A Search for Infrared Dark Clouds at 70 Μm

A Search for Infrared Dark Clouds at 70 µm

A thesis submitted to The University of Manchester for the degree of Master of Science By Research in the Faculty of Science and Engineering

2016

Hazel Blake School of Physics and Astronomy Contents

List of Figures 3

Abstract 8

Declaration 9

Acknowledgements 11

1 Introduction 12 1.1 Star Formation ...... 13 1.2 Low Mass Stars ...... 14 1.3 High Mass Stars ...... 16 1.3.1 Formation Models ...... 17 1.4 This Study ...... 20

2 Infrared Dark Clouds 22 2.1 Evolution ...... 23 2.2 Star Formation in Infrared Dark Clouds ...... 24

3 Previous Work 28 3.1 MSX Dark Clouds ...... 28 3.2 Spitzer GLIMPSE Dark Clouds ...... 30

1 CONTENTS 2

3.2.1 Follow Up Study ...... 33 3.3 Column Densities and Dust Temperature Maps of IRDCs . . . 33 3.4 Herschel Counterparts of the Spitzer Dark Cloud Catalogue . 35

4 A Search for IRDCs at 70 µm 38 4.1 Method ...... 38 4.2 Results ...... 46

5 Discussion 56 5.1 Further Work ...... 58

6 Conclusions 61

Bibliography 62

Word Count = 11,777 List of Figures

1.1 A brief overview of star formation. Gas and dust are inﬂuenced by a

nearby event, sending shock waves through the clouds. This causes

small pockets of dense gas and dust to form, eventually collapsing

in on itself, forming a star. (http://science.howstuﬀworks.com/how-

are-stars-formed.htm) ...... 15

1.2 A diagram of the four main stages of star formation as outlined by

Shu et al. (1987). (a) Molecular clouds form cores. (b) In the centre

of a core, a protostar will form with a circumstellar disk. (c) Bipolar

ﬂows are created by stellar winds along the rotational axis. (d) A

star is formed with a circumstellar disk...... 19

2.1 Three random clouds from a catalogue by Peretto and Fuller (2009),

with images from GLIMPSE Spitzer 8 µm. These indicate how varied

IRDC shapes and sizes can be...... 22

2.2 Cloud G0.253+0.016, located within 100 pc of the Galactic centre.

This cloud has a high average density, and therefore should be form-

ing numerous stars. As of yet, there are no visible stars seen within

the structure. The Submillimetre Array (SMA) reveals a few star-

forming cores, suggesting it might be a young cloud. The Combined

Array for Research in Millimetre-wave Astronomy (CARMA) shows

the cloud is full of silicon monoxide (SiO), suggesting the cloud is

a collision between two clouds and being compressed (Kauﬀmann

et al., 2013)...... 25

3 LIST OF FIGURES 4

3.1 A schematic view of a typical IRDC ﬂux density proﬁle. Ifore has

been set to a speciﬁc value in this ﬁgure, but in reality, Ifore can be

anywhere between Izl and Imin.IMIR is the observed mid-infrared radiation ﬁeld. (Peretto and Fuller, 2009) ...... 31

3.2 Substructures of the three IRDCs shown in ﬁgure 2 as 8 µm opacity

maps. Contours range from 0.4 to 0.8 in steps of 0.2 for the ﬁrst and

last image, whilst the contours for the middle ﬁgure are from 0.4 to

1.9 in steps of 0.3. Peretto and Fuller (2009) ...... 32

3.3 An example of a temperature map. The contours show the H2 column densities. As can be seen, the temperature minima are asso-

ciated with the column density peaks, but not one-to-one. (Peretto

et al., 2010) ...... 34

4.1 The original HIGAL image at 70 µm . Within this image are stars,

gas, dust and IRDCs. To detect the IRDCs, the background and

stars need to be removed from the image. The z-axis represents tem-

perature, where 0 deﬁnes the coolest objects, and 21000 represents

the warmest objects...... 39

4.2 The stars are removed from the original HIGAL image, comparing

the original image with image after the stars are removed. After

calculating the peak values in the HIGAL image, these are removed,

leaving the background image. SOme of the background emission is

left behind, but will be smoothed with a large median ﬁlter to remove

the small scale structures, leaving a large scale background image

from which to detect the clouds. The z-axis represents temperature,

where 0 deﬁnes the coolest objects, and 21000 represents the warmest

objects...... 40 LIST OF FIGURES 5

4.3 After the stars have been removed, the image is smoothed with a

large median ﬁlter to create an average background from which to

extract the clouds. The z-axis represents temperature, where 0 de-

ﬁnes the coolest objects, and 21000 represents the warmest objects. . 42

4.4 The optical depth image which then has the detection process applied

to it. This is the background after the stars have been removed and

a large median ﬁlter applied.The z-axis represents optical depth . . . 43

4.5 A compilation of the sources found using DAOFIND. Each segment is

each detected source above the threshold. Some sources may be over-

lapping but represented as a single source.The z-axis represents an

arbitrary counting system that numbers each source with a chrono-

logical order...... 44

4.6 Overlaying the peak longitude and latitude of the 8 µm clouds (cyan

circles) on top of the 70 µm image gives a quick visual comparison

between the two. At quick glance, many of the structures seen in

8 µm are not necessarily seen at 70 µm. However, there is some

correlation between the two when viewed by eye. Many clusters of

peaks in 8 µm are around the same area as clusters of 70 µm clouds. 45

4.7 A comparison of the HIGAL image with column densities and dust

temperatures as described by Peretto et al. (2016). Comparing these

images to the segmentation image can conﬁrm if the sources detected

correspond to true peaks in column density, and so are ’true’ clouds. 46

4.8 An example of sources from the segmentation process as compared to

the other images.The ﬁrst image shows a structure seen by the detec-

tion process, compared with the original image (top right), the dust

temperature (bottom left) and the column density (bottom right).

The structure is barely visible in the HIGAL image. The image is not

traced in the bottom images, although there is a diﬀerent structure

seen...... 47 LIST OF FIGURES 6

4.9 In this image, some small points are seen by the detection process,

but not necessarily seen in the other maps. Structures can be seen

in the column density and temperature maps, but not in the original

image. Some points from the segmentation image may correlate to

the structures, but it is diﬃcult to conﬁrm outright...... 48

4.10 The structure described by the detection process can clearly be seen

in all three of the other images. This is more than likely a real cloud. 49

4.11 The detection process found a lot of points above the threshold,

classing them as clouds. These points are not seen in the original

HIGAL image, with limited points seen in the column density map or

the dust temperature map. This suggests these points are artefacts

of the manipulation process...... 50

4.12 As can be seen in the original HIGAL image, this area has a large

amount of noise in the region. This will have skewed the manipu-

lation process, as not all the noise will have been removed prior to

the median ﬁlter. This may be the reason for many points being de-

tected. There is some structure here, as seen in the dust temperature

and column density maps, but nothing has been identiﬁed clearly by

the detection process...... 51

4.13 This ring that has been detected by the segmentation process is

clearly identiﬁable in all three other images. This is therefore a real

cloud. Most of the cloud has been detected by the segmentation

process, meaning a large proportion of this cloud is visible in 70 µm. 52

4.14 An example of structures seen in most of the images. There are

some ﬁlamentary structures seen in the column density and dust

temperature maps, but hidden by clouds and noise in the HIGAL

image. Some of these structures are seen by the detection process. . 53

4.15 A very obvious square structure seen by HIGAL and seen in the

column density and dust temperature maps, but not picked up in in

the segmentation process...... 54 List of Abbreviations

IRDCs - Infrared Dark Clouds

IMF - Initial Mass Function

SPH - Smoother Particle Hydrodynamics

IR - Infrared

EGO - Extended Green Object

MSX - Midcourse Space Experiment

PAHs - Polyaromatic Hydrocarbons

PBCD - Post-basic Callibrated Data

FWHM - Full Width at Half Maximum

SDCs - Spitzer Dark Clouds

7 Abstract

Abstract of Dissertation submitted by Hazel Blake for the degree of Masters of Science by Research to The University of Manchester, September 2016 A Search for Infrared Dark Clouds at 70 µm

Peretto and Fuller (2009) produced a catalogue of infrared dark clouds at 8 µm seen in the Galactic longitude and latitude 10◦ < |l| < 65◦ and |b| < 1◦ By constructing a catalogue of infrared dark clouds at 70 µm, this study detected over 1,000 individual points. Visual inspection of these points, compared with the temperature map and column density map described in Peretto et al. (2016), produced at least 10 areas with conﬁrmed cloud candidates, and at least three regions of deﬁnitive overlap between the two studies. This study discusses the methods used to detect clouds at 70 µm using HIGAL data, and a comparison between the two catalogues.

