This document is a dissertation by Joakim Carlsen submitted in 2014/2015 for a Bsc(Honours) in Applied Physics. It investigates detecting massive galaxies at high redshift (z > 4) using photometric data from the Dark Energy Survey (DES). The dissertation aims to identify massive galaxy candidates at z > 4 based on their colors and fit their spectral energy distributions using photometric redshift modeling software. Several high redshift massive galaxy candidates were identified and their properties were analyzed, with the most promising candidates to be proposed for follow-up spectroscopy to confirm their redshifts.

1. School of Earth and
Environmental Sciences
Title: “DETECTING
MASSIVE GALAXIES AT
HIGH REDSHIFT USING
THE DARK ENERGY
SURVEY”
Name: Joakim Carlsen
Course: Applied Physics
Student No: 676951
Year: 2014/15

3. DETECTING MASSIVE GALAXIES AT HIGH REDSHIFT USING
THE DARK ENERGY SURVEY
Joakim Carlsen
676951
Supervisor: Dr. Daniel Thomas
Year of submission: 2015
Wordcount: 5066
A dissertation presented for the degree of
Bsc(Honours) Applied Physics
School of Environmental and Earth sciences
University of Portsmouth
United Kingdom
April 2015

4. Abstract
Before the release of the Dark Energy Survey s data (DES), which is mainly
intended to study low- to intermediate-redshift galaxies to constrain Dark
Energy, Davies et al. (2013) modelled galaxies at high redshift to determine
their detectability in the DES filters, given the survey characteristics (e.g. depth
and scanned survey area). They found that DES would be able to identify
massive galaxy candidates at z ≥ 4. By passively evolving the observed low-z
mass function to high-z and extrapolating it to higher masses, they concluded
that massive 1012 M should be present at the very early Universe, albeit
at a low number density. DES s final footprint will be 5000 deg2, but I have
studied a patch of ∼ 150 deg2, and I find 2 > candidates at z > 4 with stellar
masses of between 1011 and 1012M . This estimated count is consistent with
theoretical predictions of 0.02 galaxies per deg2. (Behroozi et al., 2012; Davies
et al., 2013)(See Appendix for plot). The candidates will be submitted in a list
of proposed candidates to be studied further using spectroscopy at the Very
Large Telescope (VLT) in Chile, and if the redshifts coincide with what I put
forth in this project, it would contribute heavily to our understanding of how
galaxies form and evolve.
1

5. Contents
1 Introduction 5
2 The Dark Energy Survey 7
3 Photometric redshifts 7
4 Metallicity 8
5 Galaxy modelling 8
6 Selection of galaxies at z > 4 9
7 Follow-up analysis of candidate galaxies using Hyperz 10
8 Results 13
8.1 Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
8.2 Probability Distribution Functions for galaxies at z ∼ 4 . . . . . . . . 13
8.3 Spectral Energy Distribution plots . . . . . . . . . . . . . . . . . . . . 15
8.4 Spectra Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
9 Discussion 17
10 Summary & Conclusions 18
11 Acknowledgements 19
12 Appendix 21
2

6. List of Figures
1 Plot showing that the most massive ∼ 1012
M would have formed
very quickly and remained passively evolving after the short burst of
star formation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2 Colour-colour selection plots for z = 4, 5 and 6 respectively. The dotted
lines represent the region in which high redshift massive sources would
be drawn. The grey points represent other galaxies in that specific
redshift. There would be quite a large number of contaminants so
the error in each band has been narrowed to produce a more accurate
set of data points. The cuts in the plots come from the criteria from
Davies et al (2013) and include dust extinction. Note; smaller values
of magnitude indicates a brighter source. If a source has a magnitude
of 25 in the g-band and a magnitude of 23 in the r-band, this will then
result in a colour of 25-23 = 2, which shows that the r-band shows up
brighter, hence being a redder object. . . . . . . . . . . . . . . . . . . 10
3 Flowchart of processes in HyperZ . . . . . . . . . . . . . . . . . . . . 11
4 Probability Distribution functions for 8 galaxies with the Galaxy ID in
the top left corner. The pronounced peaks at z > 3 makes them good
candidates, and thus encouraging the plotting of the SED for each
one. The 4th plot shows a greater peak at z ∼ 3, but it disregarded
due to a high χ2
value. Take note of the bottom right plot, it shows
multiple peaks in almost the entire redshift range, and it is put in as
an example of a bad PDF. . . . . . . . . . . . . . . . . . . . . . . . . 14
5 8 Spectral Energy Distribution plots for the candidate galaxies. The
x-axis represents the wavelength, and the y-axis represents the Flux.
Each one is represented with the galaxy ID, its photometric redshift
(z) and its star formation rate (SFR). The unit for SFR calculated in
HYPERZ is /yr The red points are the observed SED points and the
error bars represent the error in the Flux integration. It is important
to note that the last plot in the bottom right corner is not a very
reliable one due to the fact that its probability distribution function
shows multiple peaks all over the redshift range and large associated
errors. There are no breaks that show up as prominent as the breaks
for the rest of the SED . . . . . . . . . . . . . . . . . . . . . . . . . . 16
6 Predicted numbercounts of massive galaxies at given redshifts . . . . 21
7 Histogram of Hyperzs massive galaxies at given redshifts . . . . . . . 22
8 Histogram of Hyperzs massive galaxies at given redshifts . . . . . . . 22
9 Flowchart of processes in HYPERZ . . . . . . . . . . . . . . . . . . . 23
10 A plot of the area of the sky from where the galaxies are detected . . 24
3

