# Check Mate: Prioritizing User Generated Multi-Media Content for Fact-
Checking

Tarunima Prabhakar, Anushree Gupta, Kruttika Nadig, Denny George
Tattle Civic Technologies
<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>
###### Abstract

Volume of content and misinformation on social media is rapidly increasing.
There is a need for systems that can support fact checkers by prioritizing
content that needs to be fact checked. Prior research on prioritizing content
for fact-checking has focused on news media articles, predominantly in English
language. Increasingly, misinformation is found in user-generated content. In
this paper we present a novel dataset that can be used to prioritize check-
worthy posts from multi-media content in Hindi. It is unique in its 1) focus
on user generated content, 2) language and 3) accommodation of multi-modality
in social media posts. In addition, we also provide metadata for each post
such as number of shares and likes of the post on ShareChat, a popular Indian
social media platform, that allows for correlative analysis around virality
and misinformation. The data is accessible on Zenodo
(https://zenodo.org/record/4032629) under Creative Commons Attribution License
(CC BY 4.0).

## Introduction

Misinformation has emerged as an unfortunate by-product of growing social
media usage. Over the last few years, rapid Internet penetration and mobile
phone adoption in developing countries such as India has led to a rapid
increase in social media users. Many such users use regional Indian languages
as their primary language of communication. The resultant misinformation has
had life threatening consequences (?). On chat apps such as WhatsApp, user-
generated multi-media content has emerged as an important category of
misinformation. Figure 1 shows an example of a heavily circulated post on
WhatsApp, where a video is recirculated with a misleading text message. The
video and text together constitute a post that needs to be verified.

Misinformation is contextual- the content and form of misinformation varies by
topic of misinformation, platform of circulation, as well as the demographic
targeted with that misinformation (?). The last few years have seen efforts
towards characterizing and detecting ‘fake news’ on social media. There is a
consistent need for datasets emerging from diverse contexts to better
understand the phenomenon.

Fact-checking has emerged as an important defense mechanism against
misinformation. Social media platforms rely on third party fact checkers to
flag content on them. Fact checking groups also run
tiplines111https://techcrunch.com/2019/04/02/whatsapp-adds-a-tip-line-for-
checking-fakes-in-india-ahead-of-elections/ on WhatsApp through which they can
source content that requires fact checking. The amount of content received by
fact checkers however, significantly exceeds journalistic resources.
Automation can support fact checkers by flagging content that is of higher
priority for fact-checking. Extracting claims and assessing check-worthiness
of posts is one way to help prioritise specific content for fact-checking (?).
In case of news articles and speeches, these claims appear as factual
statements made by writers or speakers. User generated content, however, is
often multi-modal. Videos and images are supported with additional text or
media to give false context, make false connections or create misleading
content (?). In Figure 1, the text message provides false context to an
authentic video. In Figure 2, names and actions are attributed to people
identified in images. In this specific case, the image of the individual in
the top right is of a fashion designer and not of a politician, as claimed in
the post.

Previous work has predominantly focused on claims detection for single media
sources such as tweet texts, speeches and news articles (?; ?). Additionally,
most research has focused on English language content. The rise in multi-modal
user generated content makes it imperative to consider claims in multi-media
posts. Furthermore, with regards to prioritizing multi-media content for fact
checking, there is a need to deliberate on what is check-worthy in multi-media
content. For example, selfie video claim lesser journalistic integrity than
televised news coverage and might be of lower priority for fact checking.

In this paper, we present a unique dataset of multi-modal content in Hindi
that can be used to train models for identifying check-worthy multi-modal
content in the language. It is unique in its 1) focus on user generated
content, 2) language and 3) claims detection in multi-modal content. In
addition to the annotations, we also provide metadata for each post- such as
time-stamp of creation; number of shares and likes; and the tags that
accompany them on a popular Indian social media platform, ShareChat. Table 1
summarizes the different media types in the dataset. Table 2 provides an
overview of the fields in the dataset. In the subsequent section, we provide
background on data collection and discuss related work. We then describe the
process of annotation, the results from the annotations; and possible use
cases and limitations.

|  Count
---|---
Total Number of Posts |  2200
Annotated by Single Person |  1800
Annotated by Two People |  4000
Posts with primary media type: Image |  1354
Posts with primary media type: Video |  659
Posts with primary media type: Text |  187
Table 1: Dataset Summary Field Category |  Field Names
---|---
Fields Describing Post (from platform) |  bucket name, media type, caption, tag name, tag translation, text
Annotated Fields |  verifiable claim, contains video, contains image, visible source, contains relevant meme
Metadata Fields (from platform) |  number of external shares, likes, estimated timestamp of creation
Other Fields |  Unique Post ID, Annotator Label
Table 2: Dataset Fields Overview Figure 1: False Context in Multi-Modal
Content Figure 2: Text Embedded in Image

## Related Work

### Background on Sharechat

The posts in this dataset were collected from an Indian social media platform
called ShareChat. The platform has over 60 million monthly active users and is
available in fourteen regional Indian languages (?). It provides users the
option to share other users’ content on their profiles, as well as share
content to other apps such as WhatsApp. (?) provide a detailed summary of
linguistic and temporal patterns observed in the content on the platform. For
this dataset, we focus specifically on content in Hindi. With 500 million
speakers, Hindi is the most commonly spoken Indian language (?).

### Characterizing Check-Worthiness

Claim detection is a rich area of research (?; ?). Given their importance to
the American electoral process, US presidential debates have received
significant attention for claim detection (?; ?; ?). Some research has focused
on claims in user generated content on Twitter. This work however is still
focused on textual claims on the platform (?; ?).

The work of (?) is noteworthy for its focus on textual claims about images. In
the field of misinformation detection, multi-media content has received more
attention in the last few years. (?) provide a dataset of one million multi-
modal posts from Reddit, classified into different types of misinformation.
(?) provide a dataset of Brazilian and Indian election related images
circulated on WhatsApp, that were found to be misinformation. (?) analyze fake
images on Twitter during Hurricane Sandy.

More broadly, literature describing and classifying misinformation highlights
features that could help in prioritization of multi-media user-generated
content for fact checking. (?) focus specifically on fake news articles which
contain two major components- publisher and content. They classify news
features using four attributes: source, headline, main text and image/video
contained in the article. In their characterization of fake news, the
image/video is considered as a supporting visual cue to the main news article.
This is different from user generated content on chat apps, where the text can
be supplementary context for an image or video. (?) propose a eight category
typology for identifying ’fake news’ which ranges from citizen journalism to
satire and commentary. They further identify content attributes such as
factuality, message quality, evidence, source and intention to characterize
these eight categories.

Since a lot of misinformation research has focused on English content, we also
highlight here work that focuses on non-English media. The dataset provided by
(?) has images with content in Portugese and Indian languages. (?) look at
language features across English, Spanish and Portugese for fake news
detection.

## Data Collection

To source user-generated multi-media content, we scraped content from
ShareChat. On ShareChat, content is organized by ‘buckets’ corresponding to
topic areas. Each bucket has multiple tags. Figure 3 shows the conceptual
organization of content on the platform. We scraped content daily from the
‘Health’, ‘Coronavirus’ and ‘Politics and News’ buckets in Hindi from March 21
2020 to August 4 2020. We focused on these buckets since politics and health
have been observed to be the most common themes of misinformation in India
(?). This resulted in over 200,000 unique posts. We randomly sampled from this
bigger corpus to prepare the dataset presented here. The platform provides a
number of additional data points such as the number of shares and likes, that
provide some social context for a post. The platform also assigns a ‘media
type’ to each post which could be text, image, video or link. Regardless of
the media type, there is an option for users to provide captions for a post,
which can contain large text descriptions. For our sampling, we ignore posts
of media-type ‘link’ since these redirect to other websites and are therefore
not within the purview of our focus on user generated content on the platform.