8 Declaration

No portion of the work referred to in the thesis has been submitted in support of an application for another degree or qualiﬁcation of this or any other university or other institute of learning.

9 Copyright Statement

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10 Acknowledgements

I would like to thank Gary Fuller for his support and help during this research. I would also like to thank friends and family for always being there with emotional support and belief.

The Author

I previously gained my BSc(Hons) in Geology with Planetary Science at The

University of Manchester. I have not previously conducted any research of this type before this work. I have had to teach myself how to use the Python coding system and the techniques used here whilst doing the research.

11 Chapter 1

Introduction

Stars are the building blocks of space and galaxies, making the study of them important in understanding timelines and the evolution of the Universe. Stars are categorized into high-mass or low-mass, each with a diﬀerent evolutionary path.

Low-mass stars have a mass less than 8 M and are more abundant, meaning their progression has been well documented (for example, Blocker (1995a) and Blocker

(1995b)). High-mass stars have a mass of more than 8 M , are more luminous, but have a quicker evolution, so a shorter lifetime, making it more diﬃcult to observe and study them. Understanding high-mass star formation is important as they could be related to many phenomena we see (Bally et al., 2005).

High-mass and low-mass stars are thought to form in diﬀerent environments; low-mass stars are known to form in clouds and Bok Globules (Bok and Reilly,

1947), whereas high-mass stars are thought to originate in infrared dark clouds

(IRDCs) (Rathborne et al., 2006). Whilst clouds and Bok Globules, along with the star formation within them, have been well documented, IRDCs and their association with star formation are still relatively unknown. It is widely accepted that high-mass (pre-) protostars are formed within IRDCs (Menten et al., 2005).

12 CHAPTER 1. INTRODUCTION 13

1.1 Star Formation

Generally, star formation involves large clouds of gas and dust collapsing together to form stars. Some nearby event may be needed to start the collapse, such as a passing gravitational disturbance. Once the critical density is reached, (the Jeans instabilty

(McKee and Ostriker, 2007)) a protostar may form. This is needed for stars of all masses to form, but the environments and evolutions are diﬀerent depending on the mass.

The Jeans instability is the threshold for which a cloud will become unstable and collapse, possibly forming stars. For a cloud to remain stable, it must be in hydrostatic equilibrium, given as

dp Gρ(r)M (r) = − enc (1.1) dr r2

where Menc is the enclosed mass, p is the pressure, ρ(r) is the density of gas at radius r and G is the gravitational constant (Jeans, 1902). Pressure and the force of gravity must be in balance with each other in order for the cloud to stable. If either were to increase or decrease more than the other, the cloud would either expand or collapse. In the case of collapse, a star is formed. The star is also bound by hydrostatic equilibrium.

By observing the distribution of the mass of stars, an empirical function has been developed, describing the mass distribution of stars, the Initial Mass Func- tion (IMF). The IMF has been based on the observations of clusters in our own

Galaxy, so some extrapolation may be required for clusters outside our Galaxy.

There are suggestions that the IMF does not vary much between clusters, however observations do suggest that diﬀerent environments do aﬀect it (Krumholz, 2014).

There are many ways in which to “measure” the IMF, including using mature clusters with stars and some that only have younger, protostellar objects do not.

The most obvious way is to observe stars visible in the solar neighbourhood, as started by Salpeter (1955). Another method involves studying stars in a young stellar cluster.There are many advantages to this method compared to the field CHAPTER 1. INTRODUCTION 14 survey, along with some disadvantages too. Younger stars are the same age as each other, or very nearly so, and have uniform chemical compositions. They are also nearly at the same distance due to their young age and not being expelled from the cluster where they are formed, which can be measured in a variety of ways. All this removes errors in conversions found in the field survey. Disadvantages include stars not being on the main sequence yet, introducing significant errors mapping the luminosities and masses. There can also be areas of high stellar density, creating confusion determining sources. As the clusters are still young, many of them are partially shrouded by dust, limiting accurate readings of luminosities. A similar method observes stars within a globular cluster, which has similar advantages and disadvantages. Globular clusters do give rise for more parameters to be compared, such as low metallicity and ancient stars, giving a more in depth view of the IMF

(Krumholz, 2014).

There are other methods of “measuring” the IMF, each with their own advantages and disadvantages. As the IMF is purely empirical in nature, there will be many ways to determine it, and many diﬀerent conclusions drawn from it.

1.2 Low Mass Stars

Stars tend to form in clusters within large molecular clouds. These are “clumpy”, containing more than one core within them (Reipurth, 1983). To stimulate star formation, a nearby phenomenon needs to occur to disrupt the cloud. This can include other stars forming nearby, ionizing the surrounding interstellar medium, consequently sending a shock wave through the cloud (triggered star formation,

figure 1.1). This shock scatters loose gas and dust into the interstellar medium, but as the cores are denser, the effects are more subdued, causing the core to be separated from the cloud, creating a Bok Globule (Bok and Reilly, 1947). These globules are defined by being isolated and compact dust clouds with a large degree of regularity. They were classified as stars that were forming in a denser region of the interstellar medium, possibly indicating an intermediate stage of stellar evolution. CHAPTER 1. INTRODUCTION 15

Figure 1.1: A brief overview of star formation. Gas and dust are inﬂuenced by a nearby event, sending shock waves through the clouds. This causes small pockets of dense gas and dust to form, eventually collapsing in on itself, forming a star. (http://science.howstuﬀworks.com/how-are-stars-formed.htm)

During triggered star formation, there are many points in which a star may be formed:

(1) before the ionization and shock occurs, stars may already have formed in the cloud;

(2) formation may be triggered when the shock wave hits the edge of the cloud;

(3) once the shock has progressed through the cloud, it reaches the edge of the core, triggering star formation in the outer layer of the globule;

(4) the shock continues through the core, causing central star-forming collapse.

A low-mass star will begin to form when a dense isothermal core collapses. The system enters a free-fall collapse once turbulence decreases and gravity dominates, causing the core to be supported by magnetic ﬁelds (Shu et al., 1987). A disc of gas and dust surrounds the core, accreting onto the core. The core begins to rotate, and the disc is supported by the rotation. The stars continue to accrete mass from the disc (similar to Bondi-Hoyle accretion, Butler and Tan (2009)). Larson (2003) suggest that the accretion process is not uniform, possibly slowing down later.

The evolution of a low-mass star is slow. It takes many millions of years to CHAPTER 1. INTRODUCTION 16 develop from a protostar to a star on the main sequence, and then many more millions of years to progress through the main sequence. They start as clouds, evolving into a protostar, before becoming a main sequence star similar to our Sun.

They will stay on the main sequence for approximately 10 billion years (for a 1 M star), before running out of hydrogen to burn. The next stage is helium fusion as a horizontal-branch star, continuing to burn through elements until it ends as a white dwarf. Due to this extended timeline, many clusters that form low-mass stars will have a variety of older and younger stars, making comparisons between stages easy to document. These clusters also tend to form around one star that has a larger mass (see section 2.1), but these higher mass stars have shorter lifetimes.

1.3 High Mass Stars

As previously mentioned, high-mass star formation is a relatively hidden process, especially the protostar formation. The earliest phase that has been imaged is associated with hot cores, a small area of molecular gas, tending to be warm (≥ 100

K) and dense (> 106 cm−3, Van der Tak (2003)). These further evolve, producing molecular outﬂows and maser emissions which indicates accretion disks and/or outﬂows, but this only happens after the protostar has formed, meaning that the very earliest stage of star formation is still hidden. There are many theories as to the formation of high-mass stars, some of which shall be discussed below.

Star forming clumps have been identiﬁed through their very active young stars

(embedded in ultra-compact HII regions with hot dense gas, emitting radio and maser emissions, Butler and Tan (2009)) and radiative emissions. They are also

+ 13 + 13 + observed through molecular lines such as N2H , HNC, HN C, HCO ,H CO ,

HCN, C2H, HC3N, HNCO and SiO (Sanhueza et al., 2012). As the protostars continue to develop, the spectrum observed becomes richer with molecular lines through more complex molecules being formed. Early HII regions have not ionised the surrounding areas, so will have more lines in their spectrums than late-stage

HII regions, meaning more molecules should be seen, especially near the central, CHAPTER 1. INTRODUCTION 17 heating star(s). The strength of each molecular line emission can be measured to compare to each other, determining the age of the cluster, but only if the molecular lines show varying abundances over time (Kauﬀmann and Pillai, 2010).

