HI in the M31/M33 Environment

Home , Magellanic Stream

A thesis presented to

the faculty of the College of Arts and Sciences of Ohio University

In partial fulfillment

of the requirements for the degree

Master of Science

Nicole L. Free

November 2010

This thesis titled

HI in the M31/M33 Environment

NICOLE L. FREE

has been approved for

the Department of Physics and Astronomy

and the College of Arts and Sciences by

Felix J. Lockman

Adjunct Professor of Physics and Astronomy

Joseph C. Shields

Professor of Physics and Astronomy

Benjamin M. Ogles

Dean, College of Arts and Sciences 3

ABSTRACT

FREE, NICOLE L., M.S., November 2010, Physics and Astronomy

HI in the M31/M33 Environment (70 pp.)

Director of Thesis: Felix J. Lockman and Joseph C. Shields

With recent debate about a reported neutral hydrogen, HI, streamer between

M33 and M31, we set out to determine the existence of the HI streamer. Using the

National Radio Astronomy Observatory’s 100 m Green Bank Telescope, with 9.1´ angular resolution, we mapped the HI in the region from Wright’s Cloud and M33 through the location of the streamer features, as reported by Braun and Thilker (2004). To verify the findings of Braun and Thilker, we also performed pointed observations at two of the three local maxima within their maps.

From our observations, we were able to confirm the existence of the three local maxima points. The three features have column densities ranging from 2.3 to 5.4 , with most being of the order of magnitude of the latter. The masses

of the features range from 6.6 to 2.2 . All of the features appear to have more structure and be more compact than previously thought. After examining the Wright’s Cloud region to determine if there was any comparable Galactic

HI in the region, it was found that these features are inconsistent with the Galactic High

Velocity Clouds in the area of observation. Though the origin of the streamer features cannot concretely be set, with the Pan-Andromeda Archeological Survey’s (PAndAS) findings of optical evidence of recent interaction between M33 and M31, the origin of the features from such an interaction remains probable. 4

Approved: ______

Felix J. Lockman

Adjunct Professor of Physics and Astronomy

Approved: ______

Joseph C. Shields

Professor of Physics and Astronomy 5

ACKNOWLEDGMENTS

I would like to express my heartfelt thanks to all those who have helped and supported me through the completion of this thesis. My advisors Joseph Shields and Jay

Lockman for their guidance and encouragement. The other members of my committee,

Nancy Sandler and Kenneth Hicks, for their guidance. Don Roth, without whom I would probable still be working out some computer problems. The National Radio Astronomy

Observatory and the Department of Physics and Astronomy at Ohio University for my financial support.

All of my wonderful classmates and fellow astronomy students for their friendship and assistance. Chris and Kate Willis, my dear friends, for their continued love, support, and understanding. Heath Kersell for his love and friendship which have brought joy to even the longest homework assignments. My family, Dwight Free, Mary

Ann Wisherd, and Bill and Colleen Grissom for their love, guidance, and continual encouragement. Natalie Free, my beloved sister, whose love and support are one of the greatest blessings in my life. My late grandmother, Phyllis Wilt, for her love, support, and never ceasing belief in me. To the Lord God for His steadfast love.

TABLE OF CONTENTS

Page

Abstract ...... 3 Acknowledgments...... 5 List of Tables ...... 8 List of Figures ...... 9 Chapter 1: Introduction ...... 10 1.1 21 Centimeter Neutral Hydrogen ...... 12 1.2 High Velocity Clouds ...... 14 1.3 The Magellanic Stream ...... 16 1.4 Wright’s Cloud ...... 19 1.5 M33 ...... 20 Chapter 2: Observations and Data Reduction ...... 27 2.1 Observations ...... 27 2.1.1 Pointed Observations ...... 27 2.1.2 Maps ...... 28 2.2 Data Reduction ...... 31 2.2.1 GBTIDL ...... 33 2.2.2 IdlToSdfits ...... 34 2.2.3 AIPS ...... 34 2.2.4 Python ...... 35 2.2.5 CASA ...... 37 Chapter 3: Results and Discussions ...... 38 3.1 Analytical Tools and Procedure ...... 38 3.2 Completeness of Study ...... 40 3.2.1 Velocity ...... 40 3.2.2 Position ...... 40 3.2.3 HI Column Density and Mass ...... 41 3.3 M31/M33 Stream ...... 42 3.3.1 Braun I ...... 46 7

3.3.2 Braun II ...... 50 3.3.3 Braun III ...... 53 Chapter 4: Conclusions ...... 55 4.1 Milky Way High Velocity Clouds ...... 55 4.2 High Velocity Clouds Originating from M31 ...... 56 4.3 Association with M31 Dwarf Galaxies ...... 56 4.4 M31/M33 HI Stream ...... 57 4.5 Future Considerations ...... 58 References ...... 60 Appendix: Automatic Residual Instrumental Baseline Removal Python Script ...... 63

LIST OF TABLES

Page

Table 1: Characteristics of M33, M31, AND I, and AND XV ………………………...... 22

Table 2: Distance Between M31/M33 Stream Elements and Local Group Galaxies … 22

Table 3: NHI and MHI Sensitivity Limits ……………………………………………… 41

Table 4 A: M31/M33 Stream Features ………………………………………...…….... 42

Table 4 B: Continuation of M31/M33 Stream Features …………………………...….. 43

Table 4 C: Continuation of M31/M33 Stream Features ……………………...……….. 44

Table 4 D: Continuation of M31/M33 Stream Features …...………………………….. 45

LIST OF FIGURES

Page

Figure 1: Optical and Radio Image of the M81 System ...... 13

Figure 2: Magellanic System ...... 17

Figure 3: Filaments and CHVCs of the Magellanic Stream ...... 19

Figure 4: Braun and Thilker (2004) HI Survey Map ...... 23

Figure 5: Putman et al. (2009) M33 HI Map ...... 24

Figure 6: PAndAS Survey Map ………………………………………………………. 25

Figure 7: PAndAS Map With Newly Discovered Dwarf Galaxies …....……………... 26

Figure 8: Gridding Regions and Locations of Pointed Observations ………………… 30

Figure 9: Green Bank Telescope …………..……….……………………………..….. 32

Figure 10: Diagram of Data Reduction Process …...……………………………….… 33

Figure 11: Spectra from Braun I Pointed Observations …..………………………..… 47

Figure 12: Spectra from Braun I Data Cubes ……..………………………………..… 48

Figure 13: Braun I Integrated NHI Maps …..……..………………………………..…. 49

Figure 14: Spectra from Braun II Data Cubes …….…………………………………. 51

Figure 15: Braun II Integrated NHI Maps ………..………………………………….... 52

Figure 16: Spectra Braun III Pointed Observations .....………………………………. 54

Figure 17: Grossi et al. (2008) M33 HVC Map …….………………………………... 58 10

CHAPTER 1: INTRODUCTION

According to hierarchical models of structure formation, large structures in the universe formed from the merger of low mass objects. The Lambda cold dark matter model, which assumes that the universe is dominated by dark energy and dark matter, predicts this type of structure formation (Spergel et al. 2003); after inflation, irregularities in the matter and radiation density allowed small dark matter halos to form. Evidence for this comes from the very slight differences that are seen in the temperature of the cosmic microwave background (CMB), which are indicators of density variations. As these smaller dark matter halos began to merge, larger and larger halos formed, and the baryonic matter within these halos became the observed structures in the universe.