Figure 3: ShareChat Content Tree Structure

## Qualitative Analysis

### Open Coding

We use ‘Open coding’ methodology to identify features that can help prioritize
multi-media content for fact checking. Open Coding is an analytic qualitative
data analysis method to systematically analyze data (?). In the first round,
two researchers from the team loosely annotated and described 600 posts to
familiarize themselves with the data. In the second phase, we described 300
posts in detail. We converged on sixteen non-mutually exclusive categories of
multi-media posts that could be verified. These included posts that contained
statistical claims (such as Covid fatality numbers); quotes attributed to
noteworthy individuals; and newspaper clips and edited recordings from
televised news reporting.

We also recognized user generated videos claiming to debunk misinformation as
a separate category of content- the period of data collection coincided with
Covid-19 and some posts were warnings about misinformation on the pandemic.

From these sixteen categories we converged on the following broad attributes
of check-worthy content:

  * •

The implied/explicit source of the content: Similar to (?; ?) we find the
_implied_ source of the content to be an important attribute for prioritizing
content for fact checking. We identified five common sources of data (see
Table 3) that are ascribed differing levels of authenticity. Circulars and
messages attributed to government sources for example are more likely to be
believed than anonymous sources.

  * •

The kind of verifiable claim: (?) specifically characterize sentences from US
presidential debates into three categories- non-factual statement, unimportant
factual statement and check-worthy factual statement. Since this dataset is
multi-modal, we found it useful to further qualify check-worthy factual
statements. We define three broad categories of check-worthy claims in multi-
media content: 1. statistical/numerical claims. 2. world events or places
about noteworthy individuals. 3. Other factual claims such as home remedies,
cures and nutritional advice.

  * •

For video content, the ‘intentionality’ of creation, as inferred from the
video: There are multiple ways to categorize user generated video content. We
broadly divide the content into two groups- videos where the person in the
video is performing for the camera, and videos where the recording is
incidental to the event. Given the ability to provoke, we find it useful to
highlight violent incidents portrayed in videos as a third category. Notably,
we do not consider an image montage to be a video, even if the media type as
declared by the platform is ’video’.

  * •

Specifically for non digitally-manipulated images, the topic of images: While
a lot of content on ShareChat is classified as ‘image’, not all of it is
unidentified. A lot of ‘Good Morning Wishes’ memes use generic background
images. In our framework, such images are not considered important for fact
checking. We draw a distinction between digitally manipulated images and non-
digitally manipulated images.

We realize that within non-digitally manipulated images, the topic of the
image is an important feature that could help prioritize a content for fact
checking. This however would require developing a content taxonomy of social
media content. This is an important and complex research area and merits
dedicated attention. We found this taxonomy to be beyond the ambit of this
specific work. We loosely classified non-digitally manipulated images into
those that contain human figures and those that don’t. We found it useful to
flag images with human figures in them, since the context ascribed to the
photograph can be verified.

  * •

Culturally Relevant Memes: In the absence of a relevant taxonomy, we defined a
broad binary category for ‘relevant memes’. For the purposes of this
annotation, relevant memes are those that reference security forces, religious
pride, national pride, political party representatives, reverential posts
about a profession, election campaigning or current national news. While a
more detailed taxonomy is needed, this broad descriptor of culturally
important themes, when combined with other attributes such as source and
verifiable claims, can become important in highlighting a post that needs to
be fact-checked.

(a)

(b)

Figure 4: (a) Post Attributed to Government Body (Niti Aayog) (b) A Screen
Grab of TV News With Factual Claims

(a)

(b)

Figure 5: (a) An Unidentified Image with Text Description About an Oil Well
Accident (b) An Image with Text Description about Dr. Bhimrao Ambedkar

Table 3 summarizes these five attributes.

Attribute |  Options
---|---
Implied or Explicit Source of Content(mutually-exclusive) |  Print/TV Media: Newspaper clips or screen grabs from print or TV media sources. Examples of TV media are BBC, CNN, Aaj Tak (Indian); examples of print media: The Times of India, Dainik Jagran, Rajasthan Times. See Figure 4.b
Digital Only News Outlets such as Huffington Post, Editor ji, myUpchar.
Digital Content Providers such as Buzzfeed Goodful, or Instagram and Facebook
pages.
Government/Public Authority: Government logos or stamps visible in the post or
content attributed to spokespersons, officials, civil servants/bureaucrats,
other public office holders. OR Multilateral institutions and international
public bodies such as WHO, ILO and World Bank. See Figure 4.a
Anonymous/Social Media Users. See Figure 5.a
Other
Type of Factual Claim (not mutually-exclusive) |  Statistical or numerical claim. See Figure 4.a
Descriptions of real world places, events or a noteworthy individual(s). See
Figure 5.b
Other factual claims
Intentionality Portrayed in the Video (mutually-exclusive) |  Person is Performing for Camera
Contains Violent Incident
Other
Non Manipulated Images |  Contains Humans. See Figure 5.b and Figure 4.1
Does not Contain Humans. See Figure 5.a
Contains Relevant Memes? |  Yes/No
Table 3: Annotation Guideline

### Closed Coding

Based on the above mentioned guidelines, the two researchers independently
labeled posts in batches of fifty to refine these definitions. The framework
developed was expanded into an annotation guideline with descriptions of each
of the attributes to be labeled as well as sample annotations for thirteen
posts. The annotation guide is provided with the dataset. The annotation
guideline was opened to two additional members of the Tattle team, who were
not a part of the open coding stage. By virtue of their work, each member is
familiar with the misinformation challenge in India, but not equally familiar
with the nature of the content circulated. The four person annotation team was
then split into two pairs. To estimate agreement across annotators, each
person in the pair annotated 200 hundred posts common with the other member of
the pair. Table 4 lists the agreement scores for the two pairs of annotators
across the different attributes. The agreement for video type and image type
attribute was substantial (greater than 0.6) for both pairs. Annotators
strongly agreed on a post containing a verifiable claim as well. This can be
used to train models for claim detection on multi-media posts. There was far
weaker agreement on the specific kind of factual claim, when a post did
contain one. Finally, on the implied or explicit source of content, one pair
had moderate agreement while the second had weak agreement.

|  Video Type |  Image Type |  Source |  No Factual Claim |  Statistical Claim |  Real Event, Place, Person |  Other Factual Claim |  Relevant Memes
---|---|---|---|---|---|---|---|---
Pair 1 |  0.74 |  0.68 |  0.5 |  0.76 |  0.65 |  0.30 |  0.54 |  0.70
Pair 2 |  0.85 |  0.68 |  0.35 |  0.83 |  0.33 |  0.31 |  0.50 |  0.61
Table 4:  Agreement

## Annotation Procedure

After observing moderate to high agreement across four fields, each annotator
proceeded to annotate 450 additional posts. This resulted in a total of 1800
posts uniquely annotated by one of the four annotators. We developed an in-
house user interface for annotating specific
posts222http://annotation.test.tattle.co.in.s3-website.ap-
south-1.amazonaws.com/. The interface is a Gatsby App built using React and
Grommet UI library. Through the interface, annotators could load post related
data from a JSON file and preview the media and metadata associated with it.
Annotators could navigate from one post to another using ‘previous’ and
‘next’. On clicking ‘next’ annotated labels gets saved in the same JSON file.
(See figure 6)

Figure 6: Annotation Interface

Attributes such as video style, image content and source that had mutually
exclusive options were enforced as single option questions in the interface.
The attribute about the kind of factual claim was listed as a check-box, since
a post can contain multiple kinds of factual claims.

After all the annotations were completed, the JSON files were converted to CSV
files for greater legibility and ease of sharing.

## Dataset Description

### Data Sample

In this dataset we share 400 posts that were annotated by two people each in
the closed coding phase, as well as 1800 posts annotated by four separate
annotators. In total, there are 2200 annotated posts shared in this dataset.

Before expanding on the annotations, we’ll describe the data fields scraped
from ShareChat. The metadata about a post scraped from the platform includes:

  * •

timestamp: time at which the post was created on the platform

  * •

media type: can be image, video or text

  * •

text: text content if media type of post is text.