7. List of Tables
1 Table showing the Colour−selection criteria . . . . . . . . . . . . . . 9
2 Table 2: Showing the redshift, age and mass comparison between
values in the DES catalogue and the values computed via HYPERZ
for each of the galaxies. The Star formation rate and the associated
model is listed. If follow-up spectroscopic analysis is to be done, the
values for the position of each galaxy is added (Right ascension and
Declination) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
4

8. 1 Introduction
The last decade has been filled with impressive advancements in astronomical
technologies. High redshift astrophysics is now at a point where we can look into the
very early Universe. Recent observations have confirmed galaxies back to within 1
billion years of the Big Bang. (Bunker et al., 2010; Stanway et al., 2004; Vanzanella
et al., 2009). Studies of these galaxies have been done and have provided estimates
of the stellar mass of the Universe at the very early stages. (Banerji et al., 2010;
Conselice et al. , 2005; Stark et al., 2009) However, to do detailed studies of high-z
galaxies, large chunks of telescope time are needed. The result of this difficulty
is that current studies have been primarily limited to very bright sources, which
are usually detected by surveys that are either wide in area but not very deep or
are deep but extended on a small portion of the sky. The fact that deep and wide
surveys are somewhat lacking could mean that we could be missing an important
set of rare, massive (> 1011
M ), high-z galaxies, which have been predicted to
have formed before z ∼ 3. (Thomas et al., 2005, 2010) These types of galaxies are
very important for our understanding of how galaxy formation and evolution works
within the currently accepted cosmological framework. The most widely accepted
galaxy formation model today is that of a hierarchical growth model. This model
depicts a formation where the most massive objects complete their assembly at low
redshifts(De Lucia et al., 2006). If this is the case, then there should be a paucity of
these massive objects at high redshift. Reasons, based on observationally-supported
evidence, exists to believe that this is not the best model; studies on fossil stellar
population records from the local Universe (z ∼ 0) imply that the most massive
galaxies hosting the oldest stars should already have formed at z ∼ 5 (Thomas et al.,
2005, 2010).
No conclusive evidence has been put forward to state whether the stars we now
see in local massive elliptical galaxies formed in small systems at high redshift that
over time merged to form the massive galaxies we see today (e.g. De Lucia et al.
(2006)), or if they formed in massive systems that rapidly built up their stellar mass
and have remained semi dormant and passively evolving since. I show a plot taken
from Thomas et al. (2010), Figure[1] where specific star formation rate as a function
of look-back time and redshift shows the predicted formation for galaxies and their
respective masses indicated by the labels.
5

9. Figure 1: Plot showing that the most massive ∼ 1012M would have formed very quickly and remained passively
evolving after the short burst of star formation.
Large samples of smaller galaxies have been identified at high redshift with rapid
star formation episodes (e.g. Verma et al. (2007)), but the stellar mass contained
in these systems is not large enough to produce a giant elliptical such as the ones
we can observe at the current epoch (e.g. Douglas et al. (2010)). From the studies
of local massive galaxies, constraints have been put on their formation epochs. So
if the systems form their stars over a very brief period at z ∼ 5, then we should
see star formation in massive systems at z ∼ 5, but this has not yet been observed.
These galaxies will be rare, and not easily detected in smaller area high-z surveys.
However, recent studies based on the ZFOURGE galaxy survey have identified a
substantial population of M ∼ 1012
M galaxies at z ∼ 4 with suppressed star
formation rates, and average stellar ages of 0.8 Gyr, showing that these galaxies
likely started forming stars before z = 5 (Straatmann et al., 2014). Although this is
an interesting finding, the evidence for massive, evolved galaxies at z > 4 is not as of
yet convincing. DES will provide an opportunity to detect these rare objects. Using
DES Science Verification data, I aim to build on the works by Davies et al. (2013)
and the models by Maraston, C. (2005) in the publicly available code Hyperz by
Bolzonella et al. (2000) to identify massive galaxies at z > 4, and put them forward
for spectroscopic follow-up. I assume a ΛCDM cosmology with ΩM = 0.23, ΩΛ = 0.77
and H0 = 72kms−1
Mpc−1
. The magnitude system used is AB.
6