The accretion of mass was not only necessary for the initial formation of structure, but is an ongoing process that is responsible for the continuing evolution of galaxies. The continual accretion of gas in galaxies is necessary to account for the observed star formation rates. Based on the stellar population in the solar neighborhood and the chemical abundances in disk stars, the in-fall of neutral hydrogen (HI) gas to the

Galaxy would have to be continuous as the star formation rate has remained fairly constant. Also, the metallicities observed in the disk stars are inconsistent with models of a closed-box galaxy requiring the accretion of metal-poor gas (Binney, Dehnen, &

Bertelli 2000). Star formation is not the only aspect of galaxy formation that is dependent on the in-fall of gas. Recent models show that the central stellar bar1 that is found within approximately 75% of spiral galaxies goes through a cycle of creation and destruction

1 This temporary feature found in most spiral galaxies is a grouping of the central components of galaxies into a linear bar shape. This bar is able to rotate, and is thought to be a site of star formation as gas falls into this smaller spatial area. 11 that is mediated by gas. Gravitational instabilities initially form the central stellar bar; the bar in turn causes gas to flow inward, weakening and eventually destroying the bar through the absorption of angular momentum (Bournaud & Combes 2002). The in-flow of external gas replenishes the disk and allows for a new stage of bar growth (Combes

2009). This theoretical work is supported by the recent finding in the COSMOS field that, at higher redshifts, the number of observed bars decreases (Sheth et al. 2008). Younger galaxies would be expected to have larger quantities of gas, as this gas would not yet have been used for star formation. Therefore one would expect fewer bars within the galaxies as there would still be sufficient internal gas to weaken or destroy this feature.

The role of gas, like HI, in the evolution of galaxies can be studied by observing the continual in-fall as seen within the Milky Way and other local galaxies. HI is falling into the Galaxy through high velocity clouds (HVC), as discussed in section 1.2, and through accretion of gas from dwarf galaxies, as seen with the Magellanic Stream (MS).

While this HI in-fall has been studied for the Milky Way, by observing other galaxies we benefit from observing the process from a different perspective. Within the Local Group2, there reside two other spiral galaxies, M33 or Triangulum and M31 or Andromeda, which allow for an in-depth study of the nature of galaxies from an external perspective. Also, by observing galaxies in the Local Group observations can be made of systems that have been well characterized. M31 is the closest spiral galaxy that is comparable in size to the

Milky Way; by observing M31 one can see if HI structures found in the Milky Way are typical of spiral galaxies. The HI observations can also be compared with observations in

2 The Local Group is the group of galaxies that contains the Milky Way and more than thirty other galaxies. In the group of galaxies the most massive galaxies are the two spirals: the Milky Way and M31. M33, the third spiral in the group, is the third most massive. 12 other wavelengths of the electromagnetic spectrum, to see more fully what is occurring within the system. To that end, we have been observing the HI around M33 using the

National Radio Astronomy Observatory’s 100 meter Green Bank Telescope (GBT)3 to determine the existence of an HI streamer between M33 and M31.

In this thesis we first outline the physics and nature of HI observations in the

M33 system. In Chapter 2 we discuss the GBT observations and the data reduction procedure. Data and results are presented in Chapter 3. Finally, in Chapter 4 we present the conclusions.

1.1 21 Centimeter Neutral Hydrogen

Emission from HI is observed at a wavelength of 21 centimeters. This emission is generated when the spins of the proton and electron in the hydrogen atom switch from being parallel to anti-parallel; the difference in energy between the two states corresponds to 1420 MHz or a wavelength of 21 cm. The difference in the two spin states, parallel or anti-parallel, comes from hyperfine splitting that removes the degeneracy of the ground state of the atom. The probability of the spins switching alignment is very small, as this is a forbidden transition. Hydrogen is the main component of the interstellar medium (ISM), the gas and dust that exist in the regions between the stars. Therefore, even though the probability of the transition is very small, with the quantity of HI that exists, the 21- centimeter line of HI is directly observable and a very important line in radio astronomy.

In the ongoing efforts to understand how galaxies formed and how they continue to evolve, HI is a vital tool. The HI of a typical spiral galaxy extends much farther out

3 The National Radio Astronomy Observatory (NRAO) is operated by Associated Universities, Inc. under a cooperative agreement with the National Science Foundation. 13 than the optical portions of the galaxy, because the distant gas is more weakly bound to the galaxy than the stars. Therefore, one can find evidence of galactic interactions that may not be apparent in the optical images of the galaxies. Consider the interactions within the M81/M82/NGC3077 system shown in an optical and radio image in Figure 1.

The extent of the interactions between these three galaxies cannot be discerned from the optical image; instead, one must consider the radio image of HI to see the interactions.

Clouds of HI are also seen around seemingly isolated spiral galaxies; these clouds can help us to understand the origins of the gas needed to maintain the current star formation rates and to see evidence of minor interactions, where the structure of a galaxy is left relatively unaltered by the interaction (Sancisi et al. 2008).

Figure 1: Left: Optical image of the M81/M82/NGC3077 system from the Digital Sky Survey. Right: HI image of the same region, where the colors represent the density of HI; red represents the higher density gas. This image clearly shows the galaxies interacting from the extended HI emission (Yun 1994).

1.2 High Velocity Clouds

High Velocity Clouds, clouds of HI having radial velocities discrepant with galactic rotation, were first observed around the Milky Way in 1963 (Muller et al. 1963).

Since that time, it has been found that these clouds can be neutral or have an ionized component, and that they cover the majority of the sky; approximately 40% of the sky is covered by neutral hydrogen and approximately 85% by ionized hydrogen (Lockman et al. 2002; Shull et al. 2009). They can be isolated like Compact High Velocity Clouds

(CHVCs), or they can be found in large complexes, associated with galaxies or other clouds. Distances to HVCs are difficult to determine. Association with objects of known distances, like galaxies, and measurement of absorption lines against background objects, are two ways of limiting or determining the distance to a given HVC. For many clouds, though, there are no clear associations, and absorption lines can only place bounds on the distance if the cloud lies between bright stars along the line of sight. Knowing the distance is very important as mass ∝ D2, size ∝ D, nd v u d n ity ∝ D-1, where D is the distance to the cloud (van Woerden & Wakker 2004).

HVCs are not unique to the Milky Way. In 1988 van der Hulst and Sancisi found the first extra-galactic HVC around the galaxy M101. Since then, HVCs have been detected around M31, M33, the NCG 2403 group, the M81 group, and other galaxy groups. (Braun & Thilker 2004; Westmeier, Braun, & Thilker 2005; Grossi et al. 2008;

Westmeier, Brüns, & Kerp 2008; Putman 2009; Chynoweth et al. 2009; Brinks et al.

2007; Yun & Ho 1993; Pisano et al. 2007). 15

Since the discovery of HVCs, many theories as to the origin of the clouds have been put forth (Oort 1966, Blitz et al. 1999, Shapiro & Field 1976). In the galactic fountain model supernova explosions drive hot gas out of the disk and a few kilo-parsecs

(kpc) into the halo. As the gas radiatively cools, it begins to fall back onto the disk, forming the HVCs, and eventually re-entering the galaxy. This was first proposed by

Shapiro and Field (1976), and though this could explain some of the gas, it does not account for the HVCs outside of the galactic corona, which extends a few kpc out from the disk of the Galaxy (de Avillez 2000). Observational evidence for the galactic fountain comes from the detection of “superbubbles” (Pidopryhora et al. 2007). A superbubble is a shell formed within the interstellar medium (ISM) that is being pushed into the halo by stellar winds and multiple supernovae.