High-mass stars evolve quickly, die quickly, and are very rare. Rough outlines of formation and evolution have been developed by piecing the information discovered from observing stars at various stages in their lifetimes, and then extrapolating low- mass star formation processes to predict the high-mass star formation processes that agree with observations.

1.3.1 Formation Models

It has been suggested that high-mass stars form in an analogous way to low-mass stars (Rathborne et al., 2006). High-mass stars would follow a similar evolutionary path , meaning we would expect to the high-mass equivalents of Bok globules, but with much higher pressures, similar to those found in star-forming clumps. The environment is of little signiﬁcance in this scenario. If the gas is in a self-gravitating high-mass clump, then high-mass stars will form in the area (Bonnell et al., 2004).

Competitive accretion is a model proposed by Bonnell et al. (1998) that suggests the mechanism of falling gas and dust and the formation of stars in clusters. The protostellar core collapses, causing the gas to fall into the centre of the cloud.

The stars that then form from this central clump accrete more of the surrounding gas, making them larger than stars that form towards the edge of the clump. The stellar environment surrounding the stars may aﬀect the overall mass, but the inital mass of the star does not. These simulations do not include the magnetic ﬁelds, however, which may reduce the accretion rates. This has lead to Krumholz et al.

(2005) suggesting that this method of competitive accretion would not work in the turbulent medium that is associated with molecular clouds.

Using smoothed particle hydrodynamics (SPH, Monaghan (1992)) numerical simulations, Bonnell et al. (2004) simulated a large turbulent molecular cloud that has isothermal gas that fragmented. Unfortunately, the feedback of star produc- tion is not modelled which can limit the simulation as these feedbacks can be CHAPTER 1. INTRODUCTION 18 dynamically important in star formation. It has been suggested that the winds and supernovae input energy and momentum into the interstellar medium. These feedbacks can have a profound effect on galaxies, and are thought to be important in determining structure of galaxies, along with introducing heavy elements into the system. These feedbacks generally only influence the surrounding disk, but some events can influence the Galactic halo too Kennicutt (2005). Protostellar fragments cannot be directly simulated, so sink particles were used as replacements, enhanc- ing the simulation further. Any stellar collisions that may occur during the process have not been simulated. Results from the simulation agreed with observations of clouds, with a large mass range of stars being produced, but the low-mass stars being prevalent in the cluster. The simulation showed that the massive clumps often resolved into multiple lower-mass stars which may suggest fragmentation or another star accreting a large proportion of the clump. Limits were placed on the simulation, allowing for mass to be included via accretion after the protostar had formed, but does not include the mass acquired through competitive accretion. The stars were monitored through their evolution and it was discovered that some of the stars accreted more mass than others that had a similar concentration of gas surrounding them. The conclusion was that because a single star could accrete more mass, then competitive accretion exists in star-forming regions. The results also suggested that massive stars could accrete mass more quickly than lower-mass stars, and these higher-mass stars generally formed earlier in the cluster. This meant less competition for the gas and dust at an earlier stage and so were larger than the average star once more stars had formed in the cluster. The simulation in general allowed Bonnell et al. (2004) to suggest that most massive stars gained their mass through competitive accretion, rather than the scaled up version of low-mass star formation as suggested by Rathborne et al. (2006).

Butler and Tan (2012) suggest that high-mass star formation is a similar process to low-mass star formation, but where matter falls onto a central circumstellar disc

(as also discussed by Shu et al. (1987), see ﬁgure 1.2). The clump pressure in which the stars form is likely to be self-gravitating from the weight of the gas, and CHAPTER 1. INTRODUCTION 19

Figure 1.2: A diagram of the four main stages of star formation as outlined by Shu et al. (1987). (a) Molecular clouds form cores. (b) In the centre of a core, a protostar will form with a circumstellar disk. (c) Bipolar flows are created by stellar winds along the rotational axis. (d) A star is formed with a circumstellar disk. CHAPTER 1. INTRODUCTION 20 the stars are thought to be in pressure equilibrium with these clumps. Recorded temperatures between 10 K and 20 K by Butler and Tan (2012) within the cores suggests that these cores must be supported by non-thermal pressures, such as magnetic fields or turbulence, systems not included in the simulations by Bonnell et al. (2004). Magnetic fields are also considered to be the main reason behind massive cores not fragmenting into low-mass star clusters and forming the high- mass stars instead. However, others have also suggested that the fragmentation is impeded by the radiative heating from the newly formed low-mass protostars surrounding the core (Krumholz and McKee, 2008).

1.4 This Study

The aim of my research is to expand on a previous catalogue by Peretto and Fuller

(2009) of infrared dark clouds. It has been suggested that these infrared dark clouds may be the precursors to massive stars, and that observing them may provide insight into the formation and evolution of these hidden stars. Peretto and Fuller

(2009) constructed a catalogue of infrared dark clouds observed at 8 µm between

Galactic longitude and latitude 10◦ < |l| < 65◦ and |b| < 1◦ (see section 3.2). In total, they published nearly 300 clouds in this region.

My research aims to build upon this catalogue by observing this area at 70 µm.

By considering these sources at a longer wavelength, new structures and features may be found within known clouds, and new clouds may be seen that have not previously been documented at other wavelengths. The dust at this wavelength will have diﬀerent properties (see section 4.1), and so reveal new data with which to determine the formation and progression of massive stars. Comparing the two catalogues will allow a comprehensive overview of those clouds already known and determine new features, such as other cores and clumps, and provide a more in depth view into the inside of the clouds themselves. I shall follow a similar process as documented within Peretto and Fuller (2009), however, diﬀering slightly with the codes used, and star removal, as outlined in section 4.1. CHAPTER 1. INTRODUCTION 21

The continuation of this report will be as follows: section 2 will provide an overview of infrared dark clouds, including details of their evolution, and current knowledge of star formation within them; section 3 will explore previous studies of a similar nature, including that of Simon et al. (2006a), Peretto and Fuller

(2009) and Peretto et al. (2010), detailing their works and methods and providing summaries of their results; section 4 will follow my own work, providing an overview of my method and results, following with a discussion in section 5, with section 6 concluding. Chapter 2

Infrared Dark Clouds

Infrared dark clouds (IRDCs) are clouds of gas and dust that absorb the Galactic diﬀuse IR background emission and tend to be opaque between 7 and 100 µm

(Carey et al., 1998; Teyssier et al., 2002; Simon et al., 2006a). Clouds tend to differ in morphology from filamentary to compact (see figure 2.1), with masses ranging from 120 to 16,000 M (Rathborne et al., 2006). All clouds will exhibit one or more cores, which are believed to be the initial conditions for protostars, developing into

OB stars. These hot, massive, short-lived stars emit large amounts of UV radiation.

Many of the cores can be compared to molecular cores, suggesting that IRDCs are cold precursors to clusters (Rathborne et al., 2006; Simon et al., 2006b; Egan et al.,

1998).

Figure 2.1: Three random clouds from a catalogue by Peretto and Fuller (2009), with images from GLIMPSE Spitzer 8 µm. These indicate how varied IRDC shapes and sizes can be.

22 CHAPTER 2. INFRARED DARK CLOUDS 23

A large survey of a variety of clouds needs to be taken in order to determine their characteristics. Unfortunately, they are very difficult to image at present. It is probable that some clouds documented to be a single cloud are actually multiple clouds seen along the same line of sight at different distances. Currently, we are unable to distinguish between these clouds. Distances are difficult to determine, which also makes resolving the masses, sizes and distributions difficult. Distances are currently being measured kinematically by molecular line emissions (Simon et al., 2006b).

Many of the clouds have been observed in the ﬁrst and fourth galactic quadrants at low galactic latitudes, where the infrared background is greatest (Simon et al.,

2006b). The distribution of IRDCs has been shown to follow the Galactic diﬀuse mid-infrared background, with peaks showing towards star formation regions and spiral arm tangents (Simon et al., 2006a). It has been suggested that these may be left over from fragmented molecular clouds. There are also many IRDCs found in the so-called 5-kpc ring around the galactic centre. The ring is associated with a high star formation rate, so it is reasonable to theorise that IRDCs could be related to stellar formation in some respect.

IRDCs were discovered by Perault et al. (1996) using ISOCAM data at 15 µm looking at the Galactic plane. Whilst observing the IR background, they noticed some absorption features; the ﬁrst IRDCs. Since then, the galaxy has been imaged in various IR wavelengths and the dark patches in the emission observed, indicating the presence of a possible IRDC. They are identiﬁed as regions with a high contrast to the background IR emission. IRDCs are very cold, so their thermal dust emission peaks at millimetre and submillimetre wavelengths.

2.1 Evolution

IRDCs are imaged against the Galactic mid-IR background as extinction features

(Rathborne et al., 2006), which biases their identiﬁcation to the closest clouds to us, and to those clouds in front of the background emission from the centre of the CHAPTER 2. INFRARED DARK CLOUDS 24 galaxy. This bias means many clouds are not identiﬁed as they lie on the other side of the background emission as sizes and masses are not distinguishable, meaning there are more clouds in our Galaxy that have not been categorised. Most of these clouds will lie on the other side of the Galaxy behind the collective IR emission.

IRDCs have a large size distribution (ﬁgure 2.1). Some clouds are large and

ﬁlamentary whereas others are smaller and compact. Generally, IRDCs are smaller than molecular clouds, but larger than Bok globules (Simon et al., 2006b). They often resemble cluster-forming molecular clumps, reinforcing the theory that IRDCs are fragments of molecular clouds (Egan et al., 1998). However, IRDCs are colder than molecular clouds, so it may be that they are an earlier evolutionary stage of star and cluster formation (Rathborne et al., 2006). Once stars have formed, feedback will heat the surrounding area, causing the cloud to reach temperatures similar to those of molecular clouds that have had active star formation in the past.

This scenario would suggest that most star-forming clouds will have similar initial temperatures to those observed in cold IRDCs before star-formation heats them up.

Rathborne et al. (2006) suggest that young IRDCs are isolated clouds with no association to any emission. Cores of gas and dust form through gravitational attraction and stellar winds, creating protostars that cause sections of the IRDCs to become bright whislt the rest of the cloud remains dark. Protostars continue to accumulate gas and dust, ionize the surrounding area and create compact HII regions. More protostars continue to form, so more of the IRDC becomes bright, becoming an Infrared Bright Cloud associated with star forming regions.

2.2 Star Formation in Infrared Dark Clouds

IRDCs are potential sites for massive star formation, so detailed studies of particular clouds with evidence of star formation are being conducted. One such cloud is

G0.253+0.016, located within 100 pc of the Galactic centre (ﬁgure 2.2). Currently,

5 there are no visible stars, but with a mass of 1.3 × 10 M , an average radius of CHAPTER 2. INFRARED DARK CLOUDS 25

Figure 2.2: Cloud G0.253+0.016, located within 100 pc of the Galactic centre. This cloud has a high average density, and therefore should be forming numerous stars. As of yet, there are no visible stars seen within the structure. The Submillimetre Array (SMA) reveals a few star-forming cores, suggesting it might be a young cloud. The Combined Array for Research in Millimetre- wave Astronomy (CARMA) shows the cloud is full of silicon monoxide (SiO), suggesting the cloud is a collision between two clouds and being compressed (Kauﬀmann et al., 2013). CHAPTER 2. INFRARED DARK CLOUDS 26

2.8 pc and a dust temperature of 23 K (Rodriguez and Zapata, 2013), then it is suggested that it may be a precursor to a massive stellar cluster.

By observing data from JVLA 1.3- and 5.6-cm radio continuum observations,

Rodriguez and Zapata (2013) identify three compact radio sources projected onto the cloud. It is possible that these sources may be line-of-sight coincidences, however, at this stage, it is impossible to determine if so. From this study, six individual points of interest were highlighted, named JVLA 1 through 6. JVLA 1 and 2 are on the edge of the cloud, but still contained within its boundaries. They have a non-thermal spectral index, making them possibly background sources, however

Rodriguez and Zapata (2013) expect this is statistically improbable. The sources may be associated with the Galactic centre as they are bright in 5.6 cm. Other observations of these sources (Immer et al., 2012) suggest that they may be HII regions, although Rodriguez and Zapata (2013) rule out this option due to the non-thermal index they have derived.

The brightest source in the cloud is JVLA 3. This source is also very irregular, with a shell-like morphology and an angular diameter of 10”. By estimating the luminosity, it has been suggested that it could evolve into a O6 zero age main sequence star (ZAMS), however there is a discrepancy of deriving stellar types from photoionization or the IR luminosity. Rodriguez and Zapata (2013) and Immer et al. (2012) agree with each other suggesting that this source is most likely an HII region within the vicinity of the cloud.

JVLA 4, 5 and 6 are all projected against the cloud, with thermal spectral shapes being either ﬂat or positive, suggesting that they are free-free emitters and they are possibly located with HII regions, and either optically thin or partially optically thick. There may be thermal jets at JVLA 4, as suggested by an elongation in the east-west direction. JVLA 5 shows an extended morphology, but more study is needed to understand this.

Sanhueza et al. (2012) characterized clumps by different features observed by molecular-line surveys. They identified four main categories of clump according to this method: quiescent, intermediate, active and red. A fifth category, blue, is rare CHAPTER 2. INFRARED DARK CLOUDS 27 and not necessarily common to all clumps.

The ﬁrst evolutionary stage of clump is quiescent and exhibits no IR emission.

It is suggested that this may be the starless phase of the clouds, and therefore the most probable place for massive star formation to begin. The next most active evolutionary stage of clump is the intermediate, characterised by 4.5-µm emission, a so-called ’green fuzzy’ (Extended Green Object, EGO), or 24-µm emission, but not both types. The next phase, an active clump, is characterised by both a green fuzzy and an embedded 24-µm emission, with a red clump, the most active, being associated with a bright 8-µm emissions, most likely an HII region. Blue regions are associated with 3.6-µm emission.

Sanhueza et al. (2012) also investigated how the diﬀerent molecular lines evolved through these progressive evolutionary stages of clumps, probing the chemical changes. They found that active clumps, such as G034.43 MM1, exhibited a richer molecular line emission than a quiescent cloud, such as G028.53 MM3. As protostars evolve, they continually ionize the area surrounding them, releasing far more complex materials into the clumps they inhabit, providing them with a richer molecular line, as seen within this study.

By comparing IRDCs to clouds with known massive star formation, Kauﬀmann and Pillai (2010) hoped to discover if IRDCs could form massive clouds. They found that, on average, IRDC masses lay between the masses of clouds with and without massive star formation. They suggest that, as these IRDCs are typical of the Galactic population, it is unlikely that IRDCs could form these massive stars, but does not rule out low- and intermediate-mass star-cluster formation. However, it is generally accepted that IRDCs can and do form the massive stars we observe. Chapter 3

Previous Work

3.1 MSX Dark Clouds

The ﬁrst large survey of IRDCs was conducted by Simon et al. (2006a). They used

8.3 µm mid-infrared images from the Midcourse Space Experiment (MSX) satellite to identify IRDCs in the ﬁrst and fourth quadrants of the Galactic plane. 10,931 clouds were identiﬁed by searching for regions of high contrast with respect to a determined background. For each of these clouds, cores are also determined by having at least 40% higher extinction than the average extinction of the cloud.

Using this method, 12,774 cores were catalogued.

Simon et al. (2006a) used data from the MSX SPIRIT III telescope, launched in

1996. This telescope surveys the plane at four mid-IR wavelengths centred around

8.3, 12.1, 14.7 and 21.3 µm, bands A, C, D and E respectively. The majority of the

IRDCs catalogued in this survey were detected in band A; it has the best angular resolution and sensitivity, and contains emissions from polyaromatic hydrocarbons

(PAHs), creating the brightest mid-IR background.

In this study, IRDCs were defined as “extended regions that show a significant decrement in the mid-IR background.” To identify these regions, the algorithm created had three steps: (1) modelling the background, (2) creating an image of contrast against the background and (3) identifying the IRDCs. To model the background, they used spatial median filtering technique. To create the contrast

28 CHAPTER 3. PREVIOUS WORK 29 image, they ﬁrst convolved the original MSX images with a 20” Gaussian beam.

This smoothed image is then subtracted from the diﬀuse background image, and then divided by the background image. To identify the IRDC candidates, a two step process was involved. Firstly, the values for the contrast must be large enough to be real and not an artefact of the of the instrument noise. Secondly, the IRDCs need to be extended to remove the possibility of identifying small features that may be noise from the noisy MSX images. This lead to over 10,000 possible IRDCs being identiﬁed in this region.

To identify the cores in this sample, a two-dimensional elliptical Gaussian ﬁt was used on the contrast images, ﬁtting at least one core to each cloud starting at the peak. The process is repeated if the extinction is still higher than 40%, continuing until this criterion is not met. Every cloud has at least one core, and some have more than one, leading to the cataloguing of over 12,000 cores.

Of the clouds detected, the average size is between 1’ and 5’, with no clouds being detected that are > 7.5’. The cores are typically between 0’.75 and 2’. The minimum detected size is due to the 28” angular resolution of the MSX images after being smoothed. To conﬁrm that these clouds are “real”, a comparison between this catalogue and other catalogues was conducted. Of the combined MSX lists containing 14 entries (Egan et al., 1998; Carey et al., 1998, 2000), every cloud was detected apart from the cloud at (l, b) = (79◦.34, 0◦.33), possibly due to this cloud being very close to a very bright mid-IR emission. Comparing to the ISO data, all six of the clouds detected by Hennebelle et al. (2001) and seven of 13 IRDCs detected by Teyssier et al. (2002). It is possible the remaining six clouds were not detected due to low sensitivity, resolution, a combination of the two or strong gradients in the mid-IR emission in more complex regions. A follow up survey of a sub-section of sources performed by Simon et al. (2006b) was able to determine some distances for these objects and found that these samples were similar to CO cores (e.g. Blitz (1993)). CHAPTER 3. PREVIOUS WORK 30

3.2 Spitzer GLIMPSE Dark Clouds

Peretto and Fuller (2009) have previously constructed a catalogue of IRDCs as seen at 8 µm. They used Spitzer GLIMPSE and MIPSGAL data (3 - 9 µm and 24 & 70

µm respectively), surveying the area of Galactic longitude and latitude 10◦ < |l| <

65◦ and |b| < 1◦. Using this data, opacity maps were produced and a an algorithm

22 −2 to identify any structures above a column density of NH2 & 1 × 10 cm . The GLIMPSE and MIPSGAL data used was reduced and automatically calibrated, producing so-called post-basic calibrated data (PBCD), and because the typical size of structures seen in this study is approximately one arcminute, a calibration factor of 0.8 was added to the PBCD 8 µm images in the analysis.

IRDCs are often seen in silhouette, with many being seen up to at least 24 µm, allowing for a wide wavelength range in which to study them in absorption. The strength of this absorption related directly to the opacity along the line of sight.

The relationship, as described in Bacmann et al. (2000), between opacity τλ and the intensity at wavelength λ emerging from the cloud Iλ is given by

I8µm = Ibg × exp(−τ8µm) + Ifore (3.1)

where Ibg is the intensity of the background emission at 8 µm and Ifore is the foreground emission. When the foreground and background are known, equation

3.1 can be inverted to ! I8µm − Ifore τ8µm = − ln (3.2) Ibg to infer the spatial distribution of the opacity within an IRDC. Ifore and Ibg are related by IMIR = Ibg + Ifore (ﬁgure 3.1), where IMIR is the observed mid-IR radiation ﬁeld, which can be estimated from the 8 µm images. To infer the spatial opacity distribution of an IRDC, Ifore needs to be determined. With an increasing

Ifore, opacity increases signiﬁcantly everywhere, especially at the peak, with the diﬀerence being even more pronounced in shallow clouds.

By comparing the infrared extinction with millimeter emissions, the foreground CHAPTER 3. PREVIOUS WORK 31

Figure 3.1: A schematic view of a typical IRDC flux density profile. Ifore has been set to a specific value in this figure, but in reality, Ifore can be anywhere between Izl and Imin.IMIR is the observed mid-infrared radiation field. (Peretto and Fuller, 2009) emissions can be constrained. This requires both techniques to provide the same column density towards the sources. Therefore, this survey used 38 IRDC 1.2 mm dust emission provided by Rathborne et al. (2006) using the IRAM 30 m telescope at

11” angular resolution. After determining Ifore, it was noticed that the foreground emission was approximately equal to the background emission, also observed by

Johnstone et al. (2003). This suggests that the source of the foreground emission is the same as the background emission and local to the IRDC. This in turn means that the foreground is independent of the distance to the IRDC.

Due to the sensitivity of the Spitzer images, a large proportion of stars and galaxies are viewed at 8 µm which need to be removed in order to detect IRDCs successfully. Peretto and Fuller (2009) used the IDL FIND task from the Astronomy

Library to identify the central position of each of the stars. The average gradient plane of the pixels surrounding the stars to be removed was then calculated to replace the stars. This allows for a general recovery of the IRDCs behind such stars, but can produce some artefacts. After the stars are removed, the mid-infrared radiation ﬁeld, IMIR, was calculated using a normalised Gaussian of FWHM = 308”, CHAPTER 3. PREVIOUS WORK 32

Figure 3.2: Substructures of the three IRDCs shown in figure 2 as 8 µm opacity maps. Contours range from 0.4 to 0.8 in steps of 0.2 for the first and last image, whilst the contours for the middle figure are from 0.4 to 1.9 in steps of 0.3. Peretto and Fuller (2009) smoothing each 8 µm block individually. The Gaussian was used rather than median to better recover any potential clouds that may be situated next to a strong 8 µm emitting source; a median filter would remove these from the image. Initial viewing of these clouds suggests that most of them were filamentary, having a minor axis of no more than a few arcminutes. To convert these spatial 8 µm opacity distributions to H2 column densities, the properties of the absorbing dust is required. This depends on lines of sight, as diffuse material, chemical composition, or thus the properties will all be different. In larger clouds, it is believed that the dust grains themselves are larger than the diffuse material, possibly due to coagulation and a presence of icy mantles on the grains. To account for uncertainties, the column densities were calculated by

22 −2 NH2 = τ8µm × 3[±1] × 10 cm (3.3)

As demonstrated in ﬁgure 3.2, almost every IRDC exhibited substructures. As the peaks in column densities suggest likely hotspots for future star formation, identiﬁcation of these structures is needed to identify initial conditions of star formation. To identify these substructures, named fragments, the same code used to identify the IRDCs was used, pinpointing between 20,000 to 50,000 fragments depending on the steps. Each fragment had its size, position, peak, opacity and 24 CHAPTER 3. PREVIOUS WORK 33

µm star associations measured and discussed in a later paper.

3.2.1 Follow Up Study

Traﬁcante et al. (2015) produced a follow up survey to Peretto and Fuller (2009).

Further analysis of a subset of IRDCs detected in this previous study, selected for the region of overlap with the Galactic Ring Survey (Jackson et al., 2006), revealed 1,723 clumps, classiﬁed as protostellar or starless, depending on their 70

µm emissions. Of these 1,723 clumps, 61 per cent were defined as protostellar, and 39 per cent as starless. These clumps were found in 764 different IRDCs, of which 49 per cent are associated with protostellar clumps, 23 per cent with starless clumps, and 28 per cent with both types of clumps. Some of these IRDCs had already been previously identified by Peretto et al. (2010), suggesting that these

IRDCs are indeed dark clouds and not artefacts.

3.3 Column Densities and Dust Temperature

Maps of IRDCs

By using the first Herschel data from Hi-GAL, Peretto et al. (2010) aimed to construct accurate column densities and dust temperature maps of IRDCs. They recovered spatial variations by fitting a grey-body function at each position. This analysis showed that the dust temperatures of IRDCs decease significantly, from background temperatures of 20 - 50 K to minimum temperatures within the cloud of 8 - 15 K. Such local temperature minima are strongly correlated to column density peaks, although not on a one-to-one basis, suggesting these clouds as possible candidates of massive prestellar cores.

By checking clouds from Peretto and Fuller (2009) by eye, it was determined that 80 to 90% of the IRDCs were seen in emission with Herschel, conﬁrming that they are real clouds. Of these real clouds, a subsample of 22 IRDCs, ranging in size from 40” to 180”, large enough to contain at least one 500 µm beam, and a CHAPTER 3. PREVIOUS WORK 34

Figure 3.3: An example of a temperature map. The contours show the H2 column densities. As can be seen, the temperature minima are associated with the column density peaks, but not one-to-one. (Peretto et al., 2010) column density peak derived from extinction that is high enough to be clearly seen against the Hi-GAL background were selected for analysis. This selection process obviously prioritises larger and more massive clouds, leaving the small clouds under- represented.

After constructing the temperature map, it was confirmed that dust temperatures are not uniform throughout the cloud. The median mass-weighted temperature is approximately 15 K, but the range can be from 10 K in the cool central regions, up to 22 K, similar to the infrared background temperatures, as exhibited in figure 3.3. These temperatures are important in determining emissions, and also have an impact on the fragmentation of the cloud. It is probable that the clouds seen in this study were formed from a molecular gas, whose temperature was similar to that of the background, and cooled efficiently to 10 K.

Approximately two thirds of the IRDCs and one third of column density peaks CHAPTER 3. PREVIOUS WORK 35 currently form stars, which conﬁrms statistics from the Spitzer observations of a larger sample of IRDCs. The column densities and temperatures for these star forming cores are likely to be overestimated and underestimated respectively, due to the lack of data points accounted for at wavelengths lower than 100 µm. Other objects were identiﬁed for further study as the places for massive star birth prior to any star formation.

3.4 Herschel Counterparts of the Spitzer Dark

Cloud Catalogue

A further follow up to Peretto and Fuller (2009) was conducted by Peretto et al.

(2016), comparing the Spitzer dark clouds (SDCs) with Herschel data from Hi-

GAL. Due to the nature of absorption studies, ﬂuctuations within the background may be interpreted as clouds. By constructing H2 column density maps using 160 and 250 µm from Hi-GAL, it was determined that 76(±19)% of the SDCs are real, with the larger clouds fraction (clouds with an angular diameter larger than ∼ 1 arcminute) reducing to 55(±12)%.

Hi-GAL provides dust emission properties at ﬁve wavelengths (70, 160, 250,

350 and 50 µm). These properties were used to create H2 column density maps in which to determine real SDCs from artefacts. These column densities are measured towards the IRDCs and integrated along the line of sight. This means it is the sum of the IRDC and the background, which have different dust properties, and so different column densities. Occasionally, the background column density is larger than the cloud itself, which can be removed from the observed flux, but is a difficult process for large sample sizes. In this study, the larger structures are filtered out using a 10’ wide median filter on the maps, creating a background image, which is then subtracted from the original column density image map. Overall, the column density calculated is accurate within a factor of 2 for most of the IRDCs. The uncertainties include the component separation, but do not include uncertainties on the dust emissivity. CHAPTER 3. PREVIOUS WORK 36

Three criteria were deﬁned in which to identify the real IRDCs. First, c1, is the diﬀerence between the average Herschel column density within the cloud boundary

in out (NH2) and immediately outside the boundary (NH2 ). If the IRDC is real, then it is expected that: in out c1 = NH2 − NH2 > 0 (3.4)

The second parameter, c2, is:

in c2 = NH2/σin ≥ 3 (3.5)

where σin is the column density dispersion estimated on the scale of the cloud. The third parameter is, c3 is:

in out c3 = NH2 − NH2 /σin ≥ 3 (3.6)

The last parameter is more sensitive than c2, and it was determined that a significant number of real IRDCs would be missed by this parameter. Using criteria c1 and c2, 7,136 (63.2%) of the 11,289 available SDCs were detected with Herschel. Some of the smaller IRDCs may have been missed, and some IRDCs may have been misidentified as real due to the resolution difference between Herschel and Spitzer and with strong fluctuations in the background column density.

A visual inspection of the SDCs involved matching the morphologies of the largest clouds as seen in the 8 µm Spitzer images with their corresponding Herschel column densities. This inspection was used to determine how accurate the detection scheme used was. Overall, most of the clouds with an angular radius under 60” that were identiﬁed as real by the automated detection scheme are real, whereas larger clouds need a visual inspection to check their nature and determine if they are real. For the smallest clouds, Hershel cannot resolve the internal details, making it more diﬃcult to determine the fraction of real clouds. The fraction of real SDCs was estimated to be 76(±19)% for clouds with an angular resolution less than

32”, with the fraction decreasing to approximately 55(±12)% for the larger clouds, CHAPTER 3. PREVIOUS WORK 37 representing the increase of fraction of artefacts in the 8 µm background. Chapter 4

A Search for IRDCs at 70 µm

Figure 4.1 shows the HIGAL image of two degrees by two degrees, centred on longitude 15◦, in which this study will review and detect any clouds at 70 µm that may be present. Manipulating this, and applying equation 3.2, presents an overview of the structures present in the area above a certain, speciﬁed threshold.

Some of the resulting structures will be dark clouds, which can then be isolated and investigated further.

The aim of this work is to expand on the catalogue presented in Peretto and

Fuller (2009). The original catalogue used Spitzer and MIPSGAL data to view infrared dark clouds at 8 µm, and produced opacity maps and an algorithm to identify any structures within the observed clouds. This work will use some of the techniques described in Peretto and Fuller (2009) to observe clouds in a similar area at 70 µm instead of 8 µm to determine if any diﬀerences in structures or observations between the two wavelengths. Presented next is the method in which

I isolated the clouds in the sample area.

4.1 Method

To detect the clouds found in the image (ﬁgure 4.1), the background needs to be estimated and the stars removed before applying equation 3.2.

Throughout this process, median and Gaussian ﬁlters are applied to the images

38 CHAPTER 4. A SEARCH FOR IRDCS AT 70 µM 39

Figure 4.1: The original HIGAL image at 70 µm . Within this image are stars, gas, dust and IRDCs. To detect the IRDCs, the background and stars need to be removed from the image. The z-axis represents temperature, where 0 deﬁnes the coolest objects, and 21000 represents the warmest objects. CHAPTER 4. A SEARCH FOR IRDCS AT 70 µM 40

Figure 4.2: The stars are removed from the original HIGAL image, comparing the original image with image after the stars are removed. After calculating the peak values in the HIGAL image, these are removed, leaving the background image. SOme of the background emission is left behind, but will be smoothed with a large median filter to remove the small scale structures, leaving a large scale background image from which to detect the clouds. The z-axis represents temperature, where 0 defines the coolest objects, and 21000 represents the warmest objects. at various stages. Gaussian filters focus on comparing neighbouring pixels with an average weighting, biased towards the central pixels. A median filter also compares neighbouring pixels by calculating the median value. Using a median filter better preserves edges, meaning there is less chance of making extra artefacts in the image.

To find the stars in the image, it was necessary to detect the peak values. These are the maxima above a specified threshold of 3 sigma above the background within a localised region defined by a box size of 9. Multiple connected pixels with the same intensity are returned as a single source, otherwise there will only be one pixel per local region returned as a source. To create an image of these peaks, a Gaussian

ﬁlter is applied with a FWHM of 3 pixels. Then, this peak image multiplied by a normalisation factor of 56.55 (as calculated by the FWHM) to deﬁne the stars in the source. This star image is then subtracted from the original picture, removing the stars and bright points detected, to create an image of the background. Figure

4.2 shows a comparison between the original HIGAL image and the image with the stars removed. Most of the brightest points are removed with this method, however, a large area of background emission is left behind. This creates a level of uncertainty with anything detected in or around this area.

A median ﬁlter of 150 total pixels is then applied to this background image to CHAPTER 4. A SEARCH FOR IRDCS AT 70 µM 41 smooth it on a large scale, removing the small scale structures of the remaining background emission not removed by the star detection process (ﬁgure 4.3), before detecting the clouds on a smaller size scale. To create the 70 µm optical depth image, equation 3.2 is applied. The intensity of the foreground is assumed to be

0 in this instance, to simplify the removal of the extra background emission. This is unlikely to be the case, however and so the optical image would contain extra artefacts that are not clouds, but may be considered by the detection process as a cloud if the foreground has a lot of structure. Therefore, it is important to inspect the segments returned by the process to determine if they are real or not. Many of the points returned are likely to be spurious artefacts, rather than actual clouds, as at this point, the process has not been reﬁned. An investigation as to the accuracy of the process would need to be applied in order to improve on the process, thus reducing the possibility of artefacts being detected and revealing only the clouds.

The optical depth image (ﬁgure 4.4) is smoothed slightly with a small gaussian

ﬁlter before applying the detection process.

Photutils is a package associated with Astropy that is used to detect sources in astronomy. It uses DAOFIND and starfind to detect local maxima with a peak amplitude above a specified threshold, with a size and shape defined by a 2D Gaussian kernel. Detecting the threshold above which sources will be detected estimates the background and background root mean square using sigma-clipped statistics. This is calculated using signal to noise ratios as the sigma level above the background.

To detect the threshold, I used a value of 1.7 sigma. This threshold contains the background, so a 2D circular Gaussian kernel with a full width at half maximum

(FWHM) of 3 is applied before the final thresholding, creating a segmentation image (figure 4.5) showing detected sources, labelled by positive integers where the segments represent the isophotes of each source. Each of the segments represent a different source above the specified threshold. Overall, 1,134 sources are detected with this method. Some of these detected sources will be spurious artefacts left over from the foreground, or through the filtering process. Some artefacts will also be left over from the large area of noise left behind after the bright stars were removed CHAPTER 4. A SEARCH FOR IRDCS AT 70 µM 42

Figure 4.3: After the stars have been removed, the image is smoothed with a large median ﬁlter to create an average background from which to extract the clouds. The z-axis represents temperature, where 0 deﬁnes the coolest objects, and 21000 represents the warmest objects. CHAPTER 4. A SEARCH FOR IRDCS AT 70 µM 43

Figure 4.4: The optical depth image which then has the detection process applied to it. This is the background after the stars have been removed and a large median ﬁlter applied.The z-axis represents optical depth CHAPTER 4. A SEARCH FOR IRDCS AT 70 µM 44

Figure 4.5: A compilation of the sources found using DAOFIND. Each segment is each detected source above the threshold. Some sources may be over- lapping but represented as a single source.The z-axis represents an arbitrary counting system that numbers each source with a chronological order. CHAPTER 4. A SEARCH FOR IRDCS AT 70 µM 45

Figure 4.6: Overlaying the peak longitude and latitude of the 8 µm clouds (cyan circles) on top of the 70 µm image gives a quick visual comparison between the two. At quick glance, many of the structures seen in 8 µm are not necessarily seen at 70 µm. However, there is some correlation between the two when viewed by eye. Many clusters of peaks in 8 µm are around the same area as clusters of 70 µm clouds. from the original image. Some of the individual points may be a large cloud that has internal features that are prevalent at 70 µm. Hence, the actual number of clouds detected through the detection process will be signiﬁcantly lower.