10. 2 The Dark Energy Survey
Overshadowing previous surveys with its large area and combination of depth and
width, the Dark Energy Survey (DES) will be a very useful survey for studies of rare,
bright objects in the very early Universe. The survey is split into two: the DES-W
(WIDE), which will cover 5000 deg2
patch of the sky, and with a depth of 25.5,
25.0, 24.4, 23.9, and 22.0 magnitude in the g, r, i, z and Y bands respectively (Davies
et al., 2013), and the DES-D (DEEP) that will cover 30 deg2
in two deep and eight
more shallow fields reaching a depth of 27.1, 27.3, 27.0, 26.8, 21.7 magnitude in g,
r, i, and z bands respectively. More details can be found in Bernstein et al. (2012).
The Survey is carried out with the DECam which is mounted on the Blanco
4 meter telescope at the Cerro Tololo Inter-American Observatory (CTIO) in La
Serena, Chile. The camera is composed of an array of 64 CCDs with a resolution of
2048 x 4096 that are designed to be optimally efficient at wavelengths around 9000Å,
and it covers a wavelength of 4000-10000Å. The main purpose of the Survey is the
investigation of Dark Energy s equation of state via four probes: Baryonic acoustic
oscillations, weak lensing, galaxy clustering and supernovae.(Abbott et al., 2005)
However, the surveys combination of depth and area-width will also make the DES
data set very valuable for galaxy evolution studies. It is important to note that DES
is a photometric survey, hence it relies on photometric galaxy redshift rather than
spectroscopic ones.
3 Photometric redshifts
The technique of photometric redshift measurement dates back to the sixties
(Baum, 1962), and recent advancements in multicolour deep sky surveys have made
the use of this technique more interesting. The vast number of objects in the large
photometric surveys carried out nowadays, can be inaccessible to spectroscopic
investigation or are too time consuming. Photometric redshift is the estimate of
the cosmological redshift of galaxies via medium/large band photometry instead of
spectroscopy. The redshift is estimated by separating the light from galaxies into
broad wavelength bins, typically 1000Å wide, and then comparing the observed
Spectral Energy Distributions (SEDs) to theoretical predictions based on evolutionary
population synthesis models for different galaxy types. This technique identifies
broad features such as the Balmer and Lyman breaks and the general shape of the
spectra, which influence galaxy colours, hence giving one information about their
redshifts. The utilization of this technique makes the redshift measurements much
less time consuming and can be used on larger data sets than can be done with
spectroscopy. This method is less accurate than the spectroscopic approach, but can
be derived for much larger samples of galaxies as photometric observations require
significantly less integration time. After interesting objects are identified, a follow-up
spectroscopic measurement can be done on specific objects to put the final nail in
the coffin.
7

11. 4 Metallicity
In Astrophysics, the metallicity (Z) of a galaxy, is the proportion of its matter
making up the chemical elements, excluding hydrogen and helium. One way for a
galaxy to have high metallicity, is if it is old, hosting redder stars and hence have
had many supernovae expelling heavier elements into the intragalactic medium
The underlying theory of this project is mainly based upon studies on local
Universe galaxies where the chemical element-fractions and stellar ages indicate that
they must have gone through successive star formation episodes to be able to produce
the large metal fraction that is observed (Thomas et al., 2010). From this research
it becomes evident that local massive elliptical galaxies should have formed at the
early Universe. Elaboration to follow
5 Galaxy modelling
The Dark Energy Survey s combination of sky area and depth will result in a
volume accessible to ∼ 300 million galaxies, so the identification of each galaxy s
properties is going to be a challenge. The works by Davies et al. (2013) used models
by Maraston, C. (2005) to be able to discern between massive high redshift galaxies
from those at lower redshift. The model library used contains 32 template SED, built
with different star formation histories and different values for metallicity and age. The
models take into account Single burst stellar populations (SSP), where the galaxies
form all their star very quickly and later evolve passively, and composite stellar
populations (CSP) where the galaxies can experience more than one burst of star
formation thus the stars will have different ages, and different chemical compositions.
The stellar population models are shifted and stretched in wavelength in the DES
filters to represent galaxies at z = 3, 3.5, 4, 4.5, 5, 5.5 and 6 having masses ranging
from 1010
M to 1012.5
M . The internal dust in galaxies can absorb blue, higher
energy light and re-emit it in longer wavelength, making the galaxy look redder than
it actually is. This is also taken into account in the models by implementing the
Calzetti et al. (2000) dust extinction law which has modelled the emission made in
dust grains in the interstellar medium and account for the reddening of the emission
in higher energy light from star forming regions. In this study I utilize these models
and fit them to my observed data gathered from the DES catalogue.
8