Cold accretion is another model for HVC formation. Galaxies are believed to lie inside reservoirs of gas at . If some of this gas cools to approximately

10000K, it could condense and fall toward the galaxy, possibly along filaments. The idea of cold gas forming along filaments has been supported by the ability of numerical simulations to reproduce the gas requirements of galaxies (Dekel & Birnboim 2006;

Kereš & Hernquist 2009).

A possible connection between HVCs and dark matter halos was first suggested by Blitz et al. (1999), as a solution to the “missing” satellite problem. In the Lambda cold dark matter model, small dark matter halos merge to form larger structures. According to this model, though, there should be a large number of small satellites around every galaxy. Since the number of observed dwarf galaxies is insufficient to account for all of 16 the proposed small satellites, HVCs were suggested as the visible counterpart of the dark matter halos (Braun & Burton 2000). Such HVCs would be compact and isolated,

CHVCs, but these types of HVCs are not observed around galaxies besides the Milky

Way. Currently, there are still too few observed HVCs to account for all of the predicted dark matter halos.

Another option for the origin of HVCs is that they are the result of the interactions of galaxies. As galaxies interact or merge, HI is stripped off the galaxies. This stripped HI could then form a streamer within the interacting system with which the HVCs are associated, or the clouds could become more isolated features. Chynoweth et al. (2009) observed such HVCs in association with tidal streamers in the M81/M82 system.

Evidence for HVCs as the consequence of galactic interactions is also clearly seen within the Milky Way through the Magellanic Stream. There is clear evidence for this option of the HVCs origins, as well as for the Galactic fountain option (Bregman 2004).

1.3 The Magellanic Stream

The Magellanic system is composed of the Small Magellanic Clouds (SMC), the

Large Magellanic Clouds (LMC), the Magellanic Bridge, the Leading Arm, and the

Magellanic Stream (MS). This system extends at least 200° from the MS to the Leading

Arm. Both the SMC and the LMC are irregular dwarf galaxies that are gravitationally bound to each other and, most likely, to the Milky Way. The Magellanic Bridge is a filament of HI created by the interactions of the LMC and the SMC that connects the two galaxies. It is also the site of recent star formation, which is common in interacting systems. The Leading Arm is a tidally stripped component made of strings of clouds that 17 begins at the Magellanic Bridge and continues toward the galactic north. This feature is very clumpy, with the clumps being connected by diffuse filaments of HI. All of the components of the Magellanic System are shown in Figure 2.

LMC Magellanic Bridge Leading SMC Arm MS

Figure 2: HI image of the full extent of the Magellanic system (Nidever et al. 2010). The color scale is for the HI column density (log( ) in units of ). The longitude, LMS, axis is centered on the LMC, and the latitude axis, BMS, is centered on a great circle that most nearly bisects the MS. Top: The white dots represent the CHVCs from Westmeier & Koribalski (2008). Bottom: Total intensity of the Magellanic HI integrated along BMS, and plotted against velocity. The contours represent the plane of the Milky Way, and the white dots are the previously mentioned CHVCs.

Discovered in 1964 (Dieter 1964) and identified by Mathewson et al. (1974), the

MS is the result of the interactions between the LMC, the SMC, and the Milky Way. It extends approximately 140° across the sky and is composed of at least five filaments

(Stanimirović et al. 2008; Nidever et al. 2010), as shown in Figure 3. A sixth filament may also exist, as there appears to be a connection between a string of CHVCs, relatively compact and isolated HVCs, and Wright’s Cloud (WC) (Westmeier & Koribalski 2008;

Nidever et al. 2010), which is illustrated in Figure 3. There are no known stellar components to the MS. Therefore like the M81/M82 system shown in Figure 1, the MS is the consequence of an interaction that would be missed if not studied in HI.

Figure 3: Combined HI data from the Nidever et al. (2010) GBT data cube, the Stanimirović et al. (2008) data, part of the Brüns et al. (2005) data cube, and the Braun and Thilker (2004) data cube (Nidever et al. 2010). The dotted-boxed regions show the five filaments of the MS, S0-S4. The white dots show the CHVCs that appear to create a sixth filament that goes toward Wright’s Cloud (WC). The color scale is the same as in Figure 2.

1.4 Wright’s Cloud

Wright’s Cloud (WC) is an anomalous HVC in the region between the Magellanic

Stream and M33. It was first discovered in 1979 and named for its discover M. C. H.

Wright. In the initial paper, Wright (1979) examines the possibility that this HVC might be associated with either M33 or Galactic HVCs. Though the typical velocities of M33 20 and WC differ by approximately 200 km/s (LSR4), the spatial proximity of the two bodies suggests that there might be an association. Wright also notes that WC has many of the same characteristics as other Galactic HVCs in the vicinity, and therefore may be associated with the Galaxy. Recently it has been proposed that WC is part of the

Magellanic Stream, or part of a group that fell into the Milky Way with the MS

(D’Onghia & Lake 2008, Nidever et al. 2010). Similar to the five filaments extending to the west of the Magellanic Clouds, there is an apparent filament of CHVCs that leads from the MS to WC, as shown by the white dots in Figure 2 and in Figure 3. Though the

MS is an object of great interest, as it is the closest observable case of a galaxy accreting a gas rich dwarf galaxy, the possible connection with WC is still not determined.

1.5 M33

M33, also known as Triangulum, is the third largest spiral galaxy in the Local

Group, though much smaller than either the Milky Way or M31. Even though there has long been observed to be a warp in the HI disk of M33, the stellar components seem relatively undisturbed, implying that M33 has not undergone any recent mergers or major interactions. M33 is most likely gravitationally bound to M31, though, and might therefore be expected to show signs of tidal disturbances (van der Marel & Guhathakurta

2008; Loeb et al. 2005). As an interaction between these two galaxies would be the closest observable one outside of the Galaxy, the M31/M33 region has been studied in HI

(Braun & Thilker 2004; Westmeier, Braun, & Thilker 2005; Grossi et al. 2008;

Westmeier, Brüns, & Kerp 2008; Putman 2009). Evidence for a recent interaction with

4 The local standard or rest (LSR) is a point instantaneously centered on the sun and moving in a circular orbit around the Galactic center. 21

M31 was discovered in the survey by Braun and Thilker (2004) using the GBT and the

Westerbork Synthesis Radio Telescope with a smoothed beam size of 48´. Diffuse HI was detected between M33 and M31, forming an HI streamer between the two galaxies

-2 with an overall column density of 17.0 cm , and with three local maxima

(later referred to as Braun I, Braun II, and Braun III) with 17.5, as shown in

Figure 4. In the surveys that followed Braun and Thilker, there was no detection of the streamer, and Putman et al. (2009) even questioned its existence. The map of M33 by

Putman et al. (2009) made with the Arecibo Radio Telescope with an angular resolution of 3.4´, Figure 5, shows no signs of interaction between the two Local Group galaxies. In each of the later surveys, though, the region of the streamer was not covered, and the level of sensitivity needed to detect the very faint emission was not achieved.

