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Bachelor thesis project, VT-2019

Stellar populations in the Green Pea galaxy J1457+2232

Jan Malmgren

University of Stockholm, Astronomy department, Sweden

March 3, 2019

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The galaxy swings around

like a wheel of lighted smoke,

and the smoke is made of stars.

It is sunsmoke.

For lack of other words we call it sunsmoke,

do you see.

I don’t feel languages are equal

to what that vision comprehends.

The riches of the languages we know,

Xinombric, has three million words,

but then the galaxy you’re gazing into now

has more than ninety billion suns.

Has there ever been a brain that mastered all the words

in the Xinombric language?

Not a one.

Now you see.

And do not see.

ANIARA (poem 85), Harry Martinsson

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Abstract

In this report I present a study of possible age gradients in the Green Pea galaxy

J145735.13+223201.8 to be able to conclude if there is an extended star forming history in such

a galaxy. Data are coming from two different sources, highly resolved images in four different

wavelengths of stars in the galaxy, and of nebular gas in a narrow band H Balmer line filter,

from the Hubble Space Telescope1 (HST), as well as spectral line information from the Sloan

Digital Sky Survey2 (SDSS).

I compare the observations with stellar population models from two different libraries,

Yggdrasil and Starburst99. Due to the highly resolved images from HST this is one of the first

studies of spatially resolved stellar populations in a Green Pea galaxy. With the help from

these spatially resolved images it was possible to study star clumps independently from each

other. This would not be possible when using only data from SDSS. In this way it was

possible to conclude an age difference between the centre of the galaxy and its outskirts. I

found that the galaxy has an age gradient at a confidence level greater than 95%.

1 Based on observations made 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. These observations are

associated with program #9368134 2 Funding for the Sloan Digital Sky Survey IV has been provided by the Alfred P. Sloan Foundation, the U.S. Department of Energy Office

of Science, and the Participating Institutions. SDSS acknowledges support and resources from the Center for High-Performance Computing

at the University of Utah. The SDSS web site is www.sdss.org.

SDSS is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS Collaboration including the

Brazilian Participation Group, the Carnegie Institution for Science, Carnegie Mellon University, the Chilean Participation Group, the

French Participation Group, Harvard-Smithsonian Center for Astrophysics, Instituto de Astrofísica de Canarias, The Johns Hopkins University, Kavli Institute for the Physics and Mathematics of the Universe (IPMU) / University of Tokyo, the Korean Participation Group,

Lawrence Berkeley National Laboratory, Leibniz Institut für Astrophysik Potsdam (AIP), Max-Planck-Institut für Astronomie (MPIA

Heidelberg), Max-Planck-Institut für Astrophysik (MPA Garching), Max-Planck-Institut für Extraterrestrische Physik (MPE), National Astronomical Observatories of China, New Mexico State University, New York University, University of Notre Dame, Observatório Nacional

/ MCTI, The Ohio State University, Pennsylvania State University, Shanghai Astronomical Observatory, United Kingdom Participation

Group, Universidad Nacional Autónoma de México, University of Arizona, University of Colorado Boulder, University of Oxford, University of Portsmouth, University of Utah, University of Virginia, University of Washington, University of Wisconsin, Vanderbilt University, and

Yale University.

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Content 1. Introduction .................................................................................................................... 6

2. The Data ......................................................................................................................... 7

2.1. The Data from HST ................................................................................................. 8

2.2. The Data from SDSS ............................................................................................... 8

3. Estimating galaxy properties from SDSS data ............................................................... 9

3.1. Estimating the metallicity ........................................................................................ 9

3.2. Estimating the reddening ....................................................................................... 10

3.3. Estimating the star formation rate (SFR) ............................................................... 11

4. Photometric analysis of the galaxy ............................................................................... 11

4.1. Identifying star clumps .......................................................................................... 11

4.2. Flux measurements ................................................................................................ 13

4.2.1. Basic considerations ....................................................................................... 13

4.2.2. Star clumps measurements ............................................................................. 13

4.2.3. Surface brightness .......................................................................................... 14

4.2.4. Compactness of the galaxy ............................................................................. 16

4.3. Magnitude measurements ...................................................................................... 16

4.3.1. Converting to magnitude ................................................................................ 16

4.3.2. Calculating error in magnitude difference ...................................................... 17

4.3.3. Colour maps of the galaxy .............................................................................. 17

4.3.4. Colour difference vs. colour difference .......................................................... 18

4.4. Suggestions for future improvements .................................................................... 19

5. Discussion ..................................................................................................................... 20

5.1. Age gradients in the galaxy ................................................................................... 20

5.1.1. Age by colour maps and surface brightness ................................................... 20

5.1.2. Age estimate by colour difference vs. colour difference analysis .................. 21

5.1.3. Age estimate by Hline strength .................................................................... 22

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5.1.4. Age map based on Hline strength ................................................................ 23

5.1.5. Age estimate by colour surface plots.............................................................. 24

5.2. Is our galaxy really a Green Pea galaxy? .............................................................. 26

6. Conclusions .................................................................................................................. 27