12. 6 Selection of galaxies at z > 4
The selection of star forming high redshift galaxies has since the 90s been done
successfully by using the Lyman-break technique. It estimates roughly the photo-
metric redshifts by identification of the large break in the spectra of galaxies at
1216Å which is produced when radiation with higher energies (shorter wavelength)
is being absorbed by neutral hydrogen in star forming regions of a galaxy.(Steidel et
al., 2003) The drop-out technique relies on the dimming of the spectra at shorter
wavelength than the Lyman limit (912Å) and the increase in the spectra above the
limit. In high redshift galaxies the Lyman break gets shifted into optical wavelength,
and one can observe objects that disappear in the bluest filters. This technique has
later been implemented to develop Colour-colour selection criteria to identify high
redshift objects (Douglas et al., 2010; Vanzanella et al., 2009). A similar technique
was explored in Davies et al. (2013), where they posed three criteria to allow one to
separate high redshift galaxies from the lower redshift counterparts. The selection
has been proven successful in the identification of wanted candidates in simulated
data sets created in the wait for the DES data. The features in the spectra of the
galaxies targeted are shifted to the redder bands at higher redshift, thus requiring a
changing colour selection for each epoch (z = 4, 5, 6). I show in Table [1], the three
criteria from Davies et al. (2013)
Table 1: Table showing the Colour−selection criteria
Redshift Colour-selection
4 g - r > 0.9 g - r > 1.8(r - i) + 1.05 r - i < 0.8
5 r - i > 0.9 r - i > 2.0(i - z) + 0.9
6 i - z > 1.5 z - Y < 1.2
Figure [2] displays the Colour-colour plots made in the program IDL for detection
of sources ranging from z = 4−6. (The full code can be found in the Appendix) I find
several candidates in the selection procedure, which are then further investigated.
9

13. Figure 2: Colour-colour selection plots for z = 4, 5 and 6 respectively. The dotted lines represent the region
in which high redshift massive sources would be drawn. The grey points represent other galaxies in that specific
redshift. There would be quite a large number of contaminants so the error in each band has been narrowed to
produce a more accurate set of data points. The cuts in the plots come from the criteria from Davies et al (2013) and
include dust extinction. Note; smaller values of magnitude indicates a brighter source. If a source has a magnitude
of 25 in the g-band and a magnitude of 23 in the r-band, this will then result in a colour of 25-23 = 2, which shows
that the r-band shows up brighter, hence being a redder object.
7 Follow-up analysis of candidate galaxies using Hy-
perz
The DES data does not explore possibilities that objects reside at higher redshifts
than z ∼ 1.5, but as stated there is good reason to believe they exist within the data
pool. I use a second code to do follow-up analysis on the galaxy candidates to find
a new estimate of their redshifts and other specifications. The publically available
code HYPERZ (Bolzonella et al., 2000) has proven invaluable to this study. The
code computes photometric redshifts by Spectral Energy Distribution (SED) fitting
through χ2
− minimization. The observed SED of galaxies found from selection
within the DES data is compared to a set of 32 template spectra, in this case these
are the template spectra produced by (Maraston, C., 2005).
10

14. χ2
=
Nfilters
i=1
Fobs,i − b × Ftemp,i(z)
σi
2
(7.1)
Where Fobs,i, Ftemp,i and σi represent the observed and template fluxes and their
associated uncertainty in filter i, and b represents a normalization constant. The
input data are magnitudes and errors associated with the magnitudes. In this case
the magnitudes are values in g, r, i, z and Y, and their errors. The code convolves
the 32 synthetic template SEDs to fit the observed SED data to produce the outputs.
After running the code and producing the best fit solution, HYPERZ provides a
range of stellar population best-fit parameters such as; photometric redshifts and
associated probabilities, spectral template, fluxes of the best fit SEDs, ages, reddening,
metallicities, absolute magnitudes and Star formation rates. Since the fitting uses
the χ2
− minimization, a lower χ2
− value corresponds to a better solution and to a
better fit to the data. I have run a catalogue with 622 objects through HYPERZ on
the Sciama computer at the Institute of Cosmology and Gravitation (ICG). While
the majority of the objects classify as low redshift galaxies or objects with unphysical
properties, 8 of them showed good quality fits characterised by photometric redshift
values z > 4 and stellar masses > 1011
M , satisfying the candidate selection criteria.
A summary of their properties is presented in Table [2].
Figure 3: Flowchart of processes in HyperZ
11

15. Galaxy ID z (DES) z (Hyperz) M (DES)(M ) M(Hyperz)(M ) Logmass Age (DES)Gyr Age,(Hyperz) Gyr SFR model RA DEC
2932030543 0.64999 5.085 1.40 x 1011
2.555 x 1011
11.41 7 0.1609 753.1 tau = 0.1 72.19067 -52.47451
2942014551 0.68712 4.805 1.25 x 1011
5.038 x 1011
11.7 0.78 0.321 0 trunc = 0.3 75.5548 -51.9022
2935845950 0.38645 4.395 1.74 x 1010
1.823 x 1011
11.26 3 0.1609 1315 const 72.968 -57.7297
2935480720 0.45984 4.315 2.08 x 1010
2.703 x 1011
11.43 4.75 0.1015 0 trunc = 0.1 67.737 -58.1052
2941936045 1.00944 4.3 1.54 x 1011
3.426 x 1011
11.53 2.75 0.1434 1249.8 tau = 0.1 75.19274 -54.09516
2934188254 0.56153 4.105 3.66 x 109
3.187 x 1010
10.5 0.1805 0.1015 355.7 const 72.0913 -54.0264
2928124575 0.42134 4.09 4.44 x 109
7.758 x 1010
10.89 0.2273 0.1015 865.4 const 76.0697 -47.1314
2932120903 0.53033 4.085 4.97 x 109
4.899 x 1010
10.69 0.1805 0.1015 545.9 const 67.4098 -51.5621
Table 2: Table 2: Showing the redshift, age and mass comparison between values in the DES catalogue and the values computed via HYPERZ for each of the galaxies. The Star formation
rate and the associated model is listed. If follow-up spectroscopic analysis is to be done, the values for the position of each galaxy is added (Right ascension and Declination)
12