To compare these clouds to those detected in Peretto and Fuller (2009), an overlay image was created (ﬁgure 4.6). Each of the cyan dots represents the peak longitude and latitude of the clouds as detected by Peretto and Fuller (2009). The majority of the clouds they detected are in the lower half of the region examined in this study, and are relatively close to the edge of the image. However, it is interesting to see the correlation between the two studies, as a large proportion of the clouds detected at 8 µm seem not to be explicitly detected at 70 µm. This will CHAPTER 4. A SEARCH FOR IRDCS AT 70 µM 46

Figure 4.7: A comparison of the HIGAL image with column densities and dust temperatures as described by Peretto et al. (2016). Comparing these images to the segmentation image can conﬁrm if the sources detected correspond to true peaks in column density, and so are ’true’ clouds. be further discussed in section 4.2.

4.2 Results

After detecting the sources that are above the speciﬁed threshold, the ’real’ clouds are identiﬁed. By conducting a visual inspection of the original HIGAL image with the column density and the dust temperature as described in Peretto et al.

(2016) (figure 4.7), a selection of segments identified by the detection process can be verified as real or an artefact. Below are some examples (figures 4.8 to 4.16). Clouds can be viewed in the original image as darker, dusty areas against the background.

These structures can also be seen in the column density and dust temperature maps. Some structures seen in the segmentation image are also seen in these other maps, which suggests they are real. At some points, there are structures seen in the maps, but have not been picked up by the detection process. This may mean that the detection process has missed these structures, or that there are no structures present at 70 µm.

Figure 4.8 shows an example of structures seen in the segmentation image.

Comparing this to the HIGAL image (top right) shows a similar, faint structure.

However, this structure is not reﬂected in the dust temperature map or the column density map. A diﬀerent structure can be seen in these maps though, so some sort of structure is in the area. The 70 µm image and detection process did not detect this, so this structure may not be a ’real’ cloud. CHAPTER 4. A SEARCH FOR IRDCS AT 70 µM 47

Figure 4.8: An example of sources from the segmentation process as compared to the other images.The ﬁrst image shows a structure seen by the detection process, compared with the original image (top right), the dust temperature (bottom left) and the column density (bottom right). The structure is barely visible in the HIGAL image. The image is not traced in the bottom images, although there is a diﬀerent structure seen. CHAPTER 4. A SEARCH FOR IRDCS AT 70 µM 48

Figure 4.9: In this image, some small points are seen by the detection process, but not necessarily seen in the other maps. Structures can be seen in the column density and temperature maps, but not in the original image. Some points from the segmentation image may correlate to the structures, but it is diﬃcult to conﬁrm outright. CHAPTER 4. A SEARCH FOR IRDCS AT 70 µM 49

Figure 4.10: The structure described by the detection process can clearly be seen in all three of the other images. This is more than likely a real cloud.

There are definite structures seen in the dust temperature and the column density maps in figure 4.9, and correlate well with each other. However, these structures are not seen in the segmentation image or the original image. The original image shows a few stars and a large area of noise in the area. This noise will not necessarily have been picked up in the star detection process, and so will not have been removed before the median filter. This means the noise will have been averaged out over the area during the filtering process, so will have produced artefacts in the detection process. Therefore, the segmentation process will have detected more areas above the threshold level.

Figure 4.10 shows an example where a similar structure is seen in all four images.

A comparison of the segmentation image and the HIGAL image shows a strong correlation. Each of the segments can be easily seen in the original image, with CHAPTER 4. A SEARCH FOR IRDCS AT 70 µM 50

Figure 4.11: The detection process found a lot of points above the threshold, classing them as clouds. These points are not seen in the original HIGAL image, with limited points seen in the column density map or the dust temperature map. This suggests these points are artefacts of the manipulation process. little diﬀerence between the two. Further comparison with the dust temperature and column density maps shows a similar structure. Not all that is seen in the top two images is represented in the bottom two, but a large proportion is seen.

Frequently, the segmentation process will detect regions above the speciﬁed threshold that are not necessarily seen in the other images, as shown in ﬁgure 4.11.

As seen in the HIGAL image, there is a lot of background emission, and structures within that emission present in this area. This will not have been screened out in the star detection process, and so was involved in the median ﬁlter, possibly causing artefacts to be created which were then detected by the segmentation process.

Comparing with the column density and the temperature maps shows that little of CHAPTER 4. A SEARCH FOR IRDCS AT 70 µM 51

Figure 4.12: As can be seen in the original HIGAL image, this area has a large amount of noise in the region. This will have skewed the manipulation process, as not all the noise will have been removed prior to the median ﬁlter. This may be the reason for many points being detected. There is some structure here, as seen in the dust temperature and column density maps, but nothing has been identiﬁed clearly by the detection process. that detected in the segmentation image is actually real. There are some cold dense region in the area, but it is not easy to tell if these regions have been detected in the segmentation process.

Another example of background emission and associated structures disrupting the results is shown by ﬁgure 4.12. The 70 µm HIGAL image shows a large area of bright material, which prevents the surrounding dark clouds from being observed, if there are any in this area. This noise, again, will not have been screened out during the star detection process and hence been detected in the segmentation process. Looking at the dust temperature shows that the noise is relatively warm compared to the surrounding area, with a small area of cold next to it. This area also happens to be the most dense region in the area. There may be a dark cloud in CHAPTER 4. A SEARCH FOR IRDCS AT 70 µM 52

Figure 4.13: This ring that has been detected by the segmentation process is clearly identiﬁable in all three other images. This is therefore a real cloud. Most of the cloud has been detected by the segmentation process, meaning a large proportion of this cloud is visible in 70 µm. this area, but the detection process used here was not signiﬁcant enough to remove the surrounding noise and hence isolate the cloud.

Figure 4.13 is a good example of the detection process successfully isolating a cloud and detecting the majority of the cloud. The ring structure seen in the segmentation image can clearly be seen in the original HIGAL image, the dust temperature image and the column density image, meaning that this is more than likely a real cloud. The cloud is still cool, but appears in a colder region of this area compared to other regions previously observed in this study. It appears to be only slightly denser than the surrounding area too, with three small distinct dense regions.

In figure 4.14, the segmentation image shows a few small areas that appear above the threshold. These areas, and more can be seen in the original HIGAL image in front of the noisy background. It is very filamentary, but there is a structure visible. This same structure can be seen in the column density and dust temperature maps. There is a lot of diffuse material in the area, with the denser regions being picked up by the detection process.

The set of images shown in ﬁgure 4.15 show and interesting structure seen CHAPTER 4. A SEARCH FOR IRDCS AT 70 µM 53

Figure 4.14: An example of structures seen in most of the images. There are some ﬁlamentary structures seen in the column density and dust temperature maps, but hidden by clouds and noise in the HIGAL image. Some of these structures are seen by the detection process. CHAPTER 4. A SEARCH FOR IRDCS AT 70 µM 54

Figure 4.15: A very obvious square structure seen by HIGAL and seen in the column density and dust temperature maps, but not picked up in in the segmentation process. CHAPTER 4. A SEARCH FOR IRDCS AT 70 µM 55 in three of the four images, but not in the segmentation image. This cool dense structure has a very interesting square shape that is very obvious, however has not been detected by the segmentation process. This structure would be interesting to investigate further in a diﬀerent study.

To conﬁrm the detection process has detected a signiﬁcant number of clouds, a visual inspection was conducted by comparing the segmentation image to the original 70 µm HIGAL image, the dust temperature map and the column density map. To qualify as a cloud, the segmentation image had to portray a section of the structure seen in the column density and the dust temperature maps. Not all the structure had to be present in the segmentation image, but a recognisable point had to be seen. The structure need not necessarily have been present in the HIGAL image, as various factors could result in the cloud being hidden.