7. Acknowledgement ........................................................................................................ 28

8. References .................................................................................................................... 30

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1. Introduction

The Universe contains an almost un-numerable amount of galaxies. Each galaxy contains

anywhere from 106 to 1012 stars. The galaxies comes in many different types and shapes, even

colours are different. As most of the light comes from the stars they will determine the colour

of the galaxy. The most massive stars are the hottest and are therefore the bluest. Stars with

lower masses will be cooler and have a redder light, even going into infrared for the smallest

stars. As the most massive stars burn their nuclear fuel much faster than less massive stars they

will have a shorter life, in the order of a few Myr, on the main sequence. They leave the main

sequence when they have exhausted all hydrogen in the core and become red giants or

supergiants. This means a bluer part in a galaxy contains young stars, or red parts of the galaxy

is older due to the lack of young blue stars. Regarding types and shapes there are the elliptical

ones, with very low star formation rate (SFR), spiral ones with spiral arms like our Milky Way

with most of its star formation in the disk. In between elliptical and spiral ones we have the

lenticular ones with a rotating disc and a bulge but with no spiral arms. Then there are irregular

galaxies that lack a clear structure. In this report we will look at one type of these irregular

galaxies, namely a Green Pea galaxy, with very high SFR.

Green Pea galaxies are a sub-class family of star forming galaxies at redshift around 0.1 - 0.3.

The characteristic green colour is a result of extremely bright nebular line emission in the

[OIII]5007 line. Green pea galaxies are characterized by low mass 108.5-1010 M⊙, high SFR ≳10

M⊙/yr, EW[OIII]5007 typically > 200Å, low metallicity 12+log(O/H) of 7.6 - 8.4 and low

reddening E(B-V) < 0.26 (Cardamone, et al., 2009; Izotov, Guseva, & Thuan, 2011).

For the reionization of the Universe it is believed compact (dwarf) galaxies can be a major

contributor (Verhamme, et al., 2016). These compact galaxies formed first and then they

merged to become other larger types of galaxies through hierarchical clustering (White & Rees,

1978). Therefor the study of the formation of these early compact galaxies at high redshift is

important to understand the reionization of the Universe. However at high redshifts it is difficult

to study these galaxies due to their faintness, disturbances by lower redshift sources and

attenuation by the Intergalactic Medium, IGM. To overcome this difficulty a possibility is to

study compact galaxies at much lower redshifts. These local compact galaxies are similar to the

high redshift ones in terms of masses, metallicity, SFR and compactness (Verhamme, et al.,

2016) and can be used as alternatives to study. Green Pea galaxies have these types of properties

and are therefore important to study to understand the reionization of the Universe. It has been

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confirmed that some of these Green Pea galaxies are Lyman continuum (LyC) leakers (Izotov,

et al., 2018).

2. The Data

Our galaxy is J145735.13+223201.7 and is around 10x10kpc as seen from the Earth, see Figure

1. The redshift, z, to the galaxy is 0.148. The age at this redshift 11.9Gyr and the light has been

travelling for around 1.9Gyr to reach us. Distances to the galaxy is summarized in Table 1

below.

Mpc Mlyr

Proper distance, Dp 635 2070

Angular distance, DA 553 1802

Luminosity distance, DL 728 2378 Table 1. Distances

All the distances are calculated based on the Standard cosmological model,

the ΛCDM (Lambda Cold Dark Matter) universe with Ωm,0 = 0.308, Ωr,0 = 8.4*10-5 and Ω,0 =

0.692 and H0 = 67.8km/s/Mpc (Planck Collaboration XIII, 2015).

Figure 1. The galaxy, the red circle is giving the size of the SDSS aperture (radius is 1.5 arcsec) for our data

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2.1. The Data from HST

I have used imaging data from the Hubble Space Telescope (HST) from the cameras ACS and

WFC3 in four different filters. Two of the filters are long pass (LP) used for far UV and near

infrared and two are wide (W) used for UV and blue. The filters used are F150LP, F390W,

F475W and F890LP, see Figure 2. In this study no R-filter has been used as the [OIII]5007 line

is very strong for Green Peas galaxies and this line will fall within the R-filter band at this

redshift. For each filter I have used one image containing the flux values and one image

containing the weight values, i.e. the inverse of the variance of the measured flux values. In

addition to these images I also used an H equivalent width map from HST narrow band

imaging that was obtained as part of the observational HST programme ID: 14131. The

continuum subtraction was done using the same method as for LARS (Östlin, et al., 2014) and

will be further analysed in Rasekh et al. (in prep.). The observed Hemission comes from

recombination of hydrogen atoms in the ionized nebulae close to star forming regions in the

galaxy. The relative strength of the line (the equivalent width) varies with the age of the stellar

population (Leitherer, et al., 1999).