16. 8 Results
8.1 Properties
From outputs that the program gave, it was clear that there were many objects
with a satisfying redshift value, but there were other conditions that made it possible
to disregard the objects as true massive high redshift objects. The masses of some of
the most massive galaxies observed in the Universe is that of a 1012
M , thus any
object showing up with a very satisfying value for redshift and a mass significantly
higher than that of a 1012
M galaxy would then be deemed non-physical, and not a
candidate which would sufficiently satisfy the criteria. Other properties that would
make one disregard a certain galaxy is the magnitudes of it. As it is known in
Astronomy and Astrophysics, the absolute magnitude is a measure of the amount of
intrinsic brightness of an object. It was made a scale in which the brightest objects
had a lower value than a fainter one, for instance a full moon has a magnitude of
−13 and the planet Venus has a magnitude of −5. So if a galaxy shows up with
magnitudes Mabs > −28 then that would be classified as non-physical. But galaxies
with properties too high absolute magnitude values would most likely be removed
solely on their mass being faulty as well. Hence from the 622 initial candidates
selected for HYPERZ analysis, 8 of them showed up with high Probabilities of being
at their given high redshifts. This gives a ratio of ∼ 1% of the initial selection
classified as the most massive high redshift galaxies. The galaxies identified in this
project have different properties after running the follow-up analysis, than what
was initially listed in the DES catalogue. The limiting of the redshifts in DES
would severely falsify a fraction of the galaxies properties, the mass calculated from
HYPERZ is significantly larger for each galaxy when compared to the value from
DES.
8.2 Probability Distribution Functions for galaxies at z ∼ 4
I show probability distribution functions (PDFs) for eight galaxies in Figure
[4] which have been selected from the candidates detected from the Colour-colour
diagram in Figure [2]. From HYPERZ I was able to read out the probability for a
galaxy to have a particular redshift value. It is clear that each PDF has high peaks
above z = 4, which indicates its probability of being at the respective redshifts. The
highest peak in each plot represent the best fit solution from the SED fitting in
HYPERZ.
13

17. Figure 4: Probability Distribution functions for 8 galaxies with the Galaxy ID in the top left corner. The
pronounced peaks at z > 3 makes them good candidates, and thus encouraging the plotting of the SED for each one.
The 4th plot shows a greater peak at z ∼ 3, but it disregarded due to a high χ2 value. Take note of the bottom
right plot, it shows multiple peaks in almost the entire redshift range, and it is put in as an example of a bad PDF.
14

18. The 8 galaxies where chosen due to their high redshift, high mass and their
large probability associated with a small χ2
−value. After these 8 galaxies had gone
through visual and analytical inspection I further made SED plots for each galaxy s
best fit template SED to observe if the template fit the data well. I also aimed to
look for spectral features that could give indicators on the galaxies properties.
8.3 Spectral Energy Distribution plots
I show Spectral Energy Distribution plots in Figure [5] made from the fitting of
the Maraston, C. (2005) synthetic spectra to my observed SED. Truncated, Constant
and Simple Stellar Population Star formation rate models fit the data very well,
with a median χ2
= 0.344. The SEDs show prominent breaks representing the
breaks for Lyman-α and for the Lyman limit. Here they appear shifted into the
longer wavelength filters due to high redshift values. Although some of the candidate
galaxies show up with a star formation rate of zero, they could still host very young
bright stars and thus the more UV luminous radiation is absorbed in both the internal
dust in the galaxy, and the dust within the line of sight; hence the lack of spectral
energy in the bluer part of the spectra.
15

19. Figure 5: 8 Spectral Energy Distribution plots for the candidate galaxies. The x-axis represents the wavelength,
and the y-axis represents the Flux. Each one is represented with the galaxy ID, its photometric redshift (z) and
its star formation rate (SFR). The unit for SFR calculated in HYPERZ is /yr The red points are the observed
SED points and the error bars represent the error in the Flux integration. It is important to note that the last
plot in the bottom right corner is not a very reliable one due to the fact that its probability distribution function
shows multiple peaks all over the redshift range and large associated errors. There are no breaks that show up as
prominent as the breaks for the rest of the SED
16