Using the data of Braun and Thilker, a model showing that such a streamer could be created while leaving the stellar population relatively untouched, was created by Bekki

(2008) using gravitational interactions in an N-body simulation. What was found was that during the first interaction between M31 and M33 4-8 Gyrs ago, a tidal stream could have been created. The streamer that was created in the simulations had primarily persisted in the vicinity of M31, leaving little directly connecting the streamer to M33. This is consistent with Braun and Thilker (2004) finding all three local maxima closer to M31 than M33. The positions and systemic velocities, if known, are listed in Table 1 for M33,

M31, and two other dwarf galaxies in the field of the stream features, AND I and AND

XV. The distance between each feature and these galaxies can be found in Table 2, where for each galaxy, the features were taken to be at the distance of the galaxy. 22

Table 1

Characteristics of M33, M31, AND I, and AND XV

5 6 7 Local Group Distance α δ l b Systemic

Galaxies (kpc) (h m s) (° ´ ʺ) (km/s)

M33 840 01 32 24 +30 14 24 133.3 -31.8 -163.71

M31 785 00 42 47 +41 40 33 121.2 -21.6 -306.04

AND I 745 00 45 40 +38 02 28 121.7 -24.8

AND XV 630 01 14 19 +38 07 03 127.9 -24.5

Table 2

Distance Between M31/M33 Stream Elements and Local Group Galaxies

M31/M33 Observation M33 M31 AND I AND XV

Stream Features Type ( kpc ) ( kpc ) ( kpc ) ( kpc )

Braun I Pointed 105.91 107.47 85.65 14.76

Braun I Mapping 103.42 108.54 85.73 15.76

Braun II Mapping 125.11 79.63 56.38 12.59

Braun III Pointed 159.73 47.80 38.79 33.20

Braun III Mapping 161.18 45.27 35.20 35.52

5Distance from Earth to the other Local Group galaxies. The distance for M33 is from Braun & Thilker 2004, the distance for M31 is from McConnachie & Irwin 2006, and the distances for AND I and AND XV are from Grcevich & Putman 2009. 6The α and δ for M33 and M31 are from McConnachie & Irwin 2006; the distance for AND I and AND XV are from Grcevich & Putman 2009. 7 Braun & Thilker 2004 23

Figure 4: Map of the HI survey by Braun and Thilker (2004). M33, M31, and WC are labeled in red with arrows pointing to them. The M33/M31 HI streamer is the diagonal detection above M33 pointing toward M31, with the three local maxima circled in red. The features on the right of each of the previously mentioned features are Galactic, and according to Nidever et al. (2010) may be extensions of the MS. The contours are at levels 17.0, 17.5, 18.0, …, 20.5, and the levels in the streamer are log(NHI) = 17.0 and 17.5.

Figure 5: Putman et al. (2009) HI column density map centered on M33. Two HI features are labeled, the Northern Arc and the Southern Cloud. The 3.2e20 contour marks the outmost contour that far-ultra violet emission was detected in. Contours are at 18.9, 19.1, 19.3, …, 21.2.

Recent work by the Pan-Andromeda Archaeological Survey (PAndAS) is lending support to the idea of a recent interaction between M31 and M33. PAndAS is an optical survey of the M31 region. Making use of the Canada-France-Hawaii Telescope and covering an area of more than 300 square degrees, this survey has been looking for Red

Giant Branch (RGB) stars in the very extended part of M31 and M33. If galaxies continue to grow by the accretion of smaller ones, as in the hierarchical model of structure formation, there should be loosely bound stars surrounding the galaxy as a result of the tidal disruption of the smaller systems. Relevant results from PAndAS are shown in Figure 6. To the northwest side of M33, the galaxy in the lower left, there is an extension of RGB stars that appears to be directed toward M31, upper right. This stellar 25 warping corresponds to the well known HI warp in the M33 disk, and may be its stellar counterpart. Evidence that this feature is a possible result of an interaction with M31 is supported by the smaller feature on the southeast side, which suggests a tidal origin.

Figure 6: The PAndAS image of the M31/M33 region. M33 is in the lower left corner and M31 in the upper right. The insert in the top left corner is a close up of the center most area of M31. The dashed lines in the figure represent the survey boundary for M31 and M33, 150 and 50 kpc respectively. Roman numerals indicate visible dwarf galaxies, and the numbers within the circles indicate the largest and most obvious substructure. Scale images of the standard optical regions of the two galaxies have been overlaid (McConnachie et al. 2009).

Based on N-body simulations of McConnachie et al. (2009), performed to understand the PAndAS results, such an interaction with M31 could have occurred approximately 4-6 Gyrs ago. With the recent discovery of a new dwarf galaxy, AND

XXII, in the PAndAS survey, further considerations might need to be taken in order to estimate when and to what degree an interaction happened between M31 and M33. The 26 dwarf galaxy, AND XXII, is located on the west side of M33, as shown in Figure 7. It is possible that AND XXII could be a satellite of M33 instead of M31, because of its proximity to M33. If this is the case, it puts new limits on any interaction that could have occurred between the two Local Group spirals, as any interaction would have to have left such a dwarf as AND XXII still bound to M33, while disrupting the HI and stellar disks

(Martin et al. 2009).

Figure 7: PAndAS plot showing the location of AND XXII, a newly discovered dwarf galaxy, to the south-west of M33. The axes are created such that the center, (0, 0), is centered on M31, and the ticks show the degree distance away from this position. The boxes are the 1x1 degree squares that were observed (Martin et al. 2009) 27

CHAPTER 2: OBSERVATIONS AND DATA REDUCTION

2.1 Observations

Observations of HI 21 cm emission for this project were made with the National

Radio Astronomy Observatory’s (NRAO) 100 meter Robert C. Byrd Green Bank

Telescope. The L-band Gregorian focus receiver with dual linear polarization was used.

The spectra were measured using the GBT Spectrometer with a bandwidth of 12.5 MHz for total velocity coverage of 2637.73 km/s at a resolution of 0.1609 km/s. The angular resolution of the GBT at 1420 MHz is approximately 9.1´. During the course of the observations, two different observing modes were used, pointed observations in track mode and “on-the-fly” mapping in a continuous scan mode. In these two modes over

125,000 spectra were taken from December 2008 through August 2010.

2.1.1 Pointed Observations

We performed pointed observations at two local maxima in the Braun and

Thilker (2004, hereafter referred to as B&T) M31/M33 stream. The local maxima at 01h

20m 29s +37° 22´ 32ʺ and 01h 00m 00s +39° 30´ 00ʺ in right ascension and declination

8 (J2000) both had an HI column density of log(NHI) ~ 17.5 in the B&T map, and shall be hereafter referred to as Braun I and Braun III respectively. Two different observation modes were used: “in-band” frequency switching and position switching. “In-band” frequency switching entails shifting the center frequency of the signal spectrum relative to a rest frequency; this shift is small enough that the spectral line appears in both the rest frequency spectrum and the shifted spectrum. This mode of observation allows the

8 Column density is the amount of HI in an area projected on the sky, or the integral along the line of sight of the spatial density. 28 observer to be continuously on-source, but requires that the system be stable over multiple frequencies, and usually results in poorer instrumental baselines, as compared with position switching. The center frequency was initially offset by -4.5 MHz from the rest frequency of 1420.4058 MHz. With apparent standing waves in the YY polarization resulting in poor baselines, we experimented with adjusting the offset between -4.0 MHz,

-4.5 MHz, and -3.5 MHz to determine the optimum shifting. At a shift of -3.5 MHz, the baselines in both polarizations were no longer being affected; this was the frequency offset used for the majority of the pointed observations.

For the position-switched data, one observation was made “on” the source and another was made “off” of the source, where no source emission would be detected based on the maps by B&T. For Braun I the off position was displaced 30s in right ascension (J2000) from the on position, and for Braun III the displacement was 12m 30s.