All these images were cut out of the original images from HST and were 800 by 800 pixels

large and were already reduced when I received them. Apart from the standard reductions (bias

removal, flat-fielding and stacking) the images were also Point Spread Function (PSF) matched

using the techniques described in (Hayes, et al., 2016). The physical coordinates for the furthest

left/down corner pixel at x = 1 and y =1 are 14:57:36:3106 and 22:31:45:331. Please note this

corresponds to the pixel definition in the SAOImageDS9 image viewing tool where an integer

coordinate refers to the middle of the pixel. Throughout this report I refer to this definition of

coordinates if otherwise not stated. The size of one pixel is 0.04 arcsec corresponding to 107.15

pc.

2.2. The Data from SDSS

The SDSS spectral line data used are coming from the SDSS DR15 catalogue, estimated using

the method in (Tremonti, et al., 2004; Brinchmann, et al., 2004) and in the standard 1.5 arcsec

radius aperture. Key spectral data from SDSS can be found in Table 2 and the 1.5 arcsec radius

aperture is indicated in Figure 1 as a red circle. Other data used from SDSS is the mass estimate

of the galaxy, MG = 108.62 M⊙ ≈ 420*106 M⊙.

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Figure 2. Transmission curves for filters used

H H [OII]3727 [OII]3729 [OIII]4959 [OIII]5007

EW [Å] 924 195 22 102 102 414 1264

Flux [10-17 erg/s/cm2/Å] 2326 765 48 340 372 1860 5510 Table 2. SDSS spectral data

3. Estimating galaxy properties from SDSS data

3.1. Estimating the metallicity

In order to compare the photometry to stellar populations’ evolutionary tracks an estimate of

the metallicity in the galaxy is needed. I have used a combination of different indicators to

estimate the metallicity, Z. I have chosen to use the flux ratios R23, N2 and R3 as defined

equation 1, 2 and 3. The values for our galaxy can also be found here.

R23 = ([OIII]5007 + [OIII]4959 + [OII]3727) / H

/ H

R3 = [OIII]5007 / H

The fluxes for these spectral lines were taken from the SDSS data (http://skyserver.sdss.org).

These three indicators were then used in the metallicity calculator developed by the National

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Institute for Astrophysics in Italy (http://www.arcetri.astro.it) based on the models developed

by M Curtis et al. (Curti, et al., 2016). From this metallicity calculator we got the 12 +

log(OG/HG) to be 8.0 for our galaxy. This corresponds to an OG/HG ration of 0.00010. To get

the metallicity for our galaxy, ZG, we compare this O/H ratio with the sun and scale the Z value

of the sun accordingly, see equation 4 and 5. I have used the sun values as defined by Martin

Asplund et al. (Asplund, Grevesse, Sauval, & Scott, 2009), Z⊙ = 0.0134 and O⊙/H⊙ = 0.00049.

ZG = Z⊙ * (OG/HG) / (O⊙/H⊙) = 0.0027 (4)

ZG = 0.20 * Z⊙ (5)

3.2. Estimating the reddening

I have used the ratio between Hand H, the Balmer decrement, to estimate the reddening

(Cardelli, Clayton, & Mathis, 1989). The main idea behind this is that the emitted theoretical

value of H/ His 2.86 (Storey & Hummer, 1995) and the extinction of H is higher than for

H as longer wavelengths are less affected by dust extinction. To calculate the colour excess in

our spectra, E(B-V), we use the extinction law as per equation 6 and then calculated the

corresponding extinction A() as per equation 7 (Calzetti, Kinney, & Storchi-Bergmann, 1996),

please also see paragraph 4.4. The Robs is the observed HHand is 3.039 for our galaxy based

on the SDSS data, the Rint is the intrinsic HHand is 2.87 as per (Osterbrock, 2005). The k()

is the extinction law as discussed in (Cardelli, Clayton, & Mathis, 1989).

E(B-V)log(Rint/Robs)/0.4[k() – k()] (6)

A() = k() * E(B-V) (7)

To calculate k() I used the pivot wavelength for our four different filter, i.e. the PHOTPLAM

value from the respective image. I have calculated the reddening factors as A(F150LP) = 0.57,

A(F390W) = 0.32, A(F475W) = 0.27 and A(F850LP) = 0.13. These will, for example, be used

in the colour diff vs. colour diff plots in paragraph 4.3.4 to calculate the reddening impact of

the Yggdrasil model used for age estimation. The value of E(B-V) is 0.057. This value is

probably an upper limit for the central part of the galaxy as it is measured on the nebula and not

directly on the stars (Calzetti, et al., 1999).

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3.3. Estimating the star formation rate (SFR)

The star formation rate was estimated based on Hemission line flux (Kennicut & Evans, 2012;

Murphy, et al., 2011; Hao, et al., 2011).

For de-reddening of the Hline flux I used equation 8 and to calculate the SFR I used equation

9. The calibration constant, log(Cx), is 41.27 as per (Kennicut & Evans, 2012). This gives a

SFR estimate of 9.46 M⊙/yr.

LH-dered = LH-obs * 10E(B-V)* k(

log(dM/dt) = log(LH-dered) – log(Cx) (9)

4. Photometric analysis of the galaxy

4.1. Identifying star clumps

I used the F475W image to identify possible star clumps as it is the most overall deepest image.