20. 8.4 Spectra Analysis
I do a check if the Lyman break(s) resides in the correct position for each galaxy
corresponding to its redshift. I use the rule:
z + 1 =
λobs
λemit
(8.2)
The Lyman limit in the rest frame is at 912Å (highest energy), and the Lyman
series ranges all the way down to 1216Å and is a Hydrogen spectral series of
transitions and result in emission lines in the ultraviolet as an electron from n ≥ 2
to n = 1 which is the lowest energy level of the electron. (n is the principal quantum
number). As you decrease the wavelength you will increase in energy, and radiation
at higher energies than the Lyman limit will be absorbed by neutral gas around the
galaxy and the 1216Å line is the lowest energy possible. If I apply this rule to my
galaxies where I re-arrange equation (8.2) to give me the observed wavelength. The
Lyman limit 912Å and the lowest energy transition 1216Å show up as prominent
features in all the SEDs. I do a check for one of the galaxies. ID: 2928124575
(4.09 + 1) × 912Å=4600.08Å (8.3)
(4.09 + 1) × 1216Å=6189.44Å (8.4)
By looking at the associated SED I find that the two most prominent breaks
appear in the right position. This procedure is done for all the galaxies and they all
show the same correct placements of the breaks.
It is important to bear in mind that potential biases on the stellar population
properties, could arise by assumptions introduced when modelling the different SED
templates. This would produce results that are biased to assumptions on the star
formation histories, initial mass functions, metallicity etc.
9 Discussion
Although the selection procedure did find the wanted candidates within the pool
of data, most of the candidates initially selected ended up not being high redshift
sources. This is not a big problem, but further error estimation and limiting could
account for the many unwanted objects. Running the entire catalogue through
HYPERZ could be an option, but the vast amount of data in the finished catalogue
(300 000 000 galaxies) would take an immense amount of time to compute, so it is
efficient to make an initial selection. It is important to note that in the middle of
the project process I relaxed the error limiting in the bluest bands for each selection
to make the drop − out technique more efficient. This is when I found the highest
redshift candidate at z ∼ 5. The galaxies show z > 4, average ages of 0.149 Gyr
and masses ranging from 10.5 >Log10M∗
/M ∼ 12, which implies that they likely
started forming stars well before z > 5. Recent studies show likely evidence for
progenitors of these types of galaxies, lying in the redshift range z ∼ 10. (Bouwens et
17

21. al., 2012; Pirzkal et al., 2015) Two of my galaxies show non-existent star formation
rate, as suggested by the best fit model for the two this is due to the truncation
of the formation. (Trunc = 0.1 trunc =0.3). The stars likely formed their mass
at a constant rate, for the case of the t = 0.1, the stars formed their mass over a
period of 0.1 Gyr and from then the star formation rate will be zero for the rest
of the galaxy s life. The galaxies are now what is called quiescent galaxies. Two
galaxies show a star formation rate indicated by the best fit model τ = 0.1. This
implies that the galaxies star formation rates are exponentially declining. While
they still have active star formation rates that are relatively high, they have either
gone through their peak of formation and are now quieting down, or have several
peaks of star formation episodes. As mentioned in the abstract, Davies et al. (2013)
show a predicted number count for sources with M∼1012
M and they imply that
there should be 0.02 sources per deg2
(Behroozi et al., 2012), and seeing as this
study looks at a patch of ∼ 150 deg2
and I find ∼ 5 sources that lie around that log
mass ∼ 1012
M , it agrees well with the prediction. And the results are certainly in
contradiction with what is proposed in De Lucia et al. (2006)(hierarchical growth).
Low redshift galaxies can exhibit particular emission lines in their spectra mimick-
ing the energy distribution of their higher redshift counterparts. To fully investigate
whether this is the case for any of the candidate galaxies one could do a similar
investigation done in Pirzkal et al. (2015), where they model nebular emission lines
to constrain redshift. However, the best possible way to determine the redshifts
would be via spectroscopic observations of the galaxies.
10 Summary & Conclusions
Using the data from the Dark Energy Survey I find evidence for the existence of
massive (M ∼ 1012
M ) galaxies with a range of different star formation rates at early
times in the universe (4 < z ≤ 5). The galaxies successfully satisfy the photometric
redshift Colour-colour criteria set in Davies et al. (2013), which previously has been
tested on simulated galaxy libraries. The Spectral Energy Distribution fittings show
prominent breaks sampled well by the DES, this in turn leads to accurate estimates of
the photometric redshift. The survey area used for this study covers ∼ 150 deg2
of the
sky, the full catalogue contained ∼ 6 000 000 objects, and with the error constrains
in the DES filters used in this project 622 candidates where drawn in the selection
boxes in Figure [2]. The initial fraction of massive high redshift galaxies found in
the DES catalogue by a Colour-colour selection, if the intention was not to do a
follow-up analysis, would be 0.00103 of the entire unfinished survey volume. 0.0128
being the final fraction (622 selected, 8 classified as high redshift massive objects).
At the end of the project, five candidates are the most interesting because they
exhibit the highest photometric redshift and the highest masses. Further analysis
on these candidates via spectroscopy would determine whether or not they are real
or if they have some of the properties that would mimic high redshift galaxies as
briefly mentioned here. If these candidates really do reside at the redshifts estimated
18

22. in this project, it would very much improve our understanding of galaxy formation
and evolution.
11 Acknowledgements
I would like to express my gratitude to the ICG and to my supervisor Dr. Daniel
Thomas for firstly letting me do this project, and for all the help along the way. I
thank Dr. Diego Capozzi for a very helpful hand in the process of making the codes
for this project, and for always helping me with any question. Dr. Claudia Maraston
has also been a very good source for information and insight. Further I would like to
thank the PhD student Xan Atkinson for all the help.
References
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20

24. 12 Appendix
Figure 6: Predicted numbercounts of massive galaxies at given redshifts
21

25. Figure 7: Histogram of Hyperzs massive galaxies at given redshifts
Figure 8: Histogram of Hyperzs massive galaxies at given redshifts
22

26. Figure 9: Flowchart of processes in HYPERZ
23

27. Figure 10: A plot of the area of the sky from where the galaxies are detected
24

28. 25
APPENDIX
LYMAN BREAK CHECK FOR ALL GALAXIES:
(z + 1) = lambdaOBS/lambdaEMIT
ID: 2932030543
z = 5.085
(5.085 + 1) ∗ 912Å = 5549.52Å
(5.085 + 1) ∗ 1216Å = 7399.36Å
Both match the SED.
ID: 2942014551
z = 4.805
(4.805 + 1) ∗ 912Å = 5294.16Å
(4.805 + 1) ∗ 1216Å = 7058.88Å
Both match the SED.
ID: 2935845950
z = 4.395
(4.395 + 1) ∗ 912Å = 4924.20Å
(4.395 + 1) ∗ 1216Å = 6560.32Å
Both match the SED.
ID: 2935480720
z = 4.315
(4.315 + 1) ∗ 912Å = 4847.28Å
(4.315 + 1) ∗ 1216Å = 6463.04Å
Both match the SED.
ID: 2941936045
z = 4.3
(4.3 + 1) ∗ 912Å = 4833.6
(4.3 + 1) ∗ 1216Å = 6444.8Å
The breaks are there, but along with many other spectral indeces.

29. 26
ID: 2934188254
z = 4.105
(4.105 + 1) ∗ 912Å = 4655.76Å
(4.105 + 1) ∗ 1216Å = 6207.68Å
Both match the SED.
ID: 2928124575
z = 4.09
(4.09 + 1) ∗ 912Å = 4642.08Å
(4.09 + 1) ∗ 1216Å = 6189.44Å
Both match the SED.
ID: 2932120903
z = 4.085
(4.085 + 1) ∗ 912Å = 4637.52Å
(4.085 + 1) ∗ 1216Å = 6183.36Å

30. 30
IDL CODE USED FOR SELECTION
;getting data from fits file table with indeces.
ftab_ext,'Table_for_Joakim_DES_COMMODORE_sample.fits',[1,3,4,5,6,7,8,9,10,11,12],ID,
g_mag,r_mag,i_mag,z_mag,Y_mag,err_G,err_R,err_I,err_z,err_Y
;ftab_ext,'sample_table.fits',[1,3,4,5,6,7,8,9,10],ID,g_mag,r_mag,i_mag,z_mag,Y_mag,err_G,
err_R,err_I
r_i=r_mag-i_mag
g_r=g_mag-r_mag
i_z=i_mag-z_mag
z_Y=z_mag-Y_mag
err_G=err_G
err_R=err_R
err_I=err_I
err_z=err_z
err_Y=err_Y
cgDisplay, 1000, 1000 ; making a box for my three plots
;premade calculations for detection criteria.
color_sel_z5_riz=r_i gt (2.0*(i_z)+0.9)
color_sel_z4_gri=g_r gt (1.8*(r_i)+1.05)
;~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
; REDSHIFT 4 CANDIDATES
index_z4_initial_sel=where(err_R lt 0.08 and err_I lt
0.06,flag_z4_initial_sel,/NULL,complement=index_not_z4_initial_sel,ncomplement=flag_no
t_z4_initial_sel)
if (flag_z4_initial_sel ne 0) then begin
r_i_initial_sel=r_i[index_z4_initial_sel]
g_r_initial_sel=g_r[index_z4_initial_sel]
color_sel_z4_gri_initial_sel=color_sel_z4_gri[index_z4_initial_sel]
ID_initial_sel=ID[index_z4_initial_sel]
cgplot,r_i_initial_sel,g_r_initial_sel,xtitle='r-i',ytitle='g-r',title='z ~ 4',$
xrange=[-2.,3.],xstyle=1,yrange=[-1.,3.],psym=3,color='grey',CHARSIZE=1.49,$
Position=[0.05,0.55,0.45,0.95] ; positioning my first plot in the top left corner
;selecting the candidates we want with criteria specific for z = 4.
index_z4=where(g_r_initial_sel gt 0.9 and r_i_initial_sel lt 0.8 and
color_sel_z4_gri_initial_sel,$
flag_z4,/NULL,complement=index_not_z4,ncomplement=flag_not_z4)
if (flag_z4 ne 0) then begin ;if they are found, begin.
;plotting these with a red colour, using overplot to plot over the original data points.