  * •

caption: text caption to images and videos.

  * •

external shares: number of times the post was shared from the platform to an
external app such as WhatsApp

  * •

number of likes: number of times the post was liked on ShareChat

  * •

tags: the tags attached to the post by the user. See figure 3

  * •

bucket name: the bucket under which this post was classified by the platform.
See figure 3

Regardless of the media type attributed by the platform, users can add
additional text for images and videos which is captured in the ‘caption’ field
for each post.

(a)

(b)

Figure 7: (a) Visible Source (b) Type of Non-Manipulated Images

(a)

(b)

Figure 8:  (a) Type of Video (b) Proportion of Posts Containing Verifiable
Claims

### Annotated Data

The fields developed in the closed coding stage were annotated as following:

  * •

verifiable claim: a list of kinds of verifiable claims contained in the post.
If no claim was found, the post is labeled as ‘No’. The claims can be those
listed in Table 3. Since a post can have more than one kind of claim, the
annotated field is a list of claims.

  * •

contains video: if the post contains a video, it can be marked as ‘Person
Performing for Camera’ or ‘Contains Violent Incident’. A video that doesn’t
fall in these two categories is marked as other. A post that does not contain
a video is marked as ‘No’.

  * •

contains image: A post is said to contain an image if the image is not
digitally manipulated. Within an image, a post can be marked as ‘containing
human figures’ or ‘not containing human figures’. A post that does not contain
an image is marked as ‘No’.

  * •

visible source: Marked as one of the sources listed in Table 3

  * •

contains relevant meme: a binary category which is marked yes if the post is a
text or image (not video) based meme about a culturally relevant topic.

Figure 8 summarizes the attributes of the annotated posts (n=1800) that had
high agreement in closed coding stage. Even though a majority of posts are
labeled as images by the platform, only forty percent of the posts have non-
manipulated images that can be fact checked (see Figure 7(b)). In this sample
of data, for a majority of posts, the visible source of the post was the
platform user (see Figure 7(a)). It must be clarified that the user could have
picked up their content from other established sources such as online websites
and newspapers. Those sources however were not visible in the post. Part of
the challenge of fact-checking user generated content is identifying sources
from which content emerges. The high prevalence of user generated content
without a recognized source, emphasizes the need for systems that prioritize
this content for fact checking.

## Dataset Structure

The Tattle CheckMate Database consists of two folders. The first folder
contains annotations from the closed coding phase. The second folder contains
annotations of 1800 posts done by single annotators. The dataset also contains
a README file that describes the folder structure and fields in the datasets.

## Ethical Considerations

### Data Collection and Sharing

While content creators need to be logged into the platform to upload posts,
anyone with access to the post URL can view it. We thus treat the posts as
content in the public domain that can be scraped and researched. We recognize
that this content may contain images and videos of platform users and that
opening the data can introduce some risk of identification of people in these
posts. We have however mitigated this risk by removing the original source URL
and sharing only the post. We also recognize that the violent imagery in some
of the posts could be disturbing for users of this data. While labels such as
those for violent videos could serve as an alert or trigger warning for some
posts, we recognize that this is not comprehensive. Disturbing imagery is
however unavoidable in the problem space that this dataset aims to intervene
in. It is, in fact, one aspect of social media use that we hope this dataset
can inform.

### FAIR Requirements

This dataset adheres to FAIR principles (Findable, Accessible, Interoperable
and Re-usable). The dataset is Findable and Accessible through Zenodo on
https://zenodo.org/record/4032629 and licensed under Creative Commons
Attribution License (CC BY 4.0). The data is shared as a set of CSV files with
the referenced media items in the folder. Thus the data is reusable and inter-
operable.

## Conclusion

To the best of our knowledge, this dataset is a first of its kind in focusing
on claims detection and prioritization of multi-media content for fact
checking. This dataset provides a starting framework for features in different
mediums that can help in flagging content. Future work can expand on this
framework for more granular features across the different modalities. This
dataset is also novel in its cultural and linguistic context and can be used
for a range of open research problems:

### Training Models to Prioritize Multi-modal Content for Fact-
Checking/Verification

This dataset can be used for building models that can highlight check-worthy
multi-modal content, especially in Hindi. This data can also supplement other
datasets on misinformation. (?) summarize datasets that are focused on the
automation of fake news detection.

### Understanding Content Virality

This dataset provides content features as well as some social context such as
the number of shares, likes and tags. It can be used to better understand the
relationship between the content and style of post and its popularity.

This work provides a framework for working with multi-modal content, but it
also highlights a number of open research questions such as the need to build
a taxonomy of image based content, and a more detailed typology of verifiable
claims that are relevant for prioritizing of multi-modal content for fact
checking. Future research can expand on this work to support such questions
with other datasets.

## Acknowledgements

We thank Dr. Sandeep Avula and Dr. Swair Shah for their continuous feedback in
creation of this dataset. This work was partially supported by the ‘The Ethics
and Governance of the Artificial Intelligence Fund’ of the AI Ethics
Initiative.

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# Internal Structure of Protocluster Galaxies: Accelerated Structural
Evolution in Overdense Environments?11affiliation: Based on observations with
the NASA/ESA Hubble Space Telescope, obtained at the Space Telescope Science
Institute, which is operated by the Association of Universities for Research
in Astronomy, Inc., under NASA contract NAS 5-26555

Andrew W. Zirm22affiliation: Dark Cosmology Centre, Niels Bohr Institute,
University of Copenhagen, Juliane Maries Vej 30, DK-2100 Copenhagen, Denmark;
<EMAIL_ADDRESS><EMAIL_ADDRESS>Sune Toft22affiliation: Dark
Cosmology Centre, Niels Bohr Institute, University of Copenhagen, Juliane
Maries Vej 30, DK-2100 Copenhagen, Denmark<EMAIL_ADDRESS><EMAIL_ADDRESS>Masayuki Tanaka33affiliation: Institute for the Physics
and Mathematics of the Universe, The University of Tokyo, 5-1-5 Kashiwanoha,
Kashiwa, Chiba 277-8583, Japan<EMAIL_ADDRESS>
###### Abstract

We present a high spatial-resolution HST/NICMOS imaging survey in the field of
a known protocluster surrounding the powerful radio galaxy MRC1138-262 at
$z=2.16$. Previously, we have shown that this field exhibits a substantial
surface overdensity of red $J-H$ galaxies. Here we focus on the stellar masses
and galaxy effective radii in an effort to compare and contrast the properties
of likely protocluster galaxies with their field counterparts and to look for
correlations between galaxy structure and (projected) distance relative to the
radio galaxy.

We find a hint that quiescent, cluster galaxies are on average less dense than
quiescent field galaxies of similar stellar mass and redshift. In fact, we
find only two (of nine) quiescent protocluster galaxies are of simliar density
to the majority of the massive, quiescent compact galaxies (SEEDs) found in
several field surveys. Furthermore, there is some indication that the
structural Sersic $n$ parameter is higher ($n\sim 3-4$) on average for cluster
galaxies compared to the field SEEDs ($n\sim 1-2$) This result may imply that
the accelerated galaxy evolution expected (and observed) in overdense regions
also extends to structural evolution presuming that massive galaxies began as
dense (low $n$) SEEDs and have already evolved to be more in line with local
galaxies of the same stellar mass.