The “off” observation was then subtracted from the “on” observation. Observations made in this mode result in better baselines, as the system needs only to be stable over time, but half of the observing time must be spent off source. Three “on/off” pairs each for 20 minutes were made. Both the Braun I and Braun III positions were observed for approximately three hours of total exposure time. These positions are shown as red stars in Figure 8 (Bottom).

2.1.2 Maps

We also observed the M33 region with “on-the-fly” (OTF) mapping using in- band frequency switching; during OTF mapping, data are collected as the telescope continuously moves through a specified region. In contrast with the pointed observations, 29 the majority of the map observations were made at a -4.5 MHz offset, as these observations were made prior to the detection of the standing wave in the spectra. The area of observation was divided into 2°×2° grid regions in Galactic longitude and latitude, which are shown in Figure 8 (Top). Each region was observed in a raster scan, which scans in longitude and then steps in latitude, with 3´ spacing in both coordinates.

Each scan in longitude consisted of 36 six-second integrations, with three seconds at the rest frequency and three seconds at the shifted frequency. Two 2°×2° grids encompassing the Braun I region and another local maximum, hereafter referred to as Braun II, were stepped in right ascension and declination to match the observations of B&T. Regions around the B&T stream were re-observed totaling up to six passes on some of the grids.

Declination

00 Right Ascension Figure 8: Top: An overlay of the mapping grids on the B&T M31/M33 map. The contours within the bridge are drawn at nd . Bottom: An extracted area of the top image. The red stars represent the locations of the two pointed observations, and the blue star represents another local maximum that was observed in the mapping observations.

2.2 Data Reduction

During the data reduction process, there were four main areas of concern: calibration, interference excision, stray radiation correction, and removal of residual instrumental baselines. The antenna temperature scale was established using laboratory values for the receiver noise sources with approximately 2% error. The calibration steps remove most of the instrumental response by subtracting the reference data, either the offset frequency data or the offset position data, from the data taken on source or on frequency. Radio frequency interference (RFI) is not removed in the calibration process, though. Spurious signals, RFI, that occur in a spectrum can be created by lightning, cell phones, digital cameras, microwaves, and other devices that generate signals at the observed frequency. Typically the signals from RFI are short lived and highly polarized.

The effects of RFI may be removed from a data set by removing the affected channels, which correspond to frequency, individual integrations, or an entire scan. RFI may also be removed from specific channels within the data; here the channel affected is replaced with an interpolated value based on the preceding and following channels. We utilized all of the aforementioned methods for RFI excision.

Stray radiation can also alter the data and needed to be addressed. Radio telescopes have some response to all areas of the sky. Radiation from outside the main beam, the beam that is pointed on a source being observed, is referred to as stray radiation. Sidelobes are a type of stray radiation that results from signal spilling over the dish and subreflector. The GBT was designed to minimize the sidelobes by having an unblocked aperture as seen in Figure 9, where the feed arm is set at one edge of the dish. 32

With a well characterized telescope response pattern, areas of stray radiation can be identified and corrected (Blagrave, Lockman, & Martin 2010). At high local standard of rest (LSR) velocities, this step is not critical, but for low velocity gas, such corrections become very important. Once all other corrections had been made, we removed any residual instrumental baselines by fitting and subtracting a low order polynomial to the data.

Figure 9: Image of the Green Bank Telescope (GBT) courtesy of NRAO/AUI.

The data reduction process we used utilized the software packages GBTIDL, the

Astronomical Image Processing System (AIPS, classic version), Python, and CASA, as 33 graphically shown in Figure 10. The final products from this process were a single spectrum for each of the pointed observations, and a data cube relating the brightness temperature with the Galactic longitude and latitude and the velocity in the LSR frame for the mapping data.

Figure 10: Diagram of the data reductions procedure.

2.2.1 GBTIDL

After retrieving the data from the archive, it was processed through GBTIDL. In

GBTIDL calibration was done using getps and getfs for the position-switched and frequency-switched data respectively. This step was also used to limit the size of the mapping data sets, by keeping only the velocities in the range of -600 km/s to 300 km/s

(LSR). Once calibrated, channels in the map containing RFI were identified and their value replaced with an interpolated value from the channel on either side of the one 34 affected. GBTIDL also separated the linear polarizations, with each polarization generating three FITS files9. These files were then corrected for stray radiation.

All of the data reduction of the pointed observations occurred within GBTIDL.

After calibration, the pointed observations at a given local maximum were averaged together, and a fourth order polynomial was fit and subtracted, resulting in a spectrum for

Braun I and Braun III.

2.2.2 IdlToSdfits

The calibrated GBTIDL files were then processed with idlToSdfits10. This process allowed the data to be boxcar smoothed and decimated11 to 3.2 km/s, while converting the files into the required form for input into AIPS.

2.2.3 AIPS

AIPS was used to produce a data cube, which relates the brightness temperature with the Galactic longitude and latitude and the velocity in the LSR frame, from stray- radiation-corrected data. After inputting the data files, a spherical Bessel convolving

function with parameters optimized for GBT HI observations (HPBW = 550,

275, 462, 2, where HPBW is the half power beamwidth

expressed in arc-seconds) was applied using the function SDGRD. Here the pixel or cell size of the map was also set to 105ʺ x 105ʺ. The AIPS function OHGEO was then used to re-grid the equatorial data cube onto the galactic coordinate system. This allowed for all

9 One of the files contained the spectra that were mapped in equatorial coordinates. The other two files are the spectra observed in galactic coordinates; two files were necessary as the idlToSdfits programs can handle a maximum of 100,000 spectra, and this data set exceeded that. 10 IdlToSdfits was developed by Glen Langston of NRAO. 11 The decimate command saves the data for every nth channel as specified by the amount of smoothing. For the data smoothed over twenty channels, every twentieth data point was saved. 35 three data cubes, three for each polarization, to be combined into one data cube using the

COMB function.

2.2.4 Python

The cube produced in AIPS was then corrected for residual instrumental baselines by fitting and subtracting a low-order (fifth-order) polynomial to the spectra. In order to process the large number of spectra in the data cubes, a script was written in

Python by the author to fit and subtract a polynomial from the spectrum in each pixel of the data cube. The script was designed to determine the rms of the spectrum and to find and block the regions of emission before fitting and removing a polynomial from the data. Given the input file, the highest order polynomial that was desired to be fit to the data, and the amount of velocity that was extracted beyond the region of interest for both the positive and negative velocities, where no emission was expected, the script proceeded through an iterative polynomial fitting process. The process began by calculating an rms and determining regions for fitting a second-order polynomial. The rms was calculated from the channels that corresponded to the velocities that were outside of the region of interest, thereby omitting strong emission from the Galaxy and potentially M33, which would skew the mean and rms to high values that preclude the detection of weak emission. The second order polynomial was fit, subtracted, and “peak” detection started. The criterion for a “peak” was that there had to be a 1.5σ detection over at least three consecutive channels. Requiring three adjacent pixels at or above the threshold reduced the effects of random fluctuations. 36

The peak region was then refined. The width of any peak was limited to 300 km/s.

A peak that was found to exceed this value was reset so that a 300 km/s velocity range was centered on the channel with the greatest brightness temperature. Regions with the same channel or overlapping regions were merged; spectra were checked to insure that no peaks were detected within the velocity range outside of the region of interest in the negative velocity range. For a peak found at higher velocities than Galactic emission, if the average of the brightness temperature in the peak channels was equal to or greater than 1.75σ, it was merged with the Galactic emission peak. If it was weaker, then the feature was attributed to noise and was not included in the emission regions. When a peak was found that had outer limits that exceeded the highest channel number, the limit of the peak was set to be the last channel. Lastly, peaks separated by one channel were merged into one peak.