31. 31
cgplot,r_i_initial_sel[index_z4],g_r_initial_sel[index_z4],psym=6,color='red',/overpl
ot
;storing ID's with indeces.
ID_cands_z4=ID_initial_sel[index_z4]
;saving the candidates as an ascii file.
forprint, format='A',ID_cands_z4,comment='#ID',textout='ID_z4_candidates.dat'
endif
endif
;creating the cuts in the plot where candidates are expected to be found.
x_line1=[-2.,-1.,-0.083]
y_line1=[0.9,0.9,0.9]
x_line2=[0.8,0.8,0.8]
y_line2=[2.49,3.,4.]
;x_line3=[-0.083,0.8]
;y_line3=[0.9,2.49]
x_line3=[-0.083,0.3,0.8]
y_line3=(1.8*(x_line3))+1.05
cgplot,x_line1,y_line1,linestyle=2,thick=1, /overplot
cgplot,x_line2,y_line2,linestyle=2,thick=1, /overplot
cgplot,x_line3,y_line3,linestyle=2,thick=1, /overplot
WRITE_JPEG, 'Joakim_plot_sample4_z4.jpg', QUALITY=100,TVRD(/TRUE), /TRUE
;~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
;REDSHIFT 5 CANDIDATES
index_z5_initial_sel=where(err_I lt 0.06 and err_R lt 0.08 and err_z lt
0.06,flag_z5_initial_sel,/NULL,complement=index_not_z5_initial_sel,ncomplement=flag_no
t_z5_initial_sel)
if (flag_z5_initial_sel ne 0) then begin
i_z_initial_sel=i_z[index_z5_initial_sel]
r_i_initial_sel=r_i[index_z5_initial_sel]
color_sel_z5_riz_initial_sel=color_sel_z5_riz[index_z5_initial_sel]
ID_initial_sel=ID[index_z5_initial_sel]
cgplot,i_z_initial_sel,r_i_initial_sel,xtitle='i_z',ytitle='r-i', $
xrange=[-2.,3.],title='z ~ 5',xstyle=1,$
yrange=[-1,3],psym=3,color='grey',CHARSIZE=1.49,$
Position=[0.25,0.05,0.65,0.45], /NoErase
;selecting data that has these specifics for z = 5.
index_z5=where(r_i_initial_sel gt 0.9 and color_sel_z5_riz_initial_sel,flag_z5, $

32. 32
/NULL,complement=index_not_z5,ncomplement=flag_not_z5)
if (flag_z5 ne 0) then begin ;if they are found, begin.
;plotting these with a red colour, using overplot to plot over the original.
cgplot,i_z_initial_sel[index_z5],r_i_initial_sel[index_z5],psym=6,color='red', $
/overplot
;storing ID's with indeces
ID_cands_z5=ID_initial_sel[index_z5]
forprint, format='A',ID_cands_z5,comment='#ID',textout='ID_z5_candidates.dat'
endif
endif
;creating the cuts in the plot where candidates are expected to be found.
x_line1=[-2.,-1.,0.]
y_line1=[0.9,0.9,0.9]
x_line2=[0.0,1.1]
y_line2=[0.9,3.0]
cgplot,x_line1,y_line1,linestyle=2,thick=1, /overplot
cgplot,x_line2,y_line2,linestyle=2,thick=1, /overplot
WRITE_JPEG, 'Joakim_plot_sample4_z5.jpg', QUALITY=100,TVRD(/TRUE), /TRUE
;~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
;REDSHIFT 6 CANDIDATES
index_z6_initial_sel=where(err_I lt 0.06 and err_Y lt 0.06 and err_z lt
0.06,flag_z6_initial_sel,/NULL,complement=index_not_z6_initial_sel,ncomplement=flag_no
t_z6_initial_sel)
if (flag_z6_initial_sel ne 0) then begin
i_z_initial_sel=i_z[index_z6_initial_sel]
z_Y_initial_sel=z_Y[index_z6_initial_sel]
ID_initial_sel=ID[index_z6_initial_sel]
cgplot,z_Y_initial_sel,i_z_initial_sel,xtitle='z-Y',ytitle='i-z', $
xrange=[-2.,3.],title='z ~ 6',xstyle=1,$
yrange=[-1,3],psym=3,color='grey',CHARSIZE=1.49,$
Position=[0.50,0.55,0.90,0.95], /NoErase
;selecting data that has these specifics for z = 5.
index_z6=where(i_z_initial_sel gt 1.5 and z_Y_initial_sel lt 1.2,flag_z6, $
/NULL,complement=index_not_z6,ncomplement=flag_not_z6)
if (flag_z6 ne 0) then begin

33. 33
cgplot,z_Y_initial_sel[index_z6],i_z_initial_sel[index_z6],psym=6,color='red', $
/overplot
ID_cands_z6=ID_initial_sel[index_z6]
forprint, format='A',ID_cands_z6,comment='#ID',textout='ID_z6_candidates.dat'
endif
endif
x_line1=[-2.,-1.,1.2]
y_line1=[1.5,1.5,1.5]
x_line2=[1.2,1.2,1.2]
y_line2=[1.5,2.,4.]
cgplot,x_line1,y_line1,linestyle=2,thick=1, /overplot
cgplot,x_line2,y_line2,linestyle=2,thick=1, /overplot
WRITE_JPEG, 'Joakim_plot_sample4_z6.jpg', QUALITY=100,TVRD(/TRUE), /TRUE
close, /all
end