each network, while the modularity score indicates how connected
nodes are within their communities. The mean reciprocity score is 0.22,
indicating that, on average, 22% of connections among nodes are reciprocal.
This means approximately one-fifth of interactions within each reference
network involve mutual mentions between users. This finding aligns with Figure
15, which highlights the dominance of some highly active users in the
communication network, where lower reciprocity is expected.

Fig 17: Does network shows the power-law degree distribution?

Figure 17 demonstrates that many mention networks follow a power-law degree
distribution. I fitted a power-law degree distribution to each network and
used the Kolmogorov-Smirnov test to determine if we could reject the null
hypothesis—that the network could be derived from this distribution. The
results show that over 96% of the networks conform to the fitted power-law
degree distribution, similar to those observed on other social media platforms
[37, 38, 39]. This implies that the structural characteristics of mention
networks in political streaming chats on Twitch are not significantly
different from those on other social media platforms. In other words, despite
the technological differences inherent in real-time interaction and streaming
chat, the fundamental structure of political communication networks on Twitch
remains similar to those on other platforms.

##  8 Conclusion

In this article, I have addressed three main questions regarding Twitch
politics: 1) Who are the political Twitch streamers? 2) What content is
covered in political streams? 3) How do audiences of political streams
interact with each other? These questions were explored by focusing on the
streaming chat function, which is central to the platform’s communication
technologies.

To answer the first question, I utilized the Twitch API and supervised
machine-learning techniques to identify 574 political streamers. This study is
the first comprehensive attempt to identify political actors on the platform,
providing a valuable methodology for future research. The pipelines used to
identify political streamers and collect chat data will be fully open and
shared with this article.

For the second question, I employed topic modeling to examine the content of
political streams. Since the content of political streams on Twitch was
previously unknown, this study contributes by offering informative snapshots
of this content. Notably, I found that identity-related topics are frequently
discussed, highlighting how real-time interaction technology may influence
topic choices among political actors. Future research could further explore
identity-related topics, such as focusing on feminist streamers.

To address the third question, I created and analyzed user-reference networks
within each political streamer’s chatroom. The analysis revealed that a small
number of audience members dominate the communication network by frequently
referring to one another. Additionally, despite the technological differences
of real-time interaction and streaming chat, the fundamental structure of
political communication networks on Twitch resembles those on other platforms.
Most user-reference networks follow a power-law distribution, similar to
communication networks on other social media platforms [37, 38, 39].

There are, however, areas for improvement. This study only analyzed text data
from stream titles and profiles of streamers who used three specific game
names. There may be political streamers who discuss political issues without
explicitly indicating so in their titles or profiles. Addressing this
limitation in future research could lead to a more thorough identification of
political streamers and a deeper understanding of Twitch politics.
Additionally, chat post data could be analyzed in various ways, such as
focusing on toxicity. Further analyses could also explore the partisanship of
political streamers and how user engagement in streaming chat varies based on
the streamer’s partisanship. These insights could contribute to the literature
on both partisanship and political communication.

## Supporting information

##### S1 Appendix A. Game name identification

The first step of the process is getting to know what “game-names” political
streamers use. “Game-names” are tags of Twitch live-streaming that refer to
what streamers are broadcasting. As Twitch is a mainly game-oriented platform,
most of game-names are the names of games they are playing, such as League of
Legends and Fortnite. However, there are game names that are not directly
related to gaming activities, ranging from “Just Chatting” to “Cooking”.

It is quite apparent that streamers who stream with the game name “Politics”
can be classified as political streamers and can be added to the list. In
addition, based on the information from the site “Twitchmetrics”, which
provides various information of Twitch streamers including rankings by
category based on the information they received through Twitch API, I was able
to know that political streamers also use the game name “Talk Shows and
Podcasts” [40]. However, there are also some streamers who stream political
content without using “Politics” or “Talk Shows and Podcasts” as the game name
of their streams. The best example would be Hasanabi, who has been thoroughly
described in the previous section: He never broadcast with “Politics”.
Instead, he uses “Just Chatting” as the game name when he is streaming about
political issues. This might indicate the possibility that I might be able to
find political streamers out of all Twitch streamers who stream with “Just
chatting” tag. The challenging part is that most of the streamers who do not
explicitly stream their gaming activity (sometimes they even stream their
gaming activity with “Just Chatting” game name) but do something else choose
to use ”Just Chatting” as their game name. The topics vary from talk shows by
herself or with other streamers to vlogging their traveling activities. In
other words, when I use Twitch API to get information on live streaming with
“Just Chatting” game name, I will get a list of streamers who stream very
different topics. But, before including “Just Chatting” game name to the list
of game names that might have political streamers, I conducted preliminary
analysis on “Just Chatting” streamers to see whether there are political
streamers who are using the game name.

Using Twitch API “Get Streams” function, I have retrieved the list of
streamers who stream in English with the game name “Just Chatting”.111The list
has been retrieved at 12:00 PM, June 30th, 2021 As there are a substantial
number of streamers who have only a single viewer, I stopped when the list
ends up with a streamer with a single viewer count. I was able to identify 873
streamers who were streaming at the time by making “Get Stream” requests
through Twitch API. “Get Stream” request retrieves various information about
the ongoing streaming: user id of the streamer, viewer count, the title of the
streaming, URL of a thumbnail image, etc. Then, how can I know whether
streaming covers political content or not? I found thumbnail images and titles
of streamings can be helpful.

By using URL information in the retrieved data of live streaming, I was able
to download all thumbnails of 873 streamings. Thumbnails are basically the
screen viewers were watching at the time I made the “Get Stream” request.
Therefore, by downloading thumbnail images, I can retrieve visual information
of ongoing streamings. By combining visual information and text information
from streaming titles, I was able to identify some political streamings.

Fig A1: Example of political streamings with ”Just Chatting” game name

Figure A1 shows the thumbnail images of political streamings. Upper left of
figure 1 is a thumbnail image of the streaming by “Central_Committee” with the
title “DNC WORKING TO BLOCK NINA TURNER — HASAN MALDING OVER DEBATE BROS — NYC
MAYOR’S RACE VOTE COUNT MASSIVE ERROR — ”. Upper right is a thumbnail image of
the streaming by “ImNotRightNetwork” with the title “Where are our freedoms?”.
Lower left is a thumbnail image of the streaming by “GeneralKaosLive” with the
title of “(rainbow emote) TREVOR PROJECT FUNDRAISER (rainbow emote)Helping to
prevent suicide and giving back to the LGBTQ+ communty - !Charity !CXP ”. The
lower right is a thumbnail image of the streaming by “Axe_Truth” with the
title of “Discouraged & Division in the ranks Candace Owens vs Kim Klacik”.
When we use both textual information from the titles and visual information
from the thumbnail images, they can definitely be classified as political
streamers. All of the titles touch on some political terms, such as “Mayor’s
Race”, “freedom”, “LGBTQ+”, “Candace Owens vs Kim Klacik”. Also, the
composition of thumbnail images shares some characters. The upper left and
lower right both show political content (one is visual and the other is
textual). And except for the lower right, all three streamers use a similar
broadcasting setting: a very huge microphone with their face exposed. Overall,
there are political streamers who broadcast with the game-name “Just
Chatting”.