###### Subject headings:

galaxies: clusters: individual (MRC1138-262) – galaxies: evolution – galaxies:
high-redshift – galaxies: structure

##  1\. Introduction

The internal spatial and velocity distribution of stars is an indicator of the
manner in which galaxies have formed, assembled and evolved. In the local
universe, tidal streams, shells and kinematically distinct cores are examples
of archeological clues to past merging and formation events (e.g., Peng et
al., 2002b; Emsellem et al., 2007; van Dokkum, 2005; Blanton & Moustakas,
2009). Even coarse measures, such as the average stellar surface mass density
within the effective radius ($\Sigma_{50}$), correlate with the star formation
rate or the mean stellar age. Giant elliptical galaxies have high stellar mass
per unit area (or volume) and show negligible current star formation while
more diffuse stellar disks and dwarf irregulars are forming stars at sometimes
prodigious rates per unit stellar mass (specific star-formation rate; sSFR).
At higher redshift, analogous relations are already in place (e.g., Franx et
al., 2008). While observationally it remains difficult to separate high-
redshift galaxies into classical Hubble-types we can now photometrically
determine redshifts, stellar masses and galaxy sizes for large numbers of
galaxies at $z\sim 2$. Such studies (e.g., Zirm et al., 2007; Toft et al.,
2007; van Dokkum et al., 2008; Toft et al., 2009; Williams et al., 2010;
Mosleh et al., 2011) have found that quiescent galaxies are in general more
dense than their star-forming counterparts.

The origin of this bi-modal distribution of galaxy properties is unclear. It
is possible that the quiescent $z\sim 2$ galaxies are more compact because
they formed when the universe was smaller and more dense and mergers were more
gas-rich. It also seems plausible that the process which quenches star
formation may be linked to a morphological change. Or perhaps the dominant
formation processes differ for galaxies with different present-day stellar
masses (Khochfar & Silk, 2006, 2009, 2011; Dekel & Birnboim, 2008; Dekel et
al., 2009)

In general terms, dissipation should result in more compact stellar cores than
dissipationless assembly (e.g., Ciotti, Lanzoni, & Volonteri, 2007; Naab,
Johansson, & Ostriker, 2009; Oser et al., 2010). Dense stellar systems, once
formed, tend to persist through successive (minor) mergers. The relatively
recent discovery of quiescent, massive and compact galaxies at $z\sim 2$
(hereafter Semi-Evolved Elephantine Dense galaxies or “SEEDs”) implies that at
least some galaxies have their origin in high-redshift ($z\mathrel{\hbox
to0.0pt{\lower 3.0pt\hbox{$\mathchar 536\relax$}\hss}\raise
2.0pt\hbox{$\mathchar 318\relax$}}4$), gas-rich mergers (Daddi et al., 2005;
Zirm et al., 2007; Toft et al., 2007; van Dokkum et al., 2008; Cimatti et al.,
2008). These mergers resulted in many stars being formed in a relatively small
volume. By $z\sim 2$ these systems have low star-formation rates and
relatively high stellar masses in addition to their small sizes
($r_{e}\mathrel{\hbox to0.0pt{\lower 3.0pt\hbox{$\mathchar
536\relax$}\hss}\raise 2.0pt\hbox{$\mathchar 316\relax$}}1$kpc). The SEEDs
therefore have extreme internal stellar mass densities They do not fall on the
size-mass relation defined by local galaxies. Their evolution from $z\sim 2$
to $z=0$ is therefore a puzzle.

The most massive galaxies in the present-day universe are located at the
centers of rich clusters. These galaxy overdensities were statistically the
first to separate from the Hubble flow and collapse and are therefore believed
to follow an accelerated timeline for the process of galaxy formation. There
is some observational evidence that galaxies in the progenitors of clusters,
protoclusters, do have significantly older stars and higher masses than
galaxies in the field at similar redshifts (Steidel et al., 2005; Tanaka et
al., 2010). Might cluster galaxies, having formed earlier, be even more dense
than field SEEDs? Or, alternatively, the ’fast-forward’ evolution of cluster
galaxies may lead to lower density galaxies in protoclusters compared to their
field counterparts. It is interesting, then, to look for dense SEED galaxies
in protoclusters at redshift $z\sim 2$.

We have undertaken a NICMOS imaging program to study the red galaxy population
in a protocluster at $z=2.16$. Broad and narrow-band imaging, both in the
optical and near-infrared, of the field surrounding the powerful radio galaxy
MRC 1138-262 ($z=2.16$) have identified more than 100 candidate companion
galaxies. There are surface-overdensities of both line-emitting candidates
(Lyman-$\alpha$ and H$\alpha$), X-ray point sources, sub-mm selected galaxies
and red optical–near-infrared galaxies (Pentericci et al., 2002; Kurk, 2003;
Kurk et al., 2004b; Stevens et al., 2003; Croft et al., 2005). Fifteen of the
Ly$\alpha$ and 9 of the H$\alpha$ emitters have been spectroscopically
confirmed to lie at the same redshift as the radio galaxy (Kurk et al.,
2004a). By obtaining deep images through the NICMOS $J_{110}$ and $H_{160}$
filters, which effectively span the 4000Å-break at $z=2.16$, we have
identified a large surface overdensity of red galaxies consistent with a
forming red sequence (Zirm et al., 2008). In this paper we present a more
detailed analysis of the masses and morphologies of galaxies in this field.
The article is organized as follows: in Section §2 we describe the data and
their reductions, in Section §3 we present the photometric redshifts, stellar
population models and morphological fits, in Section §4 we present the
internal stellar mass densities and other derived properties and finally in §5
we discuss these results in the context of galaxy evolution models. We use a
$(\Omega_{\Lambda},\Omega_{M})=(0.7,0.3)$, $H_{0}=73$ ${\rm km}$ ${\rm
s^{-1}}$ ${\rm Mpc^{-1}}$ cosmology throughout. At $z=2.16$ one arcsecond is
equivalent to 8.4 kpc. All magnitudes are referenced to the AB system (Oke,
1974) unless otherwise noted.

##  2\. Observations and Data Reductions

###  2.1. NICMOS Imaging

The NICMOS instrument on-board HST is capable of deep near-infrared imaging
over a relatively small field-of-view ($51\arcsec\times 51\arcsec$). In the
case of MRC 1138-262, we know that galaxies are overdense on the scale of a
few arcminutes (Kurk et al., 2004b; Croft et al., 2005) and are thus well-
suited for observations with NICMOS camera 3 on HST. We used 30 HST orbits to
image seven overlapping pointings in both filters and one additional pointing
in $H_{160}$ alone. These observations reach an AB limiting magnitude
($m_{10\sigma}$; 10$\sigma$, $0\farcs 5$ diameter circular aperture) of
$m_{10\sigma}=24.9$ mag in $J_{110}$ and $m_{10\sigma}=25.1$ mag in $H_{160}$.
The same field was imaged in the $g_{475}$ ($m_{10\sigma}=27.5$ mag) and
$I_{814}$ ($m_{10\sigma}=26.8$ mag) filters using the Wide-Field Channel of
the Advanced Camera for Surveys on HST as part of a Guaranteed Time program (#
10327; Miley et al. 2006).

The NICMOS images were reduced using the on-the-fly reductions from the HST
archive, the IRAF task pedsky and the dither/drizzle package to combine the
images in a mosaic. The dither offsets were calculated using image cross-
correlation and were refined iteratively. Alignment of the pointings relative
to each other was accomplished using a rebinned version of the ACS $I_{814}$
image as a reference. The final mosaic has a pixel scale of $0\farcs 1$.
Galaxies were selected using the $H_{160}$-band image for detection within
SExtractor (Bertin & Arnouts, 1996). We used a $2.2\sigma$ detection threshold
with a minimum connected area of 10 pixels. We also corrected the NICMOS data
for the count-rate dependent non-linearity (de Jong, 2006). Total galaxy
magnitudes were estimated by using the MAG_AUTO values from SExtractor. We
show the outline of the NICMOS mosaic in Figure 1 along with the positions of
the radio galaxy (yellow star) and star-forming (blue circles) and quiescent
(red circles) protocluster galaxies.

The $J_{110}-H_{160}$ colors were determined by running SExtractor (Bertin &
Arnouts, 1996) in two-image mode using the $H_{160}$ image for object
detection and isophotal apertures. The $J_{110}$ image was PSF-matched to the
$H_{160}$ band. We also incorporated the two ACS bands (Miley et al., 2006),
the Spitzer IRAC bands, $U_{n}$ (Zirm et al., 2008) and $V$ bands from
Keck/LRIS, $z$ and $R$ from VLT/FORS2 (Kurk et al., 2004b, a), $H$ band from
NTT/SOFI and $J$ and $Ks$ from Subaru/MOIRCS (Kodama et al., 2007). The
assembly of the merged multi-band catalog is detailed in Tanaka et al. 2010.

Figure 1.— Outline of the NICMOS mosaic. The red and blue points mark the
locations of the quiescent and star-forming cluster galaxies respectively. The
yellow star is the radio galaxy MRC1138-262.

###  2.2. FIREWORKS Survey Data and Literature Sample

The FIREWORKS data are described in detail in Wuyts et al. (2008). In brief,
the survey is $K_{S}$-band selected to $5\sigma$ depth of 24.3 (AB) over an
area of 113$\square\arcmin$. In addition to the deep $K_{S}$ band data there
is high-quality imaging in each of the $U$, $B$, $V$, $I$, $i$, $z$, $J$, $H$,
the four Spitzer/IRAC bands and the $24\mu$m Spitzer/MIPS band. The combined
multi-band catalog has been used to measure precise photometric redshifts,
galaxy sizes (Toft et al., 2009) and to model the spectral energy
distributions to derive stellar masses, ages and star-formation rates (Damen
et al., 2009). We have made three cuts to the FIREWORKS sample to ensure that
we are making appropriate field-to-cluster comparisons. First, since we are
comparing galaxy sizes (densities) we require that the galaxies are bright
enough to have a reliable size measurement in these ground-based data. Based
on the comparison of size measurements from VLT/ISAAC and HST/NICMOS for the
same galaxies, Toft et al. (2009) found that at $K\sim 21.5-22.0$ the scatter
between these two size determinations increases significantly. We therefore
select only $K<21.5$ galaxies from FIREWORKS. Next, we have made a photometric
redshift cut $1.9<z<2.6$ to select galaxies within the field at roughly the
same epoch as the protocluster galaxies. Finally, we select the quiescent
field population on the basis of the specific star-formation rate (log(sSFR)
$<-11$ yr-1). We note that after these cuts, the stellar mass distribution
remains similar to our protocluster galaxy sample.

For further comparison to our protocluster field data, we have compiled a
sample of $z\sim 2$ quiescent galaxies published in the literature. We used
four references for this sample: Cassata et al. (2010), van Dokkum et al.
(2008), Mancini et al. (2010) and Saracco, Longhetti, & Andreon (2009).
Saracco, Longhetti, & Andreon (2009) used HST/NICMOS Camera 3 as we have,
Mancini et al. (2010) used HST/ACS imaging, van Dokkum et al. (2008) studied
HST/NICMOS Camera 2 imaging while Cassata et al. (2010) use imaging from the
WFC3/IR channel on HST.

We have attempted to translate these published stellar mass estimates to the
same IMF (Salpeter) and to the same stellar population synthesis model set
(Maraston 2005). We have used the analyses of Salimbeni et al. (2009; see
their Fig. 1) to derive mean corrections between model sets. The adopted IMF
also affects the derived star-formation rates. The offsets in this quantity
are similar in magnitude to the systematic shift in derived stellar mass
(e.g., Erb et al., 2006; Nordon et al., 2010), so the specific star formation
rate (i.e., the ratio of star-formation rate to stellar mass) should be
effectively unchanged.

Figure 2.— Upper panels, left to right: Galaxy image, GALFIT best-fit model
and image residuals after subtraction of the model. Lower panel: Photometry
and best-fit SED model from FAST (red circles and solid black line Kriek et
al., 2009). The shaded regions represent the photometric redshift probability
distribution (upper scale) centered at rest-frame 4000Å.

##  3\. Analysis

Here we combine the multiband photometric catalog and the NICMOS high spatial-
resolution imaging to derive physical parameters for individual galaxies. We
pare down the total NICMOS galaxy sample to those which have a high quality-
of-fit for the photometric redshift (using EAZY, Brammer, van Dokkum, & Coppi
(2008)), the spectral energy distribution (using FAST, Kriek et al. (2009))
and 2D surface-brightness profile fit (using GALFIT, Peng et al. (2002a)).
This reduces the galaxy sample from the $H_{160}$-band detected total of 711.
We further restrict our attention to those galaxies which most likely lie
within the known protocluster (see Section §3.4).

Figure 3.— Fig. 1 cont.

###  3.1. Photometric Redshifts

The thirteen filter photometric catalog (Tanaka et al., 2010) was used to
determine galaxy photometric redshifts. We used the public code, EAZY, to fit
a set of model templates to each galaxy’s photometric data (Brammer, van
Dokkum, & Coppi, 2008). We required that each galaxy have at least 5 colors
measured for the photometric redshift fit. The set of SED templates we used
included both galaxy spectral energy distributions and a narrow emission line
spectrum. EAZY uses all linear combinations of the input templates to find the
best photometric redshift fit. For each fit, EAZY produces the full redshift
probability distribution (see Figs. 2 \- 4). We are most interested in the
galaxies detected in the relatively small ($\sim 5\square\arcmin$), but deep,
NICMOS $H_{160}$-band area. Therefore, we have only included sources detected
in the $H_{\rm 160}$ NICMOS images. For the target redshift of $z=2.2$, the
primary strong spectral feature covered by the photometric data is the 4000Å
break. We note that even with 13 bands of imaging, photo-$z$s are not
sufficiently precise to determine whether a galaxy is inside the cluster or
not. There are 12 spectroscopically confirmed (emission line) protocluster
members within the NICMOS mosaic. Of these, four $H\alpha$ and one Ly$\alpha$
emitters have well-determined photometric redshifts (the remaining members are
generally too faint to have detections in enough bands). Four of the five
photo-$z$s are around $z\sim 2.1$, ranging from $1.8$ to $2.1$. There is one
clear outlier, the $H\alpha$ emitter with $z_{\rm phot}=0.33$.

###  3.2. Stellar Population Modeling

Figure 4.— Fig. 1 cont.

Using the calculated best-fit photometric redshifts, we used FAST (Kriek et
al., 2009) to fit stellar population model templates to the rest-frame
photometry. These templates consist of a grid of models drawn from the
Maraston (2005) set. We chose to use the Salpeter IMF, exponentially declining
star-formation histories with $\tau$ varying between $10^{7}$ and $10^{10}$
years and $A_{V}$ between $0$ and $3$ magnitudes. FAST calculates the best-
fitting model template among the grid and thereby derives a luminosity-
weighted mean stellar age, stellar mass, star-formation rate and extinction
for each galaxy. FAST also outputs the 1$\sigma$ error estimates for each of
these fit parameters. We show the derived masses and their errors for the
protocluster galaxies in Table 1. We note that the star-formation rates from
SED fitting are equivalent to a dust-corrected rest-frame UV SFR and that none
of our quiescent protocluster galaxies have significant detections in the MIPS
24 $\mu{\rm m}$ image (20693, PI: Stanford). Since we are only concerned with
differentiating the quiescent and star-forming galaxies, our results are
sensitive only to catastrophic errors in these SFR determinations.

###  3.3. NICMOS Galaxy Sizes and Morphologies

NICMOS camera 3 provides good angular resolution over its field-of-view (PSF
FWHM $\approx 0\farcs 27$). To exploit this resolution we have used the GALFIT
code (Peng et al., 2002a) to fit analytic Sérsic surface-brightness profiles
(Sérsic, 1968) to all the $H_{160}\leq 26.5$ sources in our $H_{160}$-band
mosaic. We have used our own error map as input to GALFIT for properly
weighting the image pixels and have masked all neighboring objects. A model
point-spread function was created for each of these galaxies individually by
generating a TinyTim simulated PSF (Krist, 1993) at the galaxies’ positions in
each exposure and then drizzling these PSFs together in exactly the same
fashion as for the data themselves (see Zirm et al. 2007). We then executed
several different runs of GALFIT. We ran fits holding the Sérsic index
constant at $n=1$ and $4$, using a single model PSF for all galaxies, using a
stellar PSF instead of the model(s) and holding the sky value fixed at zero.
For all fits we restricted the Sérsic index, $n$, to be between 0.5 and 5. The
range of output fit values for all these different runs gives us an idea of
the variance of the derived parameters due to model assumptions. We choose the
best fit from these runs by applying the F-test to the resultant
$\chi^{2}_{\nu}$ values.

The primary source of systematic offsets in galaxy profile and size fitting is
the estimation of the local sky value. If the sky is underestimated the galaxy
size can be overestimated, particularly for small faint galaxies. Therefore,
we compare our fits where the sky level is a free parameter with those where
we explicitly fix the sky to zero. Many of the “zero-sky” fits fail to
converge, for those that do converge and have comparable chi-squared values to
the corresponding free fits, we can compare the output $r_{e}$ determinations.
It does not appear to be the case that equally good fits are obtained with and
without fitting the sky. In those five cases where an F-test shows the zero-
sky fit to be better, the sizes agree within the errors. Furthermore, none of
these where the zero-sky result is comparably good are for any or our
protocluster galaxies. We note that the GALFIT sky values while non-zero are
consistently several orders-of-magnitude smaller than the values corresponding
to galaxy pixels. This sensitivity of the fit parameters to even slight
variations in the sky highlights the importance of fitting the local sky along
with the galaxy parameters (even in sky-subtracted data).

We also note that additional scatter to the derived $r_{e}$ values introduced
by using a stellar rather than model PSF is about $10-20\%$ and therefore
comparable to the scatter on the single-fit measurements themselves.

###  3.4. Sample Selections

We use the surface-brightness profile and stellar population fits along with
the photometric redshifts to define three sub-samples of the NICMOS-detected
galaxies. We detail these in order of increasing restriction. The initial
sample is defined by a single $H_{160}$-band limit of $26.5$ (AB) and consists
of 711 galaxies.

####  3.4.1 GALFIT Sample

We ran GALFIT on the full $H_{160}$-limited galaxy sample. Using the
distribution of GALFIT $\chi^{2}_{\nu}$ values for the fits we identify
galaxies with good quality-of-fit ($\chi^{2}_{\nu}<2$; 577 of the input 711).
We then calculate the circularized $r_{e}$ ($=\sqrt{ab}$) for these well-fit
galaxies. We use this GALFIT sample in our analysis of the dependence of the
Sersic index, $n$, on radial position within the cluster (see left panel of
Fig. 7). We note that we have re-normalized the input sigma (error) maps such
that the best fits have $\chi^{2}_{\nu}\sim 1$.

####  3.4.2 Stellar Populations Sample

To select galaxies with good constraints on both the stellar mass and star-
formation rate we have selected another sub-sample for the initial 711
galaxies. For targets with both good photo-z fits and narrow redshift
probability distributions (ODDS $>0.90$, meaning 90% of the probability
distribution is contained within the $\Delta z=0.2$ around the peak value) the
sample comprises 190 galaxies out of the 711. The photometry for these targets
were fit using FAST. From this set we have identified 112 with reliably
derived parameters ($\chi^{2}_{\nu}<3.0$) based on the SED fits (for example,
see Figs. 2-4).

####  3.4.3 Probable Protocluster Galaxies

Finally, we have identified a sample of candidate protocluster members using
photometric redshifts. In lieu of spectroscopic redshifts, which are difficult
to obtain for $z\sim 2$ red, quiescent galaxies, this selection should reject
most of the interlopers. We consider only galaxies with a robust photometric
redshift (as above) that have probability $P(z)>20$% at the protocluster
redshift ($z=2.156$). We further require that these galaxies also are members
of the “Stellar Populations” sample. These selections result in a sample of 11
galaxies, nine of which are quiescent (log10 sSFR $<-11$ yr-1). We show the
galaxy cutouts, the best-fit Sersic model, the model subtraction residuals,
the broad-band SED fit and photometric redshift probability distribution for
these galaxies in Figures 2-4.

###  3.5. Stellar Mass Density

Figure 5.— Surface stellar mass density ($\Sigma$) vs. total stellar mass for
individual galaxies. The blue and black squares are from the FIREWORKS survey
(Toft et al., 2009) and other literature (van Dokkum et al., 2008; Cassata et
al., 2010; Mancini et al., 2010; Saracco, Longhetti, & Andreon, 2009)
respectively. The large blue squares are the $K>21.5$ FIREWORKS galaxies
included in the KS test. The yellow circles with error bars are the 8 likely
quiescent protocluster members. The two yellow squares are the star-forming
protocluster galaxies (the third falls at $5\times 10^{9}M_{\odot}$). The
shaded regions are the local relations for early-type (light red) and late-
type galaxies (light blue). Note that most of the protocluster members have
lower densities than their field counterparts.

The surface (volume) mass density in individual galaxies is a fundamental
property which seems to correlate directly with the absence of star formation
(e.g., Kauffmann et al., 2003; Franx et al., 2008). To measure this quantity
requires accurate total mass estimates based on either stellar velocity
dispersion or, much more commonly, the stellar mass from SED fits to the
broad-band photometry. Along with the resolved surface-brightness profile to
determine the galaxy size we can calculate the mass density. We must make the
assumption that light traces mass and that there are no strong gradients in
the stellar mass-to-light ratio, i.e., we measure $M/L$ for the integrated
galaxy light and assume that value applies to the resolved profile in a single
broad-band image.

For our galaxies, we calculate the average surface mass density ($\Sigma_{\rm
50}$ in $M_{\odot}$ ${\rm kpc}^{-2}$) within the (circularized) effective
radius ($r_{e}$) as follows:

$\Sigma_{\rm 50}=\frac{M_{\star}/2}{\pi r_{e}^{2}}$ (1)

We present our measurements in Table 1.

##  4\. Results

###  4.1. Distribution of Internal Surface Mass Densities

We have used the combination of photometric redshifts, stellar population
modeling and surface-brightness profile fits to calculate internal surface
mass densities for our protocluster sample. We have also added data from the
literature and from FIREWORKS to construct a well-populated density versus
stellar mass diagram in Figure 5. If the published data had a measured star-
formation rate in addition to the stellar mass we have restricted the points
plotted to those with low sSFRs (quiescent; ${\rm log_{10}}$ ${\rm sSFR}<-11$
${\rm yr^{-1}}$). In cases where the star-formation rate was not quoted
explicitly, we only plot those galaxies which are described as “quiescent” by
the authors. This distribution for both our protocluster candidates (yellow
circles and squares for quiescent and star-forming) and the field sources from
the literature (black squares) and FIREWORKS (blue squares) is shown in Figure
5. For the comparison field sample we have restricted to FIREWORKS galaxies
brighter than $K=21.5$ and with photo-$z$ between 1.9 and 2.6. We have made
the same redshift cut for the literature sample. This redshift range
approximately corresponds to a 1 Gyr epoch centered on the protocluster
redshift.

The mean density of the protocluster sample (${\rm log}$ $<\Sigma_{50}>=9.9$)
is 0.5 dex lower than that for the field sample. For the stellar mass range of
our protocluster sample, $10^{10.5}M_{\odot}<M_{\star}<10^{11.4}M_{\odot}$,
where the mass distributions are similar, we can calculate the distribution of
the surface mass densities irrespective of total stellar mass. We have also
used the KS test to calculate the probability that the (quiescent)
protocluster and field densities are drawn from the same parent distribution.
A fiducial value of $P_{\rm KS}<5\%$ may be considered sufficient to reject
the null hypothesis that they are from the same parent. In this case $P_{\rm
KS}\sim 5\%$ and is therefore a relatively strong constraint. We have
perturbed our measured densities and re-calculated $P_{\rm KS}$ for 10000
trials. We show the distribution of $P_{\rm KS}$ in Figure 6. For the black
(yellow) histogram 30% (60%) of the realizations fall below $P_{\rm KS}=5\%$.
Both histograms have tails to higher probabilities.