I selected the fourteen most contrast rich clumps by eye, see Figure 3. The coordinates for these

manually selected star clumps were used as an input to the centroid function in the Python

photometric package Photutils to calculate better centred coordinates. The algorithm I chose

used a basic momentum calculation to find “centre of mass” within a circular area with radius

3 pixels. With the new coordinates the flux was measured with a circular aperture with radius

2 pixel. The optimized coordinates and the flux for the four different filters are listed in Table

3, flux values given in 10-18 erg/s/cm2/Å. These flux values are not the total flux of each clump

as our aperture is too small to measure this. However we are only concerned that the relative

fluxes (colours) of the clumps are correct and have the same spatial resolution.

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Figure 3. The fourteen selected star clumps

Clump x y F150LS F390W F475W F850LS

1 390.77 408.30 1.98 0.46 0.37 0.10

2 407.49 377.55 1.86 0.46 0.50 0.18

3 376.65 346.83 0.45 0.17 0.20 0.07

4 377.42 350.26 0.72 0.19 0.21 0.07

5 410.26 396.60 4.51 0.97 0.68 0.15

6 401.24 398.72 83.99 14.38 9.85 1.97

7 405.43 365.75 0.42 0.13 0.13 0.05

8 393.06 397.53 4.76 1.03 0.68 0.15

9 398.35 363.45 0.53 0.16 0.16 0.06

10 427.49 388.69 0.84 0.18 0.15 0.05

11 398.93 394.32 16.43 3.97 2.77 0.61

12 369.83 349.58 0.33 0.11 0.11 0.04

13 380.65 363.42 0.34 0.13 0.11 0.03

14 403.38 403.38 44.75 8.85 5.82 1.15

Table 3. X- and y-coordinates and optimized flux values for the fourteen clumps. Flux in 10-18 erg/s/cm2/Å

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4.2. Flux measurements

4.2.1. Basic considerations

Before any measurement was done all the images were calibrated with the use of the

PHOTFLAM value taken from the image header from the respective image. Please note the

Python programming language have the coordinate defined as the top/right corner of a pixel.

This means for example that an x-coordinate in Python of 11 corresponds to 10.5 in the

SAOImageDS9 image viewing tool and so forth.

4.2.2. Star clumps measurements

The flux from the fourteen clumps were measured with the Python photometric package

Photutils with a circular aperture of radius two pixels. The error for the flux was estimated in a

similar way. But here I performed the photometry on the square of the error from the weight

images and taking the square root of the sum to get the total error in each clump.

Then we needed to remove the background flux to get a flux value significant for individual

clumps. For this I used an annulus aperture. I tried first to use the mean flux value in the annulus

to estimate the background flux. However it was very difficult to get an annulus big enough to

get a representative value of the background flux without starting to get into other clumps. Some

of the clumps are close to other high flux area, especially the ones close to the centre of the

galaxy, limiting the possibility to increase the annulus area. Rather than to use the mean value

I decided to use the median value. In this way I could increase my annulus and get a more

representative value for the background flux. The inner and outer radius of the annulus selected

are shown in Table 4.

Table 4. Inner and outer radius, r, of the annuluses used for measuring the background flux

With this data at hand I calculated the signal to noise (S/N) for the fourteen clumps by taking

the background-subtracted flux and divided it by the error, see Table 5. For the remainder of

this report I have discarded any flux measurement with a S/N of less than three, marked as grey

in Table 5, as they are not significant enough for further studies.

Clump 1 2 3 4 5 6 7 8 9 10 11 12 13 14

Inner r 3.36 4.78 3.55 4.16 9.65 9.65 3.17 9.65 3.30 3.64 9.65 2.58 3.12 9.65

Outer r 4.77 7.74 5.10 5.75 13.67 13.67 4.81 13.67 5.29 5.04 13.67 3.81 5.00 13.67

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Clump F150LP F390W F475W F850LP

1 3.39 8.78 15.03 7.01

2 4.68 13.29 24.53 15.71

3 0.80 4.90 11.69 5.49

4 1.95 5.74 11.98 5.17

5 11.47 31.56 35.43 11.32

6 59.60 156.07 174.30 82.54

7 0.63 1.44 3.27 1.96

8 11.39 32.14 35.22 10.90

9 0.77 2.31 5.96 2.49

10 1.81 4.351 5.06 0.67

11 26.45 79.11 90.16 42.35

12 0.60 1.79 2.89 1.74

13 0.14 3.087 3.59 0.59

14 45.58 129.70 142.03 58.06 Table 5. S/N for the fourteen clumps. Grey values have S/N <3 and are discarded for further studies

4.2.3. Surface brightness

The centre of the galaxy, where the flux value is the highest, is at 14:57:35:1544 and

22:32:01:271 corresponding to pixel number x = 401 and y = 400. The increment in radius was

selected to 2 pixels and the value at pixel 0 in the plot corresponds to the average value in the

annulus with outer radius = 2 etc., please see Figure 4. Blue arrows indicate an error bigger than

0.6 mag. The error grows to + 2.1 and - 0.8 mag for the very last data point. Figure 5 shows an

expanded view of the surface brightness closer the galaxy centre.