##### S2 Appendix B. Supervised machine learning classifier for identifying
political streamers

Table B1: ML Models Performance Model | Precision | Recall | F-1 |
---|---|---|---|---
Logit + Count | 0.822 | 0.721 | 0.760 |
Logit + TFIDF | 0.907 | 0.640 | 0.703 |
| XGBoost + Count
---
(learning rate = 0.5)
0.855 | 0.745 | 0.788 |
| XGBoost + TFIDF
---
(learning rate = 0.5)
0.868 | 0.732 | 0.781 |
Fig B1: Venn Diagram of Political Streamers

I have trained logistic regression and XGBoost classifiers using the train
data. And I have specified models with the count and TF-IDF vectorizer for
each algorithm. As the target value of the dataset is infrequent, I have used
precision, recall, and F-1 score, which are known to be more appropriate to
evaluate imbalanced data, to evaluate the performance of classifiers. Table A1
shows the performance of the 5-fold cross-validation results of each model.
XGboost models mostly outperform logistic regression models in all three
criteria, while it is hard to say which vectorizer is outperforming.
Therefore, I have four hyperparameters of the XGBoost models with different
vectorizers: colsample_bytree, learning_rate, max_depth and n_estimators. I
have used the Gridsearch method to find a combination of hyperparameters to
produce the best macro F1 score. The best score of each model with different
vectorizers looks almost identical: 0.796 for the model with TFIDF vectorizer
and 0.795 for the model with count vectorizer. Although the difference is
negligible, I have used the model with TFIDF vectorizer for machine labeling
the rest of the unlabeled data as it shows slightly better performance.
Through this process, I was able to identify additional 48 political accounts
from 45,477 just chatting streamer accounts. Adding 550 political accounts
identified during the hand-coding process, I was able to identify 598
political accounts in total.

However, there can be duplicates in the list, as the streamers are able to
switch the ‘game name’ of their streaming whenever they want. For example, a
hypothetical streamer A can stream one day with “Just Chatting” game name on
the other day with “Politics” game name. In this case, streamer A would be in
the intersection between the “Politics” set and “Just Chatting” set in Figure
A2. I was able to find 24 streamers who have ever streamed with more than a
single game name. There are 19 accounts that have at least once streamed both
in “Just Chatting” and “Politics” and 5 streamers who have ever streamed with
“Talk Shows and Podcast” and “Politics”. After dealing with the duplicates, I
was finally able to identify 574 unique political accounts on Twitch. Figure 3
shows the breakdown of these 574 political streamers: 254 streamers have
streamed only with “Just Chatting”, 174 streamers have streamed only with
“Politics”, 122 streamers have streamed only with “Talk Shows and Podcasts”,
19 streamers have streamed with both “Just Chatting” and “Politics”, and 5
streamers have streamed with both “Talk Shows and Podcast” and “Politics”.

##### S3 Appendix C. Extensive lists of political topics

Fig C1: Identified Political Topics (1) Fig C2: Identified Political Topics
(2)

## Acknowledgments

Thanks to one of the authors of Flores et al. (2019), Joseph Seering, it was
possible to successfully go through the data collection process. Streaming
chat posts collection was largely done based on the codes in his
Github:https://github.com/josephseering/twitchtext

## References

  * 1. Asbury-Kimmel V, Chang KC, McCabe KT, Munger K, Ventura T.  The Effect of Streaming Chat on Perceptions of Political Debates.  Journal of Communication. 2021;71(6):947–974.
  * 2. Lorenz T.  How Hasan Piker Took Over Twitch.  New York Times. 2020;.
  * 3. Marwick AE.  You May Know Me from YouTube: (Micro-)Celebrity in Social Media.  In: Marshall DP, Redmond S, editors. A Companion to Celebrity. Chichester: John Wiley; 2015. p. 333–350.
  * 4. Munger K, Phillips J.  A Supply and Demand Framework for YouTube Politics.  The International Journal of Press/Politics. 2020;.
  * 5. Senft TM.  Microcelebrity and the branded self.  A companion to new media dynamics. 2013; p. 346–354.
  * 6. Lewis R.  “This is what the news Won’t show you”: YouTube creators and the reactionary politics of micro-celebrity.  Television & New Media. 2020;21(2):201–217.
  * 7. Sjöblom M, Hamari J.  Why do people watch others play video games? An empirical study on the motivations of Twitch users.  Computers in human behavior. 2017;75:985–996.
  * 8. Lopez J.  Dr Disrespect will donate all stream donations in June to charity.  DOT ESPORTS. 2020;.
  * 9. Ocal A.  Benjamin ”DrLupo” Lupo speaks out in Twitch stream on George Floyd, ongoing protests.  ESPN. 2020;.
  * 10. Periwal SA.  ”WTF America”: Valkyrae, Markiplier, Jacksepticeye and more influencers condemn US Capitol violence.  sportskeeda. 2021;.
  * 11. Dean B. Twitch Usage and Growth Statistics: How Many People Use Twitch in 2021?; 2021.  Available from: https://backlinko.com/twitch-users.
  * 12. Gilbert B.  Amazon’s streaming service Twitch is pulling in as many viewers as CNN and MSNBC.  Business Insider. 2018;.
  * 13. Ask K, Spilker HS, Hansen M.  The politics of user-platform relationships: Co-scripting live-streaming on Twitch. tv.  First Monday. 2019;.
  * 14. Johnson MR, Carrigan M, Brock T.  The imperative to be seen: The moral economy of celebrity video game streaming on Twitch. tv.  First Monday. 2019;24(8).
  * 15. Churchill BC, Xu W.  The modem nation: A first study on Twitch. tv social structure and player/game relationships.  In: 2016 IEEE international conferences on big data and cloud computing (BDCloud), social computing and networking (SocialCom), sustainable computing and communications (SustainCom)(BDCloud-SocialCom-SustainCom). IEEE; 2016. p. 223–228.
  * 16. Claypool M, Farrington D, Muesch N.  Measurement-based analysis of the video characteristics of Twitch. tv.  In: 2015 IEEE Games Entertainment Media Conference (GEM). IEEE; 2015. p. 1–4.
  * 17. Flores-Saviaga C, Hammer J, Flores JP, Seering J, Reeves S, Savage S.  Audience and Streamer Participation at Scale on Twitch.  In: Proceedings of the 30th ACM Conference on Hypertext and Social Media; 2019. p. 277–278.
  * 18. Jackson NJ.  Understanding memetic media and collective identity through streamer persona on Twitch. tv.  Persona Studies. 2020;6(2):69–87.
  * 19. Sjöblom M, Törhönen M, Hamari J, Macey J.  The ingredients of Twitch streaming: Affordances of game streams.  Computers in Human Behavior. 2019;92:20–28.
  * 20. Ford C, Gardner D, Horgan LE, Liu C, Tsaasan A, Nardi B, et al.  Chat speed op pogchamp: Practices of coherence in massive twitch chat.  In: Proceedings of the 2017 CHI conference extended abstracts on human factors in computing systems; 2017. p. 858–871.
  * 21. Hamilton WA, Garretson O, Kerne A.  Streaming on twitch: fostering participatory communities of play within live mixed media.  In: Proceedings of the SIGCHI conference on human factors in computing systems; 2014. p. 1315–1324.
  * 22. Johnson MR, Woodcock J.  “And today’s top donator is”: How live streamers on Twitch. tv monetize and gamify their broadcasts.  Social Media+ Society. 2019;5(4):2056305119881694.
  * 23. Ruberg B, Cullen AL.  Feeling for an audience: The gendered emotional labor of video game live streaming.  Digital Culture & Society. 2019;5(2):85–102.
  * 24. Ruberg B, Cullen AL, Brewster K.  Nothing but a “titty streamer”: legitimacy, labor, and the debate over women’s breasts in video game live streaming.  Critical Studies in Media Communication. 2019;36(5):466–481.
  * 25. Ruberg B.  “Obscene, pornographic, or otherwise objectionable”: Biased definitions of sexual content in video game live streaming.  New Media & Society. 2021;23(6):1681–1699.
  * 26. Ruberg B, Lark D.  Livestreaming from the bedroom: Performing intimacy through domestic space on Twitch.  Convergence. 2021;27(3):679–695.
  * 27. Taylor T.  Watch me play: Twitch and the rise of game live streaming.  Princeton University Press; 2018.
  * 28. Riddick S, Shivener R.  Affective spamming on Twitch: rhetorics of an emote-only audience in a presidential inauguration livestream.  Computers and Composition. 2022;64:102711.
  * 29. Rivera J.  AOC played Among Us and achieved what most politicians fail at: acting normal.  The Guardian. 2020;.
  * 30. Holt K.  AOC will return to Twitch tonight for another round of ’Among Us’.  Engadget. 2020;.
  * 31. Dzhanova Y.  Rep. Alexandria Ocasio-Cortez streamed ’Among Us’ on Twitch and raised $200,000 for food pantries and other groups.  Business Insider. 2020;.
  * 32. Cale M.  Streamlabs report shows HasanAbi rivaled major news outlets for presidential election coverage.  DOT ESPORTS. 2020;.
  * 33. Sherrick B, Smith C, Jia Y, Thomas B, Franklin SB.  How parasocial phenomena contribute to sense of community on Twitch.  Journal of Broadcasting & Electronic Media. 2023;67(1):47–67.
  * 34. Munger K. Streaming Chat and Connective Effervescence; 2021.  https://kevinmunger.substack.com/p/streaming-chat-and-connective-effervescence.
  * 35. Roberts ME, Stewart BM, Tingley D.  Stm: An R package for structural topic models.  Journal of Statistical Software. 2019;91:1–40.
  * 36. Liebman BL, Roberts ME, Stern RE, Wang AZ.  Mass digitization of Chinese court decisions: How to use text as data in the field of Chinese law.  Journal of Law and Courts. 2020;8(2):177–201.
  * 37. Bodrunova SS, Blekanov IS.  Power laws in ad hoc conflictual discussions on Twitter.  In: Digital Transformation and Global Society: Third International Conference, DTGS 2018, St. Petersburg, Russia, May 30–June 2, 2018, Revised Selected Papers, Part II 3. Springer; 2018. p. 67–82.
  * 38. Martha V, Zhao W, Xu X.  A study on Twitter user-follower network: A network based analysis.  In: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining; 2013. p. 1405–1409.
  * 39. Myers SA, Sharma A, Gupta P, Lin J.  Information network or social network? The structure of the Twitter follow graph.  In: Proceedings of the 23rd international conference on world wide web; 2014. p. 493–498.
  * 40. Twitchmetrics.  Twitchmetrics - Streamers and Games.  twitchmetricsnet. 2022;.


# Response of a Liquid 3He Neutron Detector

A.E. Sharbaugh<EMAIL_ADDRESS>Department of Physics, University
of Colorado Denver, Denver, Colorado 80217, USA  L. Jones
<EMAIL_ADDRESS>Department of Physics, University of Colorado Denver,
Denver, Colorado 80217, USA  A.N. Villano<EMAIL_ADDRESS>Department of Physics, University of Colorado Denver, Denver, Colorado 80217,
USA

###### Abstract

The 3He(n,p) process is excellent for neutron detection between thermal and
$\sim$4 MeV because of the high cross section and near-complete energy
transfer from the neutron to the proton. This process is typically used in
gaseous forms with ionization readout detectors. Here we study the response of
a liquid 3He neutron detector with a scintillation readout. We anticipate an
efficiency boost of around a factor of 64 compared to 10 atm gaseous
detectors, given similar detector volumes.

##  I Introduction

The (n,p) reaction occurs when a neutron enters the nucleus of an atom,
simultaneously causing a proton to leave. This process provides a reliable
method for measuring the neutron flux through a particular volume, provided
that the cross section for the process is high enough and the Q-value is
positive or not very negative. Furthermore, since in this process the outgoing
proton carries an energy deterministically related to the incoming neutron
energy, it is one of the only measurements that can measure _incoming_ neutron
energies unambiguously.

For our neutron detector design, we primarily utilize the reaction 3He(n,p)3H
whose Q-value can be ascertained by subtracting the proton separation energy
of 4He from the neutron separation energy of the same. The separation energies
are obtained from the most recent Atomic Mass Evaluation (AME) in 2020 [1, 2].
The resulting Q-value for the reaction 3He(n,p)3H is 763.755 keV. Our design
differs from previous detectors using this process because those detectors
were 3He gas-based with typical fill pressures between 4–10 atm partial
pressure of 3He and equipped with ionization readouts [3, 4, 5]. Our design
implements 3He in the liquid state which is at least 64 times more dense [6]
than the gas detectors mentioned (assuming a maximum 3He partial pressure of
10 atm).The design has a scintillation readout via a traditional
photomultiplier tube (PMT); silicon photomultipliers are frozen-out at the
proposed operating temperatures.

Recently the SPICE/HeRALD collaboration provided a proof-of-principle for a
cryogenic photomultiplier readout of a liquid helium volume [7]. The
collaboration also kindly shared their published data with us in digital form
which we use for our design. Our basic design is shown in Fig. 1. We have
envisioned a hermetic copper vessel with a large gas space above a small cube
(2$\times$2$\times$2 cm3) where liquid 3He will collect when the vessel is
filled with 6.63 liters (22.9 atm) of 3He at room temperature and lowered to a
temperature of 1 K. Liquification will be achieved by attaching the vessel to
an off-the-shelf, commercially obtainable closed-cycle 1 K cryocooler [8]. The
cubical portion of the vessel will have a small quartz window through coated
with tetraphenyl butadiene (TPB) so the phototube can measure the helium
scintillation [9].

Figure 1: The prototype design of a liquid 3He neutron detector with a
photomultiplier readout. The height of the gaseous portion of the vessel is
approximately 11 cm tall, whereas the liquid fills a 2$\times$2$\times$2 cm3
cubical vessel attached near the bottom.

There are two key applications that we had in mind when designing this neutron
detector: measuring neutron backgrounds in the low-flux environments
associated with rare event searches, and measurements of neutron-producing
nuclear reactions with tiny cross sections. Rare event searches like dark
matter and neutrinoless double-beta decay experiments are sensitive to neutron
backgrounds and often operate deep underground to shield out as many neutrons
as possible. Still, there are residual neutrons in the environment for various
reasons [10]. The most common method used to estimate the level of the
residual neutrons is by simulation: either of the ($\alpha$,n) process for
radiogenic neutrons [11] or of cosmic rays for cosmogenic neutrons [12]. One
key difficulty is that, while the simulations themselves have made great
strides, there is often no measured spectrum to compare to because the neutron
flux is so low that its measurement comes with large effort [5, 13]. We will
focus the current study on the sensitivity of our detector design to neutrons
in a deep underground environment (SNOLAB).

The measurements of neutron-producing nuclear reactions will not be discussed
in detail but it is worth mentioning this as another application for our
detector design and potentially critical to the field of nuclear astrophysics.
Nuclear astrophysicists have sought for decades to understand the slow-
neutron-capture process (s-process) in stars. The s-process is responsible for
the production of almost half the heavy elements in the universe. In order to
constrain this process hundreds of nuclear cross sections must be measured.
One example of them is the 13C($\alpha$,n)16O reaction, which serves as a
neutron source in stars near the Gamow peak, between 150–230 keV. Current
measurements for that region yield uncertainties of greater than 40% [14].
Another key example is the 22Ne($\alpha$,n)25Mg which competes with the
similar ($\alpha$,$\gamma$) process that does not produce neutrons [15]. Each
of those reactions have pico-barn level cross sections in the off-resonance
regions and could be measured precisely with an instrument that has a high
neutron detection efficiency.

##  II Spectral features of 3He(n,p)

Beimer, et al. [3] outlines a procedure for computing the expected deposited-
energy spectra when a monoenergetic neutron impinges on a gaseous 3He detector
cylinder of 5 cm diameter and 15 cm length. We have reproduced the Beimer
model for all incident neutrons energies by interpolating their measured
spectral parameters. We are specifically interested in the proton leakage
portion of the distribution. For liquid helium detectors–like the prototype
we’re designing–we expect the response to be qualitatively similar except with
substantially less proton leakage coming from the possibility of a proton
escaping the sensitive area of the detector after the 3He(n,p) reaction.

In the Beimer reference, the response of the 3He detector to neutrons was
modeled by construction a probability distribution function (PDF) composed of
elements from each of the physical processes involved. The processes were:
elastic scattering from argon; elastic scattering from helium; proton leakage;
and the (n,p) peak. The original reference used 17 parameters ($A_{1}$ –
$A_{18}$; no $A_{10}$) to fit these key parts of the measured spectra to the
response PDF, as seen in Fig. 2. The important portions of the spectrum for
our purposes are the (n,p) peak, proton leakage, and elastic 3He contribution,
as we expect these processes to also be present in our liquid 3He prototype.

The (n,p) peak is governed by the terms in Eq. 1. We define $f_{p}$ to be the
PDF of measured energy $E$ for a neutron with energy $E_{n}$. Amplitude,
$A_{1}$ and $A_{8}$, behave similarly but the width of the Gaussian is
different on the lower energy side ($A_{3}$) than on the higher energy side
($A_{9}$). $Q$=763.755 keV represents the nuclear $Q$ value for the 3He (n,p)
reaction discussed above.

$f_{p}(E,E_{n})=\\\
\begin{cases}A_{1}e^{-\frac{1}{2}(\frac{(E_{n}+Q)-E}{A_{3}})^{2}};E\leq(E_{n}+Q)\\\
A_{8}e^{-\frac{1}{2}(\frac{(E_{n}+Q)-E}{A_{9}})^{2}};E>(E_{n}+Q)\end{cases}\\\
$ (1)

The elastic 3He contribution is governed by the terms in Eq. 2. This result
depends implicitly on the incoming neutron energy $E_{n}$ because the
parameter $A_{13}$ scales with this energy. The term $A_{13}-E$ gives a
slightly negative slope at low energies. The remaining parameters in the
numerator ensure the PDF is never negative for a given energy below the
maximum recoil energy.

$f_{he}(E,E_{n})=\frac{A_{11}(A_{13}-E)+A_{12}}{1+e^{\frac{E-A_{13}}{A_{14}}}}$
(2)

Parameters $A_{4}$ and $A_{6}$ describe the behavior of the exponential
preceding the primary signal peak. The population of this region, shaded green
or purple in Fig. 2, is determined by the number of protons which escape the
active area and leak out of the detection medium. We define $f_{esc}$ in Eq.
3. Note parameters $A_{2}$ and $A_{10}$ are unused [3].

$f_{esc}(E,E_{n})=\frac{1}{2}erfc(1-\frac{(E_{n}+Q)-E}{A_{3}})(A_{1}A_{4}e^{-\frac{(E_{n}+Q)-E}{A_{5}}}+A_{6}e^{-\frac{(E_{n}+Q)-E}{A_{7}}})$
(3)

Figure 2: The parameters of the response function mapped to a 1.5 MeV signal
peak. The red curve represents data collected by Biemer et al. using
ionization vessels filled with a gaseous mixture containing a partial pressure
of 6 atm of helium, 3 atm of argon, and 0.5 atm of methane [3]. Any scattering
produced by the methane is negligible. Parameters $A_{11}-A_{14}$ describe the
scattering step formed by the argon, but this part of the curve is not
relevant to our analysis because the proposed detector will contain purely
helium. The blue curve represents the predicted spectra for neutrons of
$E_{n}$=1.5 MeV in the liquid 3He detector. The area shaded green highlights
the 80% leakage from the standard gaseous detector, while the area shaded
purple highlights the 13.5% leakage from the proposed detector. Reducing
proton escape increases peak efficiency, resulting in a taller and narrower
blue curve.

Based on our calculations, the leakage accounts for approximately 80% of the
original peak. Protons (for 1.5 MeV incident neutrons) in gaseous detectors of
this type have a stopping length of approximately 10 cm. The gaseous 3He
detectors used by Biemer et al. were cylindrical, with a diameter of 5 cm and
a length of 15 cm, which led to many protons escaping. By contrast, protons in
the proposed liquid 3He detector are estimated to have a stopping length of no
more than $\sim$0.1 cm for up to around 2 MeV neutron energies. The liquid 3He
chamber will be a cube with dimensions 2 cm$\times$2 cm$\times$2 cm. As a
result, we anticipate only minor escape near the surfaces of the cube. Using
the 0.1 cm stopping length as a thickness, this surface accounts for 27% of
the total volume of the detector. We can further estimate that half of these
interactions will rebound into the detector, resulting in an upper bound of
13.5% leakage. This assumption allows us to modify parameters $A_{4}$ and
$A_{6}$ in our model. The complete probability distribution function (PDF)
obtained by summing each equation can be seen in Fig. 2.

A notable efficiency boost between a gaseous detectors of Beimer and our
designed liquid detector is expected. 3He has higher density in the liquid
state than in the gaseous state [6]. The density increase of the liquid 3He
detector we propose is a factor of 107, given a 6 atm fill pressure of the
gaseous tube we compare to. The actual thickness of the gaseous detectors used
by Beimer [3] were 5 cm thick, whereas our design is 2 cm thick; we therefore
expect an efficiency gain of about 43$\times$ for neutrons going straight
through the 5 cm dimension. This means that whereas Beimer [3] measured 0.03%
and 0.02% peak efficiencies for their gaseous 3He detectors at 1 MeV and 2.5
MeV neutron energies respectively, we would expect efficiencies of 1.5% and
0.88% respectively. For neutron energies of order 100 keV the Beimer gaseous
detectors have measured efficiencies around 0.1% so we would expect at least a
4.4% efficiency. By understanding the parameters governing the 3He(n,p)
spectra, we can now extrapolate the model for cryogenic ($\sim$ 1 K)
temperatures and assess the performance of the proposed liquid 3He detector.

##  III Berkeley model of liquid 4He response

Recently it has been demonstrated by the SPICE/HeRALD collaboration at
Berkeley that liquid 4He can be used as a scintillation detector with low-
temperature photomultipliers (PMTs) [7]. The Berkeley data show the response
of liquid 4He scintillation via PMT measurements at specific energies for
electron recoil (ER) events (radiation that produces a recoiling electron in
the detector medium) and events composed of nuclear recoils (NRs) of 4He
atoms. Since our liquid 3He prototype will utilize the (n,p) process we are
primarily interested in proton recoil events whose behavior probably lies
between the Berkeley data for electron and nuclear recoils. Nevertheless, we
use these measurements as a starting point for the expected detector response
in a 3He scintillating liquid–assuming qualitative similarity to 4He in terms
of scintillation yield and resolution.

Our primary interest is to extrapolate this data, and predict the signal peak
behavior of incident protons within the range of 700 keV - 3 MeV. To do so, we
need to define certain characteristics of each signal peak. First, we fit a
Gaussian curve to each signal peak in the Berkeley dataset (see Fig. 3 for an
example). In total, we make 13 Gaussian fits (6 ER, and 7 NR). The fitting
function is described by Equation 4.

Figure 3: The experimental data is example data taken from [7], corresponding
to 142 keV nuclear recoils. The horizontal represents the signal size in
number of photoelectrons (phe), and the vertical represents counts per bin.

$G(x)=a_{1}\textrm{exp}\left(-\frac{1}{2}\left(\frac{x-a_{2}}{a_{3}}\right)^{2}\right)+a_{4}.$
(4)

It should be noted that we obtain each data point from the same Gaussian curve
with the exception of the point corresponding to 1090 keV NR. Instead, we fit
this point to the following Gaussian plus linear curve denoted $G_{0}$:

$G_{0}(x)=a_{1}\textrm{exp}\left(-\frac{1}{2}\left(\frac{x-a_{2}}{a_{3}}\right)^{2}\right)+a_{4}x+a_{5},$
(5)

the reason for this is that the fit to the other function was not particularly
good in this case, so the new function allows for a more precise data point
such that the uncertainty has a consistent order of magnitude relative to all
other data points. Throughout all of these fits, the parameters we are
interested in are $a_{2}$ and $a_{3}$, as these determine the peak signal size
and the width of our signal respectively, in number of photoelectrons (phe).
After completing each fit, we have two sets of $a_{2}$’s and two sets of
$a_{3}$’s, one for nuclear and one for electronic recoils. We then create a
plot of each $a_{i}$ vs. recoil energy $E$. The fits of these plots are
outlined in Fig. 4.

Figure 4: (Top) From left to right, the plots of $a_{2}$ and $a_{3}$ for
nuclear recoils, respectively. (Bottom) From left to right, the plots of
$a_{2}$ and $a_{3}$ for electronic recoils, respectively. For each figure, the
energy uncertainties are obtained from [7] and the phe uncertainties are
obtained from the covariance matrix of the fits. The red points correspond to
the individual photon yield/width data and uncertainties from the Berkeley
publication and the red dashed lines are fits to those points. The blue points
represent the yield/widths from our Gaussian fits and their uncertainties and
the blue dashed lines are a fit to those points. For each plot, we used the
uncertainties from Table 1 to obtain an upper and lower bound for the energy
behavior, denoted by the shaded regions centered around the red dashed line.
Seeing as both the blue dashed line falls within this region for each plot, we
see that the fits are consistent.

For the $a_{2}$’s, we did a weighted fit to the collection of points to a
linear function $y=mx+b$, where $y$ is the signal size in npe, and $x$ is the
event energy in keV. The results of these linear fits using both our extracted
yields and the Berkeley extracted yields are shown in Fig. 4, left sides.

For the $a_{3}$ parameters we model the overall behavior in a similar way,
except using a weighted fit to a standard width-versus-energy function
$y=\sqrt{Ax+\sigma_{0}^{2}}$. Again we do this fit for both our extracted
widths and the ones from the Berekely publication [7] and the results are
displayed in Fig. 4. It was found that in all cases the $\sigma_{0}$ parameter
was negligibly small, so we don’t list it with our results. The reason for
this is that we don’t have data at low enough scattering energies to probe
this baseline (zero-energy) resolution. After the fitting procedure, we end up
with the parameters in Table 1.

Recoil Type | Slope | Intercept | A
---|---|---|---
NR | $0.461\pm 0.003$ | $4.310\pm 0.556$ | $2.457\pm 0.192$
ER | $1.185\pm 0.023$ | $-0.338\pm 1.610$ | $3.109\pm 1.263$
Recoil Type | Slope | Intercept | A
NR | $0.483\pm 0.015$ | $-0.202\pm 2.402$ | $2.621\pm 0.548$
ER | $1.308\pm 0.087$ | $-5.523\pm 6.998$ | $5.052\pm 2.613$
Table 1:  (Top) A table of fit parameters which quantify how the Gaussian
parameters $a_{2}$ and $a_{3}$ behave with deposited energy according to our
fits. (Bottom) A table of fit parameters which quantify how the Gaussian
parameters $a_{2}$ and $a_{3}$ behave with deposited energy according to the
fits from [7]. The ’slope’ and ’intercept’ parameters correspond to the slope
and intercept of the $a_{2}$ plots. The parameter $A$ corresponds to the non-
linear fit function for the $a_{3}$ parameters. Again, we do not display
values for the parameter $\sigma_{0}$ because they are all near zero. These
plots are shown in Fig. 4

The fit parameters in Table 1 show rough agreement with the analysis of the
Berkeley reference [7]. They likely differ because of the more precise fit
function for each scattering histogram and a more careful treatment of
systematic errors. We will use these models for the detector resolution in the
next section despite the large extrapolation necessary (the highest fitted
energy is around 1 MeV recoil for NRs and 180 keV for ERs whereas we need the
detector performance at the $\sim$MeV scale for both NRs and ERs).

##  IV Liquid 3He wide-band neutron spectral response

We expect our liquid 3He neutron detector design to be appropriate for low
neutron flux environments because of its high efficiency relative to
conventional gaseous 3He tubes like those used by Beimer [3]. This is because
of the much higher density of 3He in the liquid state at 1 K–0.0792
g/cm3–compared to a 6 atm gas with density 7.4$\times$10-4 g/cm3 at room
temperature [6]. In fact, our 2$\times$2$\times$2 cm3 detector is expected
register 3 times more neutron events above around 10 keV than the larger tubes
(5 cm diameter; 15 cm length) of Beimer [3]. For typical gaseous tubes with
fill pressures between 6–10 atm our design will be 64–107 times more efficient
per volume at neutron energies larger than 10 keV. Below 10 keV the efficiency
will quickly approach 100%.

SNOLAB is home to some of the most sensitive low-background experiments and as
such has an exceptionally low neutron flux. We have estimated the neutron flux
as a function of energy from around 6–10 MeV neutrons all the way down to
thermal energies. These results are seen in Fig. 5. The high-energy flux is
derived from the simulated neutron flux of the Super Cryogenic Dark Matter
Search (SuperCDMS) for the SNOLAB environment [16]. In that flux function
there were several sharp features due to the modeling from SOURCES4C [17] and
the propagation of ($\alpha$,n) neutrons through the cavern rock and
shotcrete. We have smoothed over features (see Fig. 5 red and purple curves)
to produce a smooth flux function down to around 10 keV neutron energy. The
normalization for the high-energy flux portion of this curve is taken from the
SNOLAB handbook [18] where the value is 4000 n/m2/day. The high-energy flux is
quoted as “fast” in the reference, this typically means above 1 MeV but no
explicit lower bound is given. Below about 10 keV in neutron energy we have
extrapolated the flux with a simple power-law function $aE^{b}$. The power law
was determined by a linear regression of the log of our smoothed data in the
range 10–20 keV. The extrapolated flux was joined at low energies with a
Maxwell-Boltzmann distribution for (thermal) neutrons at room temperature. The
unit-normalized function for the thermal neutrons is:

$f_{t}(E)=2\sqrt{\frac{E}{\pi}}\left(\frac{1}{kT}\right)^{3/2}exp\left(-\frac{E}{kT}\right).$
(6)

This thermal flux was multiplied by the measured total flux of thermal
neutrons in SNOLAB [13], 4144.9$\pm$49.8$\pm$105.3 n/m2/day. In this quoted
measurement the first set of uncertainties is statistical and the second set
is _systematic_. The systematic uncertainties come from inexactness in the
analysis methods whereas the statistical uncertainties are based on the
limited number of counts observed over the finite measurement period.

Figure 5: Neutron flux as a function of energy for the SNOLAB environment. The
red and purple curves are smoothings of the high-energy neutrons computed from
the SuperCDMS sensitivity projection paper [16]. The orange portion of the
curve is an interpolation in the region below 10 keV down to the Maxwell-
Boltzmann portion for the thermal neutron flux. The integral of the thermal
flux region (from 10-4 eV to about 1 eV) is normalized to the measured
underground SNOLAB thermal neutron flux: 4144.8$\pm$49.8$\pm$105.3 n/m2/day.

If the neutron flux as a function of energy from Fig. 5 is described as
$f(E)$, then we might expect the overall rate in our detector to be
$R=f(E)(\Sigma_{n,p}+\Sigma_{e})V$, where $R$ is the differential rate at a
given neutron energy; $\Sigma_{n,p}$ is the macroscopic (n,p) cross section
for 3He as a function of neutron energy; $\Sigma_{e}$ is the elastic cross
section for 3He as a function of neutron energy; and $V$ is the detector
volume. In fact this expression is only accurate for neutron energies above
approximately 1 keV. Below that value the cross section for the (n,p)
interaction is so large that there is significant _self-shielding_. Self-
shielding occurs when a material has a high enough interaction probability
that the neutrons will rarely penetrate to the center of the material. In the
case of thermal neutrons (with energies around 0.025 eV) the situation is
extreme and all (n,p) events will take place at the surface of the detector.

We modeled the neutron flux below 1 keV at the surface of our detector as non-
zero only in the direction toward the inside of the detector in a 2$\pi$ solid
angle. This was accomplished by using the following _angular neutron flux_
(following the notation of Duderstadt and Hamilton [19]):

$\varphi(\mathbf{r},E,\hat{\Omega},t)=\frac{1}{4\pi}f(E)\theta(-\hat{\mathbf{n}}\cdot\hat{\Omega}),$
(7)

where $\hat{\mathbf{n}}$ is the unit normal of the surface of our detector and
$\theta$ is the unit step function. $\hat{\Omega}$ is the unit vector about
point $\mathbf{r}$ in which the flux is moving. The _angular current density_
is then given by the following expression.

$\mathbf{j}(\mathbf{r},E,\hat{\Omega},t)=\hat{\Omega}\varphi(\mathbf{r},E,\hat{\Omega},t)$
(8)

Correspondingly, the rate of neutrons passing into our detector volume is:

$R_{\mathrm{surf}}=\oint\mathbf{j}(\mathbf{r},E,\hat{\Omega},t)\cdot
dAdEd\hat{\Omega}.$ (9)

Below 1 keV we have assumed that the detector is completely opaque and
therefore the number of neutron detections is given by Eq. 9 independent of
the macroscopic cross section $\Sigma_{n,p}$. This situation is depicted in
Fig. 6. The left side shows how the flux is treated above 1 keV neutron
energy–the detector is effectively transparent and the expression
$f\Sigma~{}V$ is an excellent approximation for the rate of detections. The
right side of Fig. 6 shows that below 1 keV a neutron entering the detector is
highly likely to interact near the surface, either right at the surface if the
energy is very low (like thermal neutrons) or slightly further in. To assist
the reader in understanding the transition between low neutron mean free path
and high mean free path, we have included a plot of the mean free path for
neutrons in liquid and gaseous (6 atm partial pressure) 3He in Fig.
LABEL:fig:mean-free-patha.

Figure 6: Schematic depicting how the flux is converted into an expected event
rate. (Left) Shows situations where the (n,p) cross section is small enough
(mean free path of neutrons much larger than detector size) that the flux can
be treated as uniform throughout the detector and equal to the external
neutron flux. (Right) Shows situations–taken to be $E_{n}$<1 keV–where any
incoming neutron is nearly guaranteed to produce an (n,p) reaction in the
detector. The black region near the edge depicts the region where thermal
neutrons ($E_{n}\sim$0.025 eV) are likely to interact. The tan region shows
where neutrons of energies somewhat higher–up to around 1 keV–are likely to
interact.

The (n,p) interactions which do take place near the edge of the
detector–mostly, but not all below 1 keV–have a chance to have some of the
proton energy escape the liquid. For gaseous detectors (as discussed earlier)
this possibility could account for large percentages (up to 80%) of the
measured events. Because of the higher densities in liquid this possibility is
highly suppressed but can still happen. We computed, using the stopping power
for protons in helium (obtained from the PSTAR online database version
2.0.1,July 2017 [20]), that 0.7% of low-energy events will have protons that
“leak” out of the sensitive volume. We account for these in our model by
selecting randomly that percentage of events in a given energy region and
decreasing their deposited energy based on what percent of their stopping path
is contained in the active region. To give the reader a sense for when leakage
will happen we included the range of proton and alpha particles in 3He liquid
and gas (at 6 atm pressure) in Fig. LABEL:fig:mean-free-pathb.

For elastic scatters of neutrons on the 3He nuclei, the detector is always
essentially transparent because the macroscopic elastic cross section
$\Sigma_{\mathrm{el}}$ is small. Therefore elastic scattering does not modify
the neutron flux throughout the detector and those scatters are modeled in the
same way as our (n,p) interactions at high-energy (above 1 keV).

For both the (n,p) and elastic processes we use the evaluated cross sections
as a function of energy from the JENDL 5.0 compilation [21]. From those, we
calculate the needed macroscopic cross sections by scaling the cross section
by the number density of the liquid 3He. The behavior of the cross sections is
reflected in Fig. 7a.

(a)

(b)

Figure 7: The properties of relevant particles as they travel in gaseous and
liquid 3He. (a) The mean free path of neutrons in 3He for (n,p). Some neutrons
will elastically scatter while others will react via the (n,p) process. (b)
The range of protons and alpha particles in 3He which help determine the area
of an energy event after a neutron interaction. Figure 8: The expected
spectrum of a 2$\times$2$\times$2 cm3 detector operated at SNOLAB for 1 yr
(black points) compared to the expected total neutron distribution (black
line) with (n,p) (blue shaded) and elastic (orange shaded) contributions. The
uncertainties for zero-count bins were scaled so they did not obscure the
plot, the points appear as black spikes on the plot ending at a vertical axis
value of 10-5.

Figure 8 shows the expected distribution of events for a 2$\times$2$\times$2
cm3 detector of liquid 3He given the spectral model, anticipated scintillation
resolutions, cross-section weighting, and leakage corrections previously
discussed. We have used an NR detector response model for recoiling 3He and an
ER response model for recoiling protons. This treatment is crude, but
plausible: modeling 3He as NRs implicitly includes nuclear and electronic
stopping powers, and the neglect of nuclear stopping power for protons is a
good approximation for the initial recoil energies we are interested in (above
around 764 keV). Our proton model is supported by Bethe-Bloch theory [22]
where electronic stopping is dominant and dependent on the relativistic
velocity $\beta$ only. Most of our proton stopping happens between
$\beta=0.01$ and $\beta=0.04$ ($\sim$764 keV)–within the range where electron-
stopping Bethe-Bloch theory is adequate for the purposes of a rough (10–20%
accurate) analysis of our design.

The figure shows that for a 1 yr exposure we will produce an excellent
measurement of the thermal neutron flux–given by the size of the peak near a
deposited energy equal to the Q-value of the (n,p) reaction. It is also likely
that a run period of that time will produce 3–4 counts in the high-energy
neutron region above 2700 keV. Neutrons of those energies will constrain the
overall fast flux present at the detector location. Similarly, the counts in
the intermediate region, above around 266 keV neutron energy but below 1000
keV will create an important constraint in a region that has previously been
difficult to measure.

##  V Conclusions

While gaseous 3He tubes are probably better for thermal neutron measurements
due to their high surface area, our design is appropriate for cryogenic
environments and above 1 keV in neutron energy our design surpasses gaseous
tube performance. A single liquid 3He detector of dimensions
2$\times$2$\times$2 cm3 has superior efficiency to a gaseous 3He detector of
the same dimensions for $\sim$1 keV–4 MeV neutrons. Our current calculations
also indicate that in a reasonable running period (1 yr) it can make impactful
measurements in some of the lowest neutron flux environments in the world,
like SNOLAB. Based on previous measurements at Berkeley [7] the resolution is
expected to be useful for either low-flux measurements or nuclear physics beam
measurements of neutrons.