Once the emission regions, or peaks, had been identified, a new rms value was calculated from the emission-free regions. Then these peak regions were blocked from the next polynomial fitting. Channels between peaks separated by 100 km/s or less were also blocked. A third order polynomial was then fit to the unblocked regions, and the process was repeated four more times with the third order polynomial. This process continued with continually increasing orders of polynomials until the order of the polynomial fitting reached the one that was specified in the input. Then the rms values, the baseline-subtracted spectrum, and the map of the blocked regions were saved to output files. The full Python script can be found in the Appendix. 37

2.2.5 CASA

For comparison with the B&T observations of the M31/M33 stream, CASA was used to spatially smooth the data to 48´ before the residual instrumental baseline was removed. Using IMSMOOTH, the data cube was convolved with a two dimensional

Gaussian to produce an angular resolution of 48´. Due to the sinusoidal behavior of the baseline in the milli-Kelvin range around the stream location, the automatic baseline script was inadequate for the polynomial fitting and removal. Therefore, IMCONTSUB was used to fit and subtract a fourth order polynomial only from -533 km/s to -195 km/s.

CHAPTER 3: RESULTS AND DISCUSSIONS

3.1 Analytical Tools and Procedure

Location and identification of the M31/M33 streamer features were determined by visual inspection of the pointed observation spectra, the baseline-subtracted data cube, and the integrated HI column density ( ) map. Spectra from the pointed observations were examined to determine if there were any possible detections, at least 3σ over 15 km/s, which might correspond to the detections from B&T. The baseline-subtracted data cubes were stepped through in velocity from -320 km/s to -190 km/s to determine if any

12 features were detectable based on the brightness temperature ( ). We also checked the spectral profiles of individual pixels, at possible local maxima in the Tb, to verify if there was a detection, and if so the nature of it. Integrated maps were inspected to determine the size of the feature.

From the spectra, either from the pointed observations or from the data cube, the peak temperature ( ), the velocity width (∆V or full width at half maximum (FWHM)), and the center velocity ( ) of detected lines were determined by fitting a Gaussian to the spectra. This was done in GBTIDL for the spectra of the pointed observations, and in

IDL for the spectra obtained from the data cubes. From the data cube, the spatial location of each feature was determined from the location of peak brightness. Using CASA, the

12 Antenna temperature is the temperature of the matched resistor whose power per unit frequency matches that produced by the antenna. It is related to the source brightness temperature (Tb) by Ta = Tb*ηmb + Tstray, where Ta is the antenna temperature that has been corrected for atmospheric attenuation, ηmb is the efficiency of the main beam, and Tstray is the calculated sidelobe contribution (Blagrave, Lockman, & Martin 2010). The correction from Ta to Tb was executed within the stray radiation correction step for the mapping data, and by considering Ta = Tb * 0.88 for the pointed observation data, where 0.88 is the efficiency of the GBT main beam at 21 cm. 39 rms noise, σ, was calculated over a region lacking significant emission at one channel/velocity in the cube. The size of the detected feature was determined by fitting a two dimensional Gaussian in CASA to the integrated maps. The total flux was obtained by summing the integrated map over the region of the Gaussian fit.

To characterize the features, the and HI mass ( ) were calculated. The was calculated using

dv , (1)

where is the brightness temperature in K, dv is the velocity range being integrated over in km/s, is the as found from the Gaussian fits to the spectra in K, and

is the full width at half maximum as found from the Gaussian fit in km/s. was calculated using

ʺ ʺ

D , (2)

where pa is the area of one pixel in the data cube in square arc-seconds, F is the total flux obtained by adding the Tb for the integrated map over the region the Gaussian was fit

13 to, D is the distance from the observer to the source , MH is the mass of one hydrogen atom, and the rest of the values are conversion factors. All the units are noted within the square brackets, where ʺ is an arc-second, and rad is a radian.

13 For all three M31/M33 stream features, the distance to M31, as found in Table 1, was used. 40

3.2 Completeness of Study

3.2.1 Velocity

To limit the size of the data sets, we extracted approximately a 900 km/s velocity range from -600 km/s to 300 km/s for analysis. This range more than covers the systemic velocities of both M33 and M31 in the negative velocity range, and allows us to detect HI features analogous to HVCs in this vicinity. HVCs around the Milky Way and other galaxies have been found to lie within 50 kpc from the center of the galaxy they are associated with, and have been found to deviate from the host galaxy’s systemic velocity by no more than 150 km/s (Braun & Thilker 2004). We are therefore well within the range of detection for either M33 or M31. For comparison, B&T report that the stream velocities are between the systemic velocities of M33 and M31. Again we suitably cover such a velocity range as to detect the predicted stream velocity.

3.2.2 Position

We fully observed a field 52 kpc in radius from the center of M33, and covered up to 30 kpc from the center position of M31 at its distance. From B&T HI features analogous to Galactic HVCs have been detected out to 50 kpc from the host galaxy.

Covering this area around M33, we would be able to detect such features. We do not approach close enough to M31 to detect all such features around it, but we do cover the region of the three B&T M31/M33 local maxima by approximately 1° around each feature; although, in the region of Braun III we do not have the sensitivity to fully distinguish this feature from the noise level. 41

3.2.3 HI Column Density and Mass

B&T detected all three of the local maxima, Braun I, Braun II, and Braun III, with a = 17.5. Around these features, a layer of diffuse gas at was also detected. The 3σ and 5σ limits of the and HI mass for our observations are reported in Table 3. For the limits, we considered to equal 3 σ and 5σ, where σ is the standard deviation observed within each cube, and assumed a FWHM of 15 km/s for both 9.1´ and 48´ angular resolutions. For the HI mass determination, the total flux was taken be the 3σ or 5σ detection, corresponding to the level of HI mass detection, over an area with the size of the GBT beam, 9.1´. With the 48´ convolved beam, the column density that we are sensitive to goes to approximately 16.75, which should detect even the fainter emission.

Table 3

and Sensitivity Limits

Angular Sensitivity

Resolution Level ( ) ( ) ( )

( ´ )

3σ 19.4 18.3 6.4 9.1 5 σ 32.3 18.5 10.7

3 σ 0.5 16.7 0.17 48 5 σ 0.9 16.9 0.29

3.3 M31/M33 Stream

Observing the region of the M31/M33 stream, we were able to detect all three of the local maxima reported by B&T in either one of the angular resolution data cubes or in the pointed observations. Though we do not have the same level of sensitivity for mapping the Braun III region, both from the data cubes and the pointed observations, we find our results consistent with B&T. The characteristics of each feature are summarized in Table 4.