Of these areas considered to have absorption features that could be cloud candidates, approximately 3 were also in the Peretto and Fuller (2009) catalogue. Figure

4.6 shows the two catalogues together, with the segmentation image in the background and the 8 µm clouds represented by their peak longitude and latitude, as described in Peretto and Fuller (2009), as cyan circles on top. The majority of the clouds seen at 8 µm appear in the same region as the conﬁrmed clouds seen at

70 µm. A visual inspection cannot provide exclusive quantitative comparisons between the two catalogues. However, it can provide the most reliable assessment and comparison of the relationship between the wavelengths and the clouds observed.

There are some cloud candidates detected at 8 µm that seem to be isolated in the

70 µm segmentation image. This could be due to a variety of reasons, such as artefact creation, or the clouds detected at 8 µm may not be visible at 70 µm. A more thorough and quantitative study would determine the reason for this discrepancy as this study only comments on ﬁndings relating to an inspection conducted by eye. Chapter 5

Discussion

Peretto and Fuller (2009) created a catalogue of infrared dark clouds seen at 8 µm between Galactic longitude and latitude 10◦ < |l| <65◦ and |b| < 1◦ above a column

22 −2 density threshold of NH2 & 1 × 10 cm . This resulted in nearly 12,000 clouds being detected in the area. Follow up studies determined that there were just over

1,700 protostellar (Peretto et al., 2016) and starless clumps within these clouds, and approximately 76% of these clouds were real. Analysis on the column density map revealed that one third of the peaks were forming stars, whilst the temperature map conﬁrmed that dust temperatures are not uniform throughout the cloud. The aim of this study was to expand on the catalogue produced in Peretto and Fuller (2009), to study clouds at 70 µm and observe any diﬀerences between the two catalogues.

By removing the stars in the image, smoothing the background, creating an optical depth image, then applying a detection process, more than 1,000 individual sources are detected within this 2◦ by 2◦ region. Not all of these will be clouds however. Some will be the result of artefacts being made during the ﬁltering process, some will be the result of the foreground emission, and some will be the result of a noisy background. Some individual points may also belong to a common cloud, but when observed at 70 µm, only small portions of the cloud will be visible. Therefore, the actual number of real clouds will be signiﬁcantly less than the number stated here. Further investigation will be needed to determine the exact value of real clouds (section 5.1).

56 CHAPTER 5. DISCUSSION 57

In this study, a visual inspection is conducted to determine the success of the detection process. The inspection was a comparison between four images: the segmentation image produced at the end of the detection process, the original 70

µm HIGAL image, the dust temperature map, and the column density map, the latter both being described by Peretto et al. (2016). To be identiﬁed as a real cloud candidate, any structure seen had to also be identiﬁed in at least the dust temperature and column density maps. If the structure was also seen in the HIGAL image, this was further evidence for the point being real. By this method, at least

10 areas were identiﬁed as possessing real clouds within them.

Whilst there are many points detected by the segmentation process, many of these did not correlate to points in the dust temperature and column density maps.

There are a number of reasons why this might be so. Whilst the star detection process did remove the majority of the stars in the image, some smaller, less luminous stars may still have been left in the image. Also, not all of the structures in the background emission have been reduced in the image. For example, there is a large feature seen at l = 15◦, b = −0.75◦ that was not removed in the star detection process. This area, and others like it, will have been incorporated into the median

filter. Whilst the median filter has less of a chance of producing artefacts than a Gaussian filter, large filters can still produce these artefacts. These areas are then detected in the segmentation process as having larger peak amplitude than the specified threshold, so the process returns these points as detected sources.

Another procedure would then be needed to determine if those points returned are genuine clouds or artefacts.

Equation 3.2 requires the knowledge of Ifore to construct the opacity. It is possible to measure Ifore, as outlined in section 3.2 (figure 3.2), however, it can be difficult to constrain, as the value can be anywhere between the values of Izl and Imin. In this study, it was assumed that the foreground emission was zero. In reality, this is not so. It is important to constrain the foreground emission when constructing these opacity maps. As Peretto and Fuller (2009) discussed, Ifore can drastically change the observed opacity of the clouds, with the opacity drastically CHAPTER 5. DISCUSSION 58 increasing as the foreground intensity increases, especially towards the peak, with the effect being even more exaggerated in shallow clouds. The opacity maps for the 8

µm catalogue were constructed under the assumption that Ifore = Izl, the minimum value it could have. However, when compared to the dust continuum emission of the cores, the results indicated that the value for the foreground intensity was very much underestimated. Therefore, the optical depth of the cloud is underestimated, and the internal density proﬁle is also incorrect. As this work is an initial exploration of the feasibility of the techniques, this is not an immediate issue, although further investigation into a more accurate value for Ifore would provide greater insight into the structures presented with this method.

Comparing the clouds detected at 70 µm visually with the clouds catalogues at 8 µm reveals at least three areas that have good correlation with each other.

A further quantitative study can provide more details about the exact number of clouds detected in both, but the results produced in this study provide a base for which to look at depth. Most of the peaks detected by Peretto and Fuller (2009) fall in similar regions to those detected at 70 µmm suggesting that many of the clouds seen at 8 µm have further structures at 70 µm, making these clouds very complex. There are some areas where there is little to no correlation between the two catalogues. These clouds may be smaller, and so not detected by the segmentation process, or these clouds may not absorb 70 µm wavelengths. A further study of these discrepancies can provide more answers.

5.1 Further Work

To further improve this study, many more actions can be taken. For example, a quantitative comparison between the sources detected at 70 µm and those at 8 µm to determine if the proportion of clouds seen in both catalogues, or those seen in only one or the other. This would help determine further investigation into clouds at different wavelengths. Clouds seen at multiple wavelengths may shed some light on the internal structure. Not everything will be seen at all wavelengths, and so CHAPTER 5. DISCUSSION 59 internal clumps or cores may be found, which can indicate star formation. By viewing these structures at different wavelengths, probing the internal workings of the clouds becomes easier and more informative. The shape and depth can also be determined more easily by comparing differing wavelengths. More clouds common to both catalogues will allow for more opportunities to compare wavelengths, internal structures and any common or differing properties within clouds seen at different wavelengths.

In some cases, clouds were only seen in one wavelength or the other. Some of these instances may just be artefacts that have been picked up the detection processes, or these clouds only absorb that wavelength. This is due to the diﬀer- ing dust properties at various wavelengths. Further investigation of these areas would allow a distinction to be made as to whether these clouds are real or just artefacts, and if they are real, why they are only viewable in the one wavelength.

Imaging these clouds at other wavelengths would also determine properties and substructures within them.

Peretto and Fuller (2009) produced column densities for their catalogue, which were used in this study to conﬁrm points detected by the segmentation process.

These column densities were produced using the dust models at 8 µm, not at 70

µm. Using the 70 µm dust models would provide column densities for the clouds detected in this survey, and would provide further details on the internal structures of the clouds. Comparing the column densities between the two surveys would also allow for a comparison of the dense regions within clouds that are common to both catalogues. This could help to identify any cores within these clouds, and also any possible sites for star formation. This comparison of column densities could also provide details of the conditions prior to star formation in IRDCs.

Peretto et al. (2016) compared the catalogue from Peretto and Fuller (2009) with Herschel data to determine the fraction of real clouds. They determined three criteria with which to determine these real clouds from spurious ones, and determined that approximately 76% of the clouds seen at 8 µm were real. By using a similar method, it would be possible to determine the fraction of real clouds CHAPTER 5. DISCUSSION 60 detected in this survey. This requires the column densities to be calculated, and the cloud boundaries to be determined. Within the research conducted by Peretto et al.

(2016), it was noted that some of the smallest clouds may have been missed, and some clouds may have been misidentiﬁed as real due to the nature of the background emission, and so another visual inspection would need to be undertaken. Once the fraction of real clouds is known, comparing the two sets of fractions would provide a better indication of the fraction of real clouds seen in further studies. Chapter 6

Conclusions

Within this study, over 1,000 individual points were detected at 70 µm. Visually comparing these points to a temperature map and a column density map produced at least 10 areas with conﬁrmed clouds. Further comparison with the catalogue of 8 µm clouds show three regions of deﬁnite overlap between the two catalogues, although most of the peaks detected at 8 µm correlate to areas of segments detected at 70 µm. There are some area where there are peaks at 8 µm that are not correlated to points at 70 µm. Further investigation into these areas would provide more information on whether these clouds are real, and if there are any there structures within them. Creating and comparing column densities between the two catalogues would also allow for more correlations between them to be found and substructures to be determined.

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