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Figure 4. Surface brightness

Figure 5. Expanded view of surface brightness

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4.2.4. Compactness of the galaxy

The compactness is showing how dense the galaxy is in terms of stars. In Table 6 below the

calculated R50 can be found (R50 is the radius that encloses 50% of the total surface brightness

flux). The bluer light indicates the compactness of younger and hotter stars and the redder light

shows the distribution of older and cooler stars. An older star population has existed for a longer

time and has had longer time to be impacted by mutual gravitation and have received higher

speeds due to this and the kinetic energy has gone up. Based on the virial theorem the sum of

the potential energy and the kinetic energy is constant. As a result the potential energy must go

down when kinetic energy goes up. This means that older star populations must be spread out

to lower the potential energy and the compactness goes down as a consequence. As can be seen

we have a good correlation with this as we have a smaller radius, i.e. higher compactness, for a

shorter wavelength. Actually the compactness is three times higher for the F150LP filter versus

the F850LP.

F150LP F390W F475W F850LP

pixel 5.8 8.2 9.45 17

pc 621 879 1013 1822

lyr 2027 2866 3302 5941 Table 6. R50 compactness of the galaxy

4.3. Magnitude measurements

4.3.1. Converting to magnitude

AB-magnitudes are used throughout this report. I used equation 10 to calculate the zero pint

(ZP) and equation 11 to convert from flux to AB magnitudes (www.stsci.edu, n.d.), f() is the

flux value for the wavelength in question. The PHOTFLAM values were taken from the image

header for each filter.

ZPAB = −2.408 – 5 * log(PHOTPLAM) (10)

MagAB = ZPAB – 2.5 * log[f()] (11)

To be able to convert flux to magnitude it was necessary to convert all negative flux values to

positive to be able to take the logarithm of the flux. I selected to replace all the negative values

with the lowest positive flux value in the respective image. This was performed for all the four

images. To be clear these negative values were all outside the galaxy defined by a S/N > 3.

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4.3.2. Calculating error in magnitude difference

For calculating the error in the magnitude difference I used error propagation equation 12 where

F is a function with two variables and is giving the error in F as a function of the errors in the

variables.

(F)2 = (x)2 * (dF/dx)2 + (y)2 * (dF/dy)2 (12)

In our case F = MagAB(1 – 2) = [ZPAB(1) – 2.5 * log[f()] - [ZPAB(2) – 2.5 * log[f()].

This gives dF/dx = -2.5 * 1/[x*ln(10)] and dF/dy = -2.5 * 1/[y*ln(10)] and substituting x and y

with our flux values f(and f() we get equation 13 below.

[MagAB(1 – 2)]2 = 1.18 * [f(/f(]2 + [f(/f(]2 (13)

Interesting to notice is that f(/f(is the same as the inverted S/N ration for the flux values.

4.3.3. Colour maps of the galaxy

To cut out the galaxy from the images I used a signal to noise mask with an S/N value for the

flux of three or higher. This means that the galaxy is defined where the image pixels have an

S/N higher than three. I used the F475W image as a base for this as it is the overall deepest

image.

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With the four filters I calculated three colour difference maps, see Figure 6 below. They are

showing the difference in magnitude and a blue colour indicates the former wavelength is the

stronger and red colour indicates the latter is the strongest of m(1) – m(2).

Figure 6. Colour difference, top left panel shows 150 – 390nm, the top right panel is showing 390 – 475nm and the bottom

panel is showing 475 – 850nm.

4.3.4. Colour difference vs. colour difference

The nine clumps with an S/N higher than three in the selected filters are plotted in a colour

difference 390 - 475nm vs. 475 - 859nm chart, see Figure 7. In the same chart Yggdrasil

(Zackrisson, Rydberg, Schaerer, Östlin, & Tuli, 2011) spectral galaxy evolution models have

been plotted with the age steps in the green encircled figures. I have used Kroupa IMF (Kroupa,

2001), fcov = 1 (maximal nebular contribution and no escape of Lyman continuum photons) and

instantaneous burst. Instantaneous burst will be better than using a constant star formation rate

model as we are here looking at individual star clumps who have single stellar populations.

The red arrow indicates how the Yggdrasil model would move in the chart should it be exposed

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to the same reddening as the galaxy, see 3.2. The fairly extensive “loop” in the model is caused

by the most massive stars exhausting there hydrogen fuel quickly and turning first into red

supergiants moving the line quickly upwards and after when they leave the red supergiant phase

the line falls back again. To understand this mechanism better one would need to analyse the

Yggdrasil model in more details.

Figure 7. Colour diff. vs. colour diff with Yggdrasil spectral galaxy evolution model with z=0.004, Kroupka IMF, fLym=0,

fcov=1 and instantaneous burst. The red arrow indicates the reddening direction for the Yggdrasil model. The green encircled

numbers indicated the age from the Yggdrasil model (5, 10, 15, 30, 50, 100, 200, 500).

4.4. Suggestions for future improvements

Should there be an interest to do further studies on this galaxy I would like to propose some

ideas for improvements. One of the most important matters is to optimize the measured flux in

the selected clumps. The higher the flux value the better S/N ration making the measurements

more significant. I used a fairly simple basic momentum calculation to find “centre of mass” of

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each clump. However this method actually gives a higher weight to a pixel further away from

the “clump centre” than one closer to the centre. This is actually the wrong way around. It would

be better to use a method that reduces the weight with the distance from “clump centre”. This

of course require you have a reasonably good estimation where the centre of the clump is.

Further I suggest also to include error calculation when estimating the metallicity and

reddening. This information is available in the SDSS data and one would get a better

understanding of how these error would impact the results.

I suggest also to ask for Yggdrasil SED models for our z value of 0.0027. I had access to z

0.004 and 0.0004 and there is quite a difference in the result between these values.

I also want to highlight the calculation of reddening in 3.2 refers to (Calzetti, Kinney, & Storchi-

Bergmann, 1996). In this reference there is a typo in the formula (formula number 6) giving a

negative E(B-V). This is corrected in this report. Interesting to notice is also that there are

scientific articles referencing this article (Calzetti, Kinney, & Storchi-Bergmann, 1996) without

pointing this out.

5. Discussion

5.1. Age gradients in the galaxy

5.1.1. Age by colour maps and surface brightness

The colour maps in Figure 6 are clearly showing a colour gradient between the centre of the

galaxy and the outer areas. Both in the top right panel, the 390 – 475nm magnitude difference,

and in the bottom panel, the 475 – 850nm magnitude difference, we can see the centre of the

galaxy is significantly bluer than the rest. This is showing there are still active massive blue

stars in the centre but not in the outskirts of the galaxy. This means the centre must be young,

in the order of a handful Myr, or these massive blue stars would have left the main sequence to

become red giants. So qualitatively we can now state there is an age gradient in this galaxy.

This is also visible in the surface brightness plots, Figure 4 and Figure 5. We can also here see

the more blue colours are stronger in the centre of the galaxy and the red colour getting stronger

in the outskirts of the galaxy.

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5.1.2. Age estimate by colour difference vs. colour difference analysis

Looking at the colour diff vs. colour diff plot in Figure 7 we can see a clear evidence for an age

gradient. Clumps 1-4 are positioned quite close together, as are clumps 5, 6, 8, 11 and 14. These

two clusters are well separated from each other. The error indicated is for one corresponding

to a 68% confidence level. In Figure 8 the same figure is plotted with twice the error, i.e. getting

a two error corresponding to a 95% confidence level. Even here we can clearly see the age

gradient between these sets of clumps. The central part of the galaxy, clumps 5, 6, 8, 11 and 14,

is positioned somewhere between Yggdrasil age estimate of 5 and 15 Myr if the reddening is

taken into account. From this we can really only indicate the age is close to either of these two

estimates. Please note the 10Myr estimate from the Yggdrasil model actually is much further

away from these clumps so this age is less likely to be valid for this part of the galaxy. However

in 5.1.3 and 5.1.4 we will see the age is estimated to less than 5Myr. The discrepancy between

the Yggdrasil model and the measurements can well be caused by the error we have in

metallicity. We have used a metallicity of 0.004 in Figure 7 but the estimated metallicity for

the galaxy is 0.0027. Further the extinction can be higher than what we have estimated. We

have estimated the extinction based on an average of the SDSS aperture on the nebula. It is

possible the stellar extinction can be different to the nebular one as well as the extinction can

be different outside the SDSS aperture. In fact preliminary SED fitting of the clumps indicate

that the Yggdrasil models needs more reddening than the assumed E(B-V) from the Balmer

decrement to find good fits to the data (private communication K. Hollyhead). Nevertheless,

the best-fit ages are perfectly consistent with the more quantitative study presented here.

The matters discussed above could explain the discrepancy we have between measurement and

the Yggdrasil model, please see 4.4 for some comments about this. The clump 1 appear to be

younger than clumps 2, 3 and 4 at around 50Myr, but this is only significant to one or a bit

less, see Figure 7. Clumps 2, 3 and 4 appear to be the oldest ones with an age of around 100Myr

old taking the reddening into account.

22

Figure 8. Colour diff vs. colour diff as in figure 7 with 2x error (two .

5.1.3. Age estimate by Hline strength

I have also used the equivalent width (EW) of H to estimate the age of the central parts of the

galaxy (Leitherer, 2004). In Figure 9 we can see age vs. H EW given by the modelling work

in Starburst99 (Leitherer, et al., 1999; Vazquez & Leitherer, 2004; Leitherer, et al., 2014) The

HEW for our galaxy is taken from the SDSS data (http://skyserver.sdss.org) and our EW(H)

= 923.9 Å and log(923.9) = 2.966. Assuming a Salpeter IMF (Salpeter, 1955) with = 2.35

(Oey, 2012), Mup = 100 M⊙ and M = 106 we get an estimated age of 4.5Myr. With = 3.30 we

get an estimated age of 3.7Myr. The - parameter is used to describe the number of stars with

masses between m and m+dm in a specific volume as proportional to m- A lower gives a

more “top heavy” profile.

23

Figure 9. Modelled age versus EW for HSolid line assumes an IMF with = 2.35 and Mup = 100 M⊙, dotted line = 3.30

and Mup = 100 M⊙, dashed/dotted line = 2.35 and Mup = 30 M⊙. The metallicity used is Z = 0.004, Mlow =1 M⊙ and M =

106 M⊙ for all lines. The model data was obtained from (http://www.stsci.edu/science/starburst99)

5.1.4. Age map based on Hline strength

I have used the same Starburst 99 model as in 5.1.3 with = 2.35 to create the age map, see

Figure 10. The left panel is showing the EW(H). The right panel is showing the age estimate.

We can clearly see the age around the centre of the galaxy is around 3Myr and decaying towards

the outskirts of the galaxy.

24

Figure 10. The left panel is showing the H EW where negative values are replaced by zero EW (black). The right panel shows

the age with noisy areas masked out and shown as black. The black areas marked by green contours indicates areas that have

a flux value in the F475W filter with S/N < 3. The remaining black areas not enclosed by the green contours are areas with no

valid EW measurement.

Looking at the model in Figure 9 we can see that an age of around 7-8Myr corresponds to an

EW of 100Å. At this level of EW and lower it is difficult to detect the H flux values and the

uncertainty in the EW increase. As a result the oldest areas we can really identify are limited to

around 7-8Myr. For older areas, coloured orange to white in Figure 10, the age estimate is

uncertain. Areas with less than a 1 Å EW has been coloured black, see Figure 10. Other black

areas enclosed by a green contour indicates areas with lower S/N < 3 (except for the galaxy

itself!). Based on this the age estimate is uncertain in the outer parts of the galaxy where the

HEW is low. It is interesting to notice that the areas around the clumps 2, 3 and 4 actually are

black in the age map indicating that these areas at least are older than 20Myr.

Please notice the small “dent” in the blue solid line in Figure 9. For a log(EW(H value of

around 3.1 the model does not give a unique age estimate. This means that in the age map,

Figure 10 right panel, there is no age estimate between around 3 and 4Myr as I have chosen to

indicate the lower age estimate, i.e. 3Myr, in this log(EW(H interval where this “dent” is.

5.1.5. Age estimate by colour surface plots

In Figure 11 we can see the measured colour surface magnitude and corresponding magnitude

from the Yggdrasil model plotted for two different colour differences, 390 – 475nm and 475 –

890nm. With the aid of the solid black lines, referring to the centre of the galaxy, we can see in

the top panel that the 390 – 475nm magnitude difference is indicating an age of 5.4Myr. In the

same way we can see in the bottom panel the 475 – 890nm magnitude difference is indicating

25

an age of 7.4Myr for the centre of the galaxy. If we include the reddening effect these age

estimates should have been younger. Also here we can see that the Yggdrasil model gives

slightly higher age estimates than the age estimates given by the HEW in 5.1.3 and 5.1.4.

In a similar way we can estimate the age further out from the galaxy centre. As the noise it

getting higher here I have taken the weighted average of the magnitudes from pixel 30 to 70,

corresponding to 3200pc to 7500pc, see equation 14. I have used the inverted variance from the

weight images as the weight, wi.

< 𝑚𝑎𝑔 > = ∑ (𝑤𝑖 ∗ 𝑚𝑎𝑔𝑖)/ ∑ 𝑤𝑖𝑖𝑖 (14)

With the aid of the blue dot/dashed line, showing the weighted average magnitude, in the top

panels in Figure 11 we get an estimated age of 19.5Myr. Please note we have a “dent” in this

line giving actually several different possibilities to read out the age. I chose to read out the

oldest estimate as we know from other estimations that the galaxy outskirts should be

considerably older than the centre. In the same way, using the green dot/dashed line, also

showing the weighted average magnitude, in the bottom panels in Figure 11, we get an age

estimate of 140Myr. If we include the reddening effect we would get somewhat lower age

estimates.

In Figure 11 we can see the measured colour difference does not give the expected result as if

we would have a single star population. Then we should have had a more monotonically

“reddening” of the surface colour difference with increased radius. However in our galaxy we

have several star clumps indicating many different star populations are scattered across the

galaxy. In Figure 11 I have indicated the position of some of our star clumps and we can actually

see a significant impact of the surface colour difference measurements at these radiuses. This

will impact our age estimations at higher radiuses. If we restrict us to the part of the galaxy

where we have a monotonic “reddening” of the surface colour difference we should discard the

age estimate based on the blue dot/dashed line in the top panels.

An alternative to use an instantaneous star burst model in Yggdrasil, which assumes a single

stellar population, is to use a constant star rate formation model. This could possibly give better

results in the age estimation as we have several different star populations with different ages.

26

Figure 11. Surface colour

5.2. Is our galaxy really a Green Pea galaxy?

It is clear that this galaxy was already confirmed as a Green Pea galaxy as part of the study by

Cardamone et al. (Cardamone, et al., 2009). However I want to reflect about the main

characteristics of our galaxy in comparison to a Green Pea galaxy.

In Table 7 a comparison between our galaxy and typical Green Pea galaxy can be found. It is

clear our galaxy is of Green Pea type as it should be. In Figure 12 the position of our galaxy,

marked in red, in comparison with LyAlpha Reference Sample galaxies (LARS) from the LARS

project that are studying local Lyα emitting dwarf galaxies, Melinder et al. (in prep). The grey

cloud in Figure 12 is the ~ 105 galaxies from the study by Brinchmann et al. (Brinchmann, et

al., 2004) of the physical properties of star forming galaxies with z < 0.2. We can see our galaxy,

27

as a starburst galaxy, has the highest SFR at its mass range and even have higher SFR than most

of the more massive galaxies.

Our galaxy Green Pea Reference

Mass 108.67 M⊙ 108.5-1010 M⊙

(Cardamone, et al.,

2009)

SFR 9.46 M⊙/yr ≳10 M⊙/yr

(Cardamone, et al.,

2009)

EW [OIII]5007 1264 Å typ. > 200 Å

(Cardamone, et al.,

2009)

12+log(O/H) 8.0 7.6 - 8.4 (Izotov, Guseva, &

Thuan, 2011)

E(B-V) 0.057 < 0.26

(Cardamone, et al.,

2009) Table 7. Comparison between our galaxy and a typical Green Pea galaxy

Figure 12. The position of our galaxy vs. LyAlpha Reference Sample galaxies (LARS) from the LARS project. The grey cloud

is the ~ 105 galaxies from the study by Brinchmann et al

6. Conclusions

I have used several different ways to estimate the age, such as HEW, colour maps, colour

difference vs. colour difference and surface brightness plots. The data I have used is coming

from two different sources, SDSS and HST, and I have used two different models, Yggdrasil

and Hmodel from Starburst99. All these ways to estimate the age are giving a clear indication

that we have an age gradient in this galaxy.

28

The work performed in 5.1.2 gives a strong indication, better than 95% confidence, that the

galaxy has an age gradient.

Further the Hbased age estimation in 5.1.3 and 5.1.4 gives also a clear indication that we have

a young population of stars in the centre of the galaxy with an age between 3 - 4Myr. Looking

at the colour difference plot in 4.3.4 vs. the Yggdrasil model we can see a young centre with an

age of 5 Myr or slightly more. The outer parts of the galaxy is estimated to have an age of

around 100Myr. There is also an indication that there are areas that lie somewhere in between

with clump 1 being around 50Myr old based on 5.1.2. However this indication is only

significant to 1 or slightly less. If we estimate the age for clump 1 with the H EW we get an

age of around 5Myr i.e. just a few Myr older than the central clumps.

It is interesting to notice that by using the SDSS data only we would not have been able to

conclude there is an age gradient between the centre and the outskirts of the galaxy. For

concluding this spatially resolved images from HST were needed.

Finally looking at the surface colour plots in 5.1.5, the surface brightness spectra in 4.2.3 and

the colour maps in 4.3.3 they are all indicating a young centre and an age decaying with the

radius of the galaxy. The surface colour plot indicates also an age at the outer areas of the galaxy

to 140Myr. If we take the reddening into account we would get an estimate of around 100Myr.

A Green Pea galaxy like our one can be a good candidate as a Lyman Continuum (LyC) leaker.

It has extended star formation with an age range from the younger star populations of 3-4Myr

in the centre, through clump 1 with an age of 5Myr (based on H EW, a bit older based on

Yggdrasil) to older populations of 50-100Myr. I.e. as our galaxy is multi-age and spatially

distributed we can have Supernovae (SN) from an older generation of stars open up channels

of fully ionized gas through which LyC photos can escape from a younger population of stars

(Clarke & Oey, 2002; Micheva, Oey, Keenan, Jaskot, & James, 2018; Jaskot & Oey, 2013).

Jaskot and Oey has actually looked at our galaxy amongst six galaxies and found indications

that Green Pea galaxies are “old enough for Supernovae and stellar winds to reshape the

interstellar medium but young enough to possess large numbers of UV-luminous O or WR

stars” to open channels to allow LyC leakage.

7. Acknowledgement

I would like to express my sincere gratitude to the Department of Astronomy at the Stockholm

University who allowed me to do this Bachelor of Science thesis at their premises. In addition

29

I would like to thank the entire staff at the Galaxy Group for welcoming me into their group

and treated me like one of them. I will certainly miss all the homemade cookies and cakes.

Finally I would like to give my best personal thanks to Jens Melinder, my supervisor for this

thesis. I’m deeply impressed with your competence in astronomy, always being available for

me and your friendly personality. Thanks Jens.

30

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