Table 4 A

M31/M33 Stream Features

Source Angular Observation α δ l b

Resolution Method14 (h m s) (° ´ ʺ)

( ´ )

9.1 Pointed (IP) 01 20 29 +37 22 32 129.3 -25.1

Braun I 9.1 Mapping (IM9) 01 20 13 +36 42 47 129.3 -25.8

48 Mapping (IM48) 01 20 56 +37 12 12 129.4 -25.3

9.1 Mapping (IIM9) 01 08 30 +37 46 06 126.7 -25.0 Braun II 48 Mapping (IIM48) 01 08 34 +37 42 26 126.7 -25.0

9.1 Pointed (IIIP) 01 00 00 +39 30 00 124.7 -23.3 Braun III 9.1 Mapping (IIIM9) 00 58 20 +39 30 08 124.4 -23.4

14 The information in the brackets is what each of the different methods for each angular resolution of each source will be referred to as in parts B-D of this table. 43

Table 4 B

M31/M33 Stream Features Continued

Source ∆ σ

(mK) (km/s) (km/s) (mK)

IP 4.0 ± 0.9 17 ± 5 -236 ± 2 0.99 4.0

IM9 60.6 ± 0.9 19.1 ± 0.2 -232.39 ± 0.09 22.5 2.7

IM48 39.7 ± 0.8 21.2 ± 0.3 -232.5 ± 0.1 1.7 23.4

IIM9 96.9 ± 0.8 24.1 ± 0.1 -274.89 ± 0.06 22.50 4.3

IIM48 78.3 ± 0.6 35.8 ± 0.2 -279.85 ± 0.09 1.7 46.1

IIIP 4.0 ± 0.6 30 ± 2 -263 ± 8 1.0 3.8

IIIM9 ------22.5 ----

Table 4 C

M31/M33 Stream Features Continued

Source Major Axis Minor Axis Position Angle

( ´ ) ( ´ ) (E of N) ( ° )

IP ------

IM9 20 ± 1 9.0 ± 0.9 40 ± 3

IM48 68.5 ± 0.7 41.3 ± 0.5 82.4 ± 0.6

IIM9 10.4 ± 0.3 7.7 ± 0.3 127 ± 4

IIM48 99.0 ± 1.0 51.6 ± 0.5 116.4 ± 0.4

IIIP ------

IIIM9 25 ± 2 15 ± 2 60 ± 7

Table 4 D

M31/M33 Stream Features Continued

Source Radius

( ´ ) ( ) ( ) ( )

IP ------1.4 ± 0.5 17.2 ------

IM9 7 ± 2 22.1 ± 0.4 18.3 9.09 ± 0.03

IM48 26.6 ± 0.9 16.2 ± 0.4 18.2 6.64 ± 0.04

IIM9 4.5 ± 0.4 44.8 ± 0.5 18.7 18.42 ± 0.04

IIM48 36 ± 1 53.7 ± 0.6 18.7 22.07 ± 0.05

IIIP ------2 ± 1 17.4 ------

IIIM9 10 ± 3 16.7 ± 0.3 18.2 11.15 ± 0.03

3.3.1 Braun I

Braun I is located to the south-east of M31 and AND XV, approximately half way between the center positions of M31 and M33, and has a velocity that is consistent with what was found by Braun and Thilker and what one would expect for a feature that was tidally stripped from M33 by M31. The pointed observation was offset from the location of the peak brightness from the 9.1´ data cube, but almost exactly at the peak brightness found in the 48´ data cube. From the un-convolved integrated map, one can see what appear to be two denser areas connected by slightly more diffuse gas. In the

48´ cube, these two areas are merged together. This shows that there is far more structure within this feature than previously seen by Braun and Thilker (2004). For both angular resolutions, the found exceeds that previously detected. The spectrum for the pointed observation is shown in Figure 11, and Figure 12 shows the spectra from the two data cubes. The integrated maps for both angular resolutions are shown in Figure 13.

After fitting the feature in the integrated NHI map with a two dimensional Gaussian to determine its extent, we found that the position angle of the fit Gaussian was not aligned in the direction between M33 and M31. The Gaussian was fit over both of the previously mentioned denser regions, which would account for the position angle. Considering each of the two dense regions separately, the resulting position angles were still not aligned with the line between M33 and M31.

0.010

0.005 Antenna Temperature (Ta) (K)

0.000

-0.005

-350 -300 -250 -200 LSR Velocity (km/s) RADIO

Figure 11: Braun I spectra from the pointed observations.

0.05

0.00

-0.05

90 110 130 Channels Channels

0.010

0.005

0.000

-0.005

-0.010

108 112 116 120 Channels

Figure 12: Top: Spectra from the Braun I 9.1´ angular resolution data cube. Bottom: Spectra from the 48´ angular resolution data cube. The axes of the spectra are channel number vs. temperature in Kelvin. The conversion from channels to velocity is v ity nn . 49

Galactic 0.14 Latitude

-24°.6

0.12 -24°.8

0.10 -25°.0

(K) -25°.2 0.08

-25°.4 0.06

-25°.6 0.04

-25°.8

0.02

-26°.0

0 130°.4 130°.0 129°.6 129°.2 128°.8 128°.4 Galactic Longitude Galactic 0.016 Latitude

-24°.5 0.014

0.012 -25°.0

0.010

(K)

-25°.5 -3 8x10

-3 6x10

-26°.0

-3 4x10

-26°.5 2x10-3

130°.5 130°.0 129°.5 129°.0 128°.5 128°.0 127°.5 Galactic Longitude Figure 13: Top: Braun I integrated map at 9.1´. Bottom: Integrated map at 48´. For both images, the colors are the total Tb in each pixel. 50

3.3.2 Braun II

Braun II is located to the south-east of M31 and south of AND XV. It is closer to

M31 at the distance of that galaxy, then to M33 at its distance, and has a velocity that is consistent with what was found by Braun and Thilker and what one would expect for a feature that was tidally stripped from M33 by M31. This feature was found to be much brighter in the 9.1´ cube than previously detected. This indicates that it is more compact and was being diluted in the 48´ beam used by Braun that Thilker (2004), and that there is far more structure within this feature than previously seen. From the 9.1´ map, one can see a small dense region surrounded by a small more diffuse region at a column density of log(NHI) = 17. For both angular resolutions, the found exceeds that previously detected. Figure 14 shows the spectra from the two data cubes. The integrated maps for both angular resolutions are shown in Figure 15. From the two dimensional Gaussian fit to the integrated NHI map of this feature, the position angle was found to be aligned to the line between M33 and M31.

0.15

0.10

0.05

0.00

-0.05

-0.10

90 100 110

Channels

0.01

0.00

-0.01

95 100 105

Channels

Figure 14: Top: Spectra from the Braun II 9.1´ angular resolution data cube. Bottom: Spectra from the 48´ angular resolution data cube. The axes of the spectra are channel number vs. temperature in Kelvin. The conversion from channels to velocity is v ity nn . 52

Galactic 0.45 Latitude

-24°.6

0.40

-24°.8 0.35

0.30 -25°.0

(K) 0.25 -25°.2

0.20

-25°.4 0.15

-25°.6 0.10

0.05 -25°.8

0.00 127°.4 127°.0 126°.6 126°.2 Galactic Longitude Galactic

Latitude 0.016

-23°.5

0.014

-24°.0

0.012

-24°.5 0.010

-3 8 x 10 (K)

-25°.0

-3 6 x 10

-25°.5 -3 4 x 10

-26°.0 2 x 10-3

0.00 128°.0 127°.5 127°.0 126°.5 126°.0 125°.5 Galactic

Longitude Figure 15: Top: Braun II integrated map at 9.1´. Bottom: Integrated map at 48´. For both images, the colors are the total Tb in each pixel.

3.3.3 Braun III

Braun III, located to the south-east of M31, is the closest of the three detected features to M31. It is also to the northwest of AND XV. For both the pointed observation and the mapping, the level of detection of this feature was approximately 3σ. Though not a solid detection for either case, the column density and velocity range are consistent with those observed by B&T. Based on the pointed observation, it appears that this feature, like Braun I and Braun II, may be more compact than previously thought, and that we simply were not observing at the location of peak brightness. Figure 16 shows the spectrum from the pointed observations. Since the level of sensitivity within the cubes, in this region, is such that Braun III remains within the level of the noise, no integrated column density maps are shown.