Figure 6.— The KS probability distributions for the protocluster versus field
galaxy comparison. The black histogram are derived from 10000 runs with the
protocluster densities perturbed at random within the Gaussian errors. The
yellow histogram is the same but excluding the most dense protocluster galaxy.
The vertical dashed line marks $P_{\rm KS}=0.05$. About 55% of the
realizations fall below the 5% probability.

###  4.2. Radial Dependencies

In order to assess possible radial gradients in the galaxy properties within
the cluster, with respect to the radio galaxy, we have constructed the
histograms shown in Figure 7. For each of these four physical galaxy
parameters: Sérsic $n$ value, surface mass density ($\Sigma$), stellar mass
($M_{\star}$) and specific SFR, we have made a single cut in the galaxy sample
and plotted two radial histograms for above (orange) and below (blue) the
chosen cut value. The left panel of Fig. 7 shows the histograms for the full
galaxy sample appropriate to each parameter, i.e., the GALFIT sample for the
Sersic $n$ value. For the rest, the galaxies must also be in the stellar
population sample. The right panel shows the histograms for our protocluster
galaxy sample (both quiescent and star-forming). We have presented these two
analyses because while the presence of field galaxies in the larger samples
will dilute any result, the statistics are poor for the quiescent protocluster
galaxy sample. Furthermore, this field exhibits a factor of six surface
overdensity of red galaxies (Zirm et al., 2008), so the large sample may not
strongly dilute trends.

For each pair of histograms we have run the two-sample KS test to determine
whether the distributions are consistent with one another. However, for the
full sample (left) the Sersic $n$ distributions are more dissimilar than for
the other parameters with a low $P_{\rm KS}=5\%$. This low probability seems
to be the result of a relatively flat distribution of the $n<2.5$ galaxies
with radius contrasting with the structure in the radial distribution of the
higher $n$ galaxies. This hint may imply a scenario in which the galaxies are
deeper within the gravitational potential. We discuss this point further
below. In the future, with spectroscopic redshifts for red galaxies, it will
be possible to repeat this test with better interloper rejection to see if the
discrepancy between histograms is significant for bona fide protocluster
galaxies.

##  5\. Discussion

We have presented the combined analysis of 13 band photometry and high
spatial-resolution NIR imaging in the field of a known galaxy protocluster at
$z=2.16$. In cases like this where there is a known, confirmed overdensity and
strong statistical evidence for a dominant contribution from protocluster
members we can make progress despite the lack of spectrocopic redshifts. We
have identified a robust sample of likely protocluster galaxies. Our
conclusions are tentative and tempered by the following caveats. We do not
have spectroscopic redshifts for the quiescent cluster galaxies. While we have
done our best to isolate the most probable cluster members, they may still be
field galaxies. The cluster sample is also relatively small and the results
are therefore more suggestive rather than statistically robust. Finally, due
to the limited areal coverage of the protocluster (see Fig. 1), the very
massive galaxies may be underrepresented in the cluster sample. These may also
be more dense.

Possible stellar population model offsets have been minimized by converting
the literature points to the same models and IMF we used to fit the cluster
galaxies.

###  5.1. Evolution of Galaxy Structure and Stellar Mass Surface Density

From the initial discovery of SEEDs, in general field surveys, the primary
question has been what evolutionary processes affect the SEEDs between $z\sim
2-3$ and $z=0$ that bring them in line with the local mass-size relation. If
the dominant process is galaxy merging, then we might expect that most of the
full galaxy mergers, as opposed to tidal interactions and harassment, may have
already happened. While if the primary determinant of galaxy density is the
formation redshift, the young Universe being denser, we might expect that
cluster galaxies will be denser than their field counterparts having formed
earlier.

In our data we see some indication for a difference between the profile shapes
and density distributions for protocluster versus field galaxies (Fig. 5). For
our sample of likely quiescent protocluster galaxies their stellar densities
are lower and perhaps even the Sersic index is higher than for similarly
selected field galaxies. From other studies it appears that the majority of
field SEEDs have higher axial ratios (flattened) with $n\sim 2$ (van der Wel
et al., 2011).

###  5.2. Cosmic Merger Clocks

Several previous studies have shown evidence that protocluster galaxies tend
to be more massive and contain older stars than their field counterparts at
the same redshift (e.g., Steidel et al., 2005; Tanaka et al., 2010). This
advanced evolution in the cluster environment may also extend to the internal
structure and dynamics of the galaxies. At lower redshift, we observe a
morphology-density relation, and we may be seeing the beginnings of that
relation in the MRC 1138 protocluster. Furthermore, the lower densities of the
protocluster galaxies suggests that the necessary merging has also taken place
at a quicker pace than in the field. If we assume then that all galaxies begin
as dense SEEDs at high redshift, we can use the observed densities as a
measure of the “merger age” of the remnants. A similar idea was put forth by
Hopkins and Hernquist (2010) to derive the global star-formation history by
using the mass profiles of galaxies. They propose that the dense cores of
galaxies form early in starbursts and the outer parts form in a more quiescent
mode, perhaps in disks. Based on the results from this paper we suggest that
the resolved mass profiles can be used further to constrain the redshift of
formation and the subsequent merger history. The ratio of high-density stellar
mass to low-density stellar mass may tell us something about the merger age of
a galaxy while the absolute density of the highest density stellar components
may tell us about the formation redshift. Studies at low-redshift have already
found some correlation between galaxy density and the mean stellar age (e.g.,
van der Wel et al., 2009). With more precise stellar ages for $z\sim 2$
galaxies now becoming available we can extend this analysis to high-redshift
when the fractional age differences between galaxies are larger.

A. Zirm and S. Toft gratefully acknowledge support from the Lundbeck
Foundation. The authors thank the anonymous referee for their helpful
comments. This work is supported by World Premier International Research
Center Initiative (WPI Initiative), MEXT, Japan and also in part by Grant-In-
Aid for Young Scientists No. 23740144.

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Table 1Protocluster Candidates  Object | Photometric | Odds | $H_{160}$ | Line | Stellar | Specific | Effective | Effective | Mass
---|---|---|---|---|---|---|---|---|---
Object | Redshift |  | (AB) |  | Mass | SFR | Radius | Radius | Density
|  |  |  |  | (log($M_{\odot}$)) | (log(yr-1)) | ($\arcsec$) | (kpc) | ($10^{9}$ $M_{\odot}{\rm kpc}^{-2}$)
Quiescent Protocluster Galaxies
312 | 2.24 | 1.00 | 21.98 $\pm$ 0.01 | 0 | $11.33^{+0.04}_{-0.04}$ | -99.00 | 0.23 $\pm$ 0.09 | 1.89 | $9.67\pm 6.5$
394 | 2.23 | 1.00 | 24.07 $\pm$ 0.01 | 0 | $10.65^{+0.20}_{-0.20}$ | -11.24 | 0.14 $\pm$ 0.07 | 1.20 | $4.98\pm 2.6$
399 | 2.49 | 0.98 | 24.20 $\pm$ 0.01 | 0 | $10.59^{+0.20}_{-0.16}$ | -11.24 | 0.23 $\pm$ 0.09 | 1.91 | $1.78\pm 0.4$
493 | 2.12 | 1.00 | 22.73 $\pm$ 0.01 | 0 | $11.13^{+0.03}_{-0.04}$ | -99.00 | 0.19 $\pm$ 0.07 | 1.58 | $8.56\pm 6.2$
507 | 2.16 | 1.00 | 22.48 $\pm$ 0.01 | 0 | $11.04^{+0.05}_{-0.03}$ | -99.00 | 0.06 $\pm$ 0.06 | 0.47 | $80.14\pm 52.9$
547 | 2.10 | 1.00 | 22.68 $\pm$ 0.01 | 0 | $10.97^{+0.08}_{-0.03}$ | -11.72 | 0.09 $\pm$ 0.08 | 0.78 | $24.21\pm 14.2$
574 | 2.13 | 1.00 | 23.33 $\pm$ 0.01 | 0 | $10.65^{+0.04}_{-0.04}$ | -99.00 | 0.14 $\pm$ 0.09 | 1.17 | $5.15\pm 3.6$
599 | 2.54 | 1.00 | 23.89 $\pm$ 0.01 | 0 | $10.75^{+0.21}_{-0.05}$ | -12.20 | 0.15 $\pm$ 0.08 | 1.22 | $6.41\pm 1.0$
Star-forming Protocluster Galaxies
347 | 2.08 | 0.92 | 23.69 $\pm$ 0.01 | 0 | 9.69 | -8.79 | 0.19 $\pm$ 0.05 | 1.57 | 0.31
456 | 2.06 | 0.95 | 23.57 $\pm$ 0.07 | 0 | 10.49 | -9.27 | 0.63 $\pm$ 0.06 | 5.19 | 0.18
595 | 2.06 | 0.91 | 23.47 $\pm$ 0.01 | 0 | 10.24 | -8.79 | 0.21 $\pm$ 0.07 | 1.72 | 0.92
Line-emitting CandidatesaaNarrow-band selected objects that are not yet
spectroscopically confirmed.
700 | 2.16 |  $\cdots$ | 24.02 $\pm$ 0.02 | 1 |  $\cdots$ |  $\cdots$ | 0.23 $\pm$ 0.06 | 1.88 |  $\cdots$
463 | 2.16 |  $\cdots$ | 24.51 $\pm$ 0.03 | 1 |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
1078 | 2.16 |  $\cdots$ | 22.94 $\pm$ 0.01 | 1 |  $\cdots$ |  $\cdots$ | 0.17 $\pm$ 0.06 | 1.38 |  $\cdots$
1070 | 2.16 |  $\cdots$ | 23.44 $\pm$ 0.02 | 1 |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
511 | 2.16 |  $\cdots$ | 24.50 $\pm$ 0.01 | 1 |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
516 | 2.16 |  $\cdots$ | 24.07 $\pm$ 0.01 | 1 |  $\cdots$ |  $\cdots$ | 0.14 $\pm$ 0.08 | 1.15 |  $\cdots$
575 | 2.16 |  $\cdots$ | 24.62 $\pm$ 0.02 | 1 |  $\cdots$ |  $\cdots$ | 0.01 $\pm$ 0.03 | 0.09 |  $\cdots$
988 | 2.16 |  $\cdots$ | 23.41 $\pm$ 0.01 | 1 |  $\cdots$ |  $\cdots$ | 0.32 $\pm$ 0.06 | 2.67 |  $\cdots$
536 | 2.16 |  $\cdots$ | 23.90 $\pm$ 0.01 | 1 |  $\cdots$ |  $\cdots$ | 0.13 $\pm$ 0.08 | 1.11 |  $\cdots$
1069 | 2.16 |  $\cdots$ | 23.91 $\pm$ 0.01 | 1 |  $\cdots$ |  $\cdots$ | 0.00 $\pm$ 0.54 | 0.01 |  $\cdots$
457 | 2.16 |  $\cdots$ | 23.84 $\pm$ 0.01 | 1 |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
451 | 2.16 |  $\cdots$ | 23.74 $\pm$ 0.01 | 1 |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
275 | 2.16 |  $\cdots$ | 23.62 $\pm$ 0.01 | 1 |  $\cdots$ |  $\cdots$ | 0.12 $\pm$ 0.06 | 0.96 |  $\cdots$
897 | 2.16 |  $\cdots$ | 24.09 $\pm$ 0.01 | 1 |  $\cdots$ |  $\cdots$ | 0.11 $\pm$ 0.05 | 0.92 |  $\cdots$
300 | 2.16 |  $\cdots$ | 22.50 $\pm$ 0.01 | 1 |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
311 | 2.16 |  $\cdots$ | 23.94 $\pm$ 0.01 | 1 |  $\cdots$ |  $\cdots$ | 6.77 $\pm$ 4.36 | 56.15 |  $\cdots$
361 | 2.16 |  $\cdots$ | 24.50 $\pm$ 0.01 | 1 |  $\cdots$ |  $\cdots$ | 0.03 $\pm$ 0.04 | 0.27 |  $\cdots$
365 | 2.16 |  $\cdots$ | 26.16 $\pm$ 0.06 | 1 |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
215 | 2.16 |  $\cdots$ | 24.09 $\pm$ 0.01 | 1 |  $\cdots$ |  $\cdots$ | 0.14 $\pm$ 0.08 | 1.18 |  $\cdots$
448 | 2.16 |  $\cdots$ | 21.93 $\pm$ 0.02 | 1 |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
435 | 2.16 |  $\cdots$ | 23.74 $\pm$ 0.01 | 1 |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
431 | 2.16 |  $\cdots$ | 22.61 $\pm$ 0.01 | 1 |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
Confirmed Line-emitting Galaxies
53 | 2.16 |  $\cdots$ | 24.00 $\pm$ 0.01 | 2 |  $\cdots$ |  $\cdots$ | 0.02 $\pm$ 0.04 | 0.19 |  $\cdots$
263 | 2.16 |  $\cdots$ | 24.01 $\pm$ 0.01 | 2 |  $\cdots$ |  $\cdots$ | 0.18 $\pm$ 0.08 | 1.52 |  $\cdots$
270 | 2.16 |  $\cdots$ | 21.11 $\pm$ 0.01 | 2 |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
450 | 2.16 |  $\cdots$ | 22.75 $\pm$ 0.01 | 2 |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
945 | 2.16 |  $\cdots$ | 23.79 $\pm$ 0.01 | 2 |  $\cdots$ |  $\cdots$ | 0.03 $\pm$ 0.02 | 0.22 |  $\cdots$
289 | 2.16 |  $\cdots$ | 22.05 $\pm$ 0.01 | 2 | 11.52 | -11.24 | 0.43 $\pm$ 0.09 | 3.53 | 4.22
757 | 2.16 |  $\cdots$ |  $\cdots$ | 2 | 10.54 | -9.27 |  $\cdots$ |  $\cdots$ |  $\cdots$
561 | 2.16 |  $\cdots$ | 21.99 $\pm$ 0.01 | 2 | 11.23 | -12.20 | 0.86 $\pm$ 0.09 | 7.09 | 0.54
296 | 2.16 |  $\cdots$ | 22.27 $\pm$ 0.01 | 2 | 10.50 | -8.18 | 0.10 $\pm$ 0.07 | 0.84 | 7.10
648 | 2.16 |  $\cdots$ | 23.38 $\pm$ 0.01 | 2 |  $\cdots$ |  $\cdots$ | 0.14 $\pm$ 0.06 | 1.18 |  $\cdots$
535 | 2.16 |  $\cdots$ | 23.90 $\pm$ 0.01 | 2 |  $\cdots$ |  $\cdots$ | 0.14 $\pm$ 0.05 | 1.19 |  $\cdots$
387 | 2.16 |  $\cdots$ | 24.70 $\pm$ 0.02 | 2 |  $\cdots$ |  $\cdots$ | 0.06 $\pm$ 0.04 | 0.47 |  $\cdots$

Figure 7.— Radial (measured from the radio galaxy) distributions of galaxies
as a function of four derived physical parameters. Top to bottom: Sérsic $n$
value, surface mass density ($\Sigma$), stellar mass ($M_{\star}$) and
specific star-formation rate. For each parameter we have made a single cut of
the total sample into two bins and plotted those two histograms separately for
those above (orange) and below (blue) this cut. The value of the cut is shown
below the parameter name and the number of galaxies in each bin to the left of
the name. Finally, the KS probability, that the two histograms are drawn from
the same parent distribution is also shown.