Even though gaseous detectors are not very limited in size, scaling them will
increase the radiogenic alpha background proportionally to the surface area.
The Helium and Lead Observatory (HALO)–a gaseous 3He array operating at
SNOLAB–is a good comparison [23]. The array consists of 128 gaseous 3He
detectors of length 3 m or 2.5 m and 5 cm diameter at a partial pressure of
2.5 atm. Alpha backgrounds in this array are probably as low as can be
expected for gas detectors, using a special ultra-pure nickel tubes that reach
a thorium contamination of 1 ppt [24]. The large size of the array means while
our prototype detector expects about 30 neutron events above 1 keV in one
year, the HALO array will expect approximately 11,000. However, the alpha
background is far lower in our liquid 3He prototype because of the
dramatically lower surface area and the possibility of ultra-pure copper. The
thorium contamination has been demonstrated in copper as low as 0.01–0.03 ppt
by the Majorana Demonstrator neutrinoless double-beta decay experiment [25].
The Enriched Xenon Observatory (EXO) has also demonstrated a contamination of
thorium at 1.8 ppt for Quartz–the same material we will use for the PMT window
[26]. Given those levels, we expect a signa-to-background ratio of 0.079 in
HALO but between 4.62 and 15.42 in our small 2$\times$2$\times$2 cm3
prototype, depending on the copper purity (between 0.01 ppt and 1 ppt).

Our work indicates that this topic is worth further study. In particular,
ambient gamma backgrounds must be considered as well as those that might
originate from the instrumentation of our design–like from the photomultiplier
tubes or electronics.

###### Acknowledgements.

The authors would like to thank Junsong Lin, A.J. Biffl, James deBoer, and
Matt Pyle for helpful discussions. We also gratefully acknowledge the
SPICE/HeRALD collaboration for providing us with the data from several plots
in their paper.

## References

  * Huang  _et al._ [2021] W. Huang, M. Wang, F. Kondev, G. Audi, and S. Naimi, “The ame 2020 atomic mass evaluation (i). evaluation of input data, and adjustment procedures*,” Chinese Physics C 45, 030002 (2021).
  * Wang  _et al._ [2021] M. Wang, W. Huang, F. Kondev, G. Audi, and S. Naimi, “The ame 2020 atomic mass evaluation (ii). tables, graphs and references*,” Chinese Physics C 45, 030003 (2021).
  * Beimer, Nyman, and Tengblad [1986] K.-H. Beimer, G. Nyman, and O. Tengblad, “Response function for 3he neutron spectrometers,” Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 245, 402–414 (1986).
  * Langford  _et al._ [2013] T. Langford, C. Bass, E. Beise, H. Breuer, D. Erwin, C. Heimbach, and J. Nico, “Event identification in 3he proportional counters using risetime discrimination,” Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 717, 51–57 (2013).
  * Best  _et al._ [2016] A. Best _et al._ , “Low energy neutron background in deep underground laboratories,” Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 812, 1–6 (2016).
  * Kerr [1954] E. C. Kerr, “Orthobaric densities of ${\mathrm{he}}^{3}$ 1.3°k to 3.2°k,” Phys. Rev. 96, 551–554 (1954).
  * Biekert  _et al._ [2022] A. Biekert _et al._ (SPICE/HeRALD Collaboration), “Scintillation yield from electronic and nuclear recoils in superfluid ${}^{4}\mathrm{He}$,” Phys. Rev. D 105, 092005 (2022).
  * [8] Cryomech, “Closed-Cycle 1L Cryocooler,” https://www.cryomech.com/articles/closed-cycle-1k-cryocooler/, online; accessed 26 June 2023.
  * McKinsey  _et al._ [1997] D. McKinsey _et al._ , “Fluorescence efficiencies of thin scintillating films in the extreme ultraviolet spectral region,” Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 132, 351–358 (1997).
  * Dokania  _et al._ [2014] N. Dokania _et al._ , “Study of neutron-induced background and its effect on the search of 0nbb decay in 124sn,” Journal of Instrumentation 9, P11002 (2014).
  * Cooley  _et al._ [2018] J. Cooley, K. Palladino, H. Qiu, M. Selvi, S. Scorza, and C. Zhang, “Input comparison of radiogenic neutron estimates for ultra-low background experiments,” Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 888, 110–118 (2018).
  * Mei and Hime [2006] D.-M. Mei and A. Hime, “Muon-induced background study for underground laboratories,” Phys. Rev. D 73, 053004 (2006).
  * Browne [1999] M. Browne,  _Preparation for Deployment of the Neutral Current Detectors (NCDs) for the Sudbury Neutrino Observatory_ , Ph.D. Dissertation (1999).
  * Ciani  _et al._ [2021] G. F. Ciani _et al._ (LUNA Collaboration), “Direct measurement of the ${}^{13}\mathrm{C}(\alpha,n)^{16}\mathrm{O}$ cross section into the $s$-process gamow peak,” Phys. Rev. Lett. 127, 152701 (2021).
  * Wiescher, deBoer, and Görres [2023] M. Wiescher, R. J. deBoer, and J. Görres, “The resonances in the 22Ne+alpha fusion reactions,” The European Physical Journal A 59, 11 (2023).
  * Agnese  _et al._ [2017] R. Agnese _et al._ (SuperCDMS Collaboration), “Projected sensitivity of the supercdms snolab experiment,” Phys. Rev. D 95, 082002 (2017).
  * Wilson  _et al._ [2002] W. B. Wilson _et al._ , “Sources 4c: a code for calculating ($\alpha$,n), spontaneous fission, and delayed neutron sources and spectra,” Tech. Rep. (United States, Jan 2002) lA-UR–02-1839.
  * SNO [2006] “Snolab users handbook rev. 2,” Tech. Rep. (2006).
  * Duderstadt and Hamilton [1976] J. Duderstadt and L. Hamilton,  _Nuclear Reactor Analysis_ (Wiley, 1976).
  * Berger, Coursey, and Zucker [2017] M. Berger, J. Coursey, and M. Zucker, “Estar, pstar, and astar: Computer programs for calculating stopping-power and range tables for electrons, protons, and helium ions (version 2.0.1).” Tech. Rep. (National Institute of Standards and Technology, Gaithersburg, MD., Jul 2017).
  * Iwamoto  _et al._ [2023] O. Iwamoto _et al._ , “Japanese evaluated nuclear data library version 5: Jendl-5,” Journal of Nuclear Science and Technology 60, 1–60 (2023), https://doi.org/10.1080/00223131.2022.2141903 .
  * Zyla  _et al._ [2020] P. A. Zyla _et al._ (Particle Data Group), “Review of Particle Physics,” PTEP 2020, 083C01 (2020), pp. 536–538.
  * Bruulsema [2017] C. Bruulsema,  _Calibration and commissioning of the Helium and Lead Observatory_ , Master’s Thesis (2017).
  * Boger  _et al._ [2000] J. Boger _et al._ , “The sudbury neutrino observatory,” Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 449, 172–207 (2000).
  * Abgrall  _et al._ [2016] N. Abgrall _et al._ , “The majorana demonstrator radioassay program,” Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 828, 22–36 (2016).
  * Leonard  _et al._ [2008] D. Leonard _et al._ , “Systematic study of trace radioactive impurities in candidate construction materials for exo-200,” Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 591, 490–509 (2008).