Antenna Temperature (Ta) (K) 0.005

0.000

- 0.005

0.010

-350 -300 -250 -200

LSR Velocity (km/s) RADIO

Figure 16: Spectra of the Braun III pointed observations.

CHAPTER 4: CONCLUSIONS

In this thesis project we have been able to confirm the existence of the B&T

M31/M33 streamer features. At the HI column density of and 17.5 we have detected the three local maxima previously observed. From the 9.1´ angular resolution observations, we have found that Braun I, Braun II, and most likely Braun III are more compact and contain more structure than previously reported. Though we do see diffuse gas around the peaks of Braun I and Braun II, we do not see the extent of diffuse gas that was expected from B&T. The velocities that we observe also correspond with the findings of B&T and agree with the idea that such clouds, if stripped from M33, would have a velocity between that of the two galaxies. Though we do confirm the stream feature existence, the nature of their origin cannot be conclusively resolved, and several possibilities for their origin are considered in the following sections.

4.1 Milky Way High Velocity Clouds

With the newly discovered extent of the Magellanic Stream in the direction of

M33 and M31, one has to consider the possibility of Galactic origins for the stream features. The largest HVC in the region of the stream features is WC. This HVC has been proposed to be associated with the Magellanic Stream and follows the velocity gradient observed within the MS. We observed and examined this region to ascertain if there existed HI gas in that region of comparable velocities as the stream features. Within the area of WC, no features or structures were detected at velocities between -300 km/s and -

200 km/s. From the work of Nidever et al. (2010) the velocity gradient of the MS in this 56 region is becoming progressively more negative, with velocities around -300 km/s to

-425 km/s. The velocities of the stream features fall outside of this range and suggest that indeed, they are not Galactic HVCs.

4.2 High Velocity Clouds Originating from M31

From the galactic fountain model, one would not expect to see HVCs 50 kpc from a galaxy. Both Braun I and Braun II are roughly 107 kpc from the center of M31, ruling out origin from M31. Braun III is within the 50 kpc radius and is therefore the only of the three that may have originated within M31, but due to its distance of more than 45 kpc, it is highly unlikely. HI clouds analogous to HVCs have also been found to have a velocity deviation of no greater than 150 km/s from the galaxies they are associated with. All three of the stream features fall within, or right at the edge, of this limit. Therefore, Braun

I, Braun II, and Braun III might be HVCs of M31, but their origin would have to be extragalactic, either tidal remnants from a past interaction or the gaseous components of dark matter satellites, as predicted by the Lambda cold dark matter model.

4.3 Association with M31 Dwarf Galaxies

The PAndAS project has discovered many new dwarf galaxies around and associated with M31. Since the nature of the stream features supports possible galactic interaction origins, we had to consider the newly discovered dwarfs in the vicinity of the features. AND I and AND XV, which is the closer of the two, are the two closest of the dwarf galaxies of M31 to the stream features. The position of the features relative to

AND XV and M31 supports other origins outside of the dwarf galaxies. Braun I and

Braun II lie on the side of AND XV opposite to M31, and though Braun III is more in the 57 direction of M31, it too lies more along the line between the M31 stellar arm directed toward M33 and the component from M33 toward M31. Therefore, it is highly unlikely that any of the features have origins from one of these dwarf galaxies.

4.4 M31/M33 HI Stream

The observed features from this study have velocities that are consistent with the findings of B&T, and with the origin of the features being tidally stripped from M33. If the stream features were stripped from M33 by M31, it would be expected that the velocity of the features should be between the systemic velocities of the two galaxies.

There is such a velocity match for the stream features. When the WC region was observed for detection of any features within the velocity range of Braun I, Braun II, and

Braun III, maps of M33 were also made. In agreement with the findings of Grossi et al.

(2008), HVCs around M33 where found with velocities around -200 km/s in the direction of M31. As shown in Figure 17, there are several HVCs with a velocity between -180 km/s to -320 km/s on the north-west side of the galaxy, toward M31. Interestingly, the velocities of the M33 HVCs are more negative on the side toward M31, at about the same velocity as the Braun clouds, and Grossi et al. (2008) suggested that these could be the denser elements of the B&T M31/M33 HI stream. The similar nature between these

HVCs and the stream features give weight to the idea that Braun I, II, and III are indeed part of a stream of HI that was stripped off of M33 by a recent interaction with M31.

Figure 17: HVC detections by Grossi et al. (2008). The HVCs and the HI extent of M33 are overlaid on an optical image of the galaxy. The red and blue contours are for the approaching and receding sides of the disk, respectively, and the color scale for the velocity of the clouds is given by the legend in the upper left corner.

4.5 Future Considerations

In order to determine the exact origins of the M31/M33 stream features, there are several further steps that can be taken. If Braun I, II, and III originate from M33, they should have similar metallicities to the gas in M33. If the metallicities were very low, as compared with what is seen within galaxies, then it may suggest that these are primordial

HVCs that formed early with dark matter halos and have not been affected by star formation. Therefore, by knowing the metallicities of the features, one can begin to set more concrete constraints on the nature of their origin. 59

Increasing the sensitivity would also increase our understanding in the field around the stream features. If there is more diffuse emission within the area between M33 and M31 as B&T suggest, then by increasing the sensitivity that we have at 9.1´, not only would we be able to confirm the existence of the more diffuse gas, but also see if there is any structure within it. In the Braun III region, increasing the sensitivity would allow us to characterize Braun III better, and to see if there is a consistent change in the velocity of the features along their length. 60

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APPENDIX: AUTOMATIC RESIDUAL INSTRUMENTAL BASELINE REMOVAL

PYTHON SCRIPT

# Annotations are preceded by the pound sign and in italics.

#Import pre-built modules that will be accessed during the script. import pyfits import numpy from pylab import * import numpy.ma as ma def autoBase(filein, nfit, excessLeft, excessRight): '''Determines and removes a baseline for a data cube, given the input file (a data cube), the order of polynomial needed, and the extra velocity on each end of the cube (velocity outside of region of interest).'''

# Reading information from the data cube and the file header. This is used to set the limits for the programs execution (the channels that is goes over) and calculates the channel range that corresponds to each specified velocity quantity in the input. header = pyfits.getheader(filein) data = pyfits.getdata(filein) v = arange(data.shape[1]) nch, b, l = len(v), 0, 0 c = 2.997E5 deltafreq = header['cdelt3'] restfreq = header['restfreq'] velPerChan = abs(deltafreq*c/restfreq) chanMin = int(floor(excessLeft/velPerChan)) chanMax = nch-int(floor(excessRight/velPerChan)) bMax = data.shape[2] lMax = data.shape[3]

# Setup the output arrays. This is what will eventually be output for the Mask and Rms "cubes". maskout = zeros([1, len(v), bMax, lMax], float) rmsout = zeros([bMax, lMax], float)

# Loops that will step through each b, l pixel (x, y) from (0, 0) through (bMax, lMax). for b in range(bMax): for l in range(lMax):

# Retrieval of the spectrum for each pixel and initializing the Tmask1 such that all elements in the array are set to False (will be considered in polynomial fitting). Due to the nature of the files, the relevant information is the first element of the array. All other elements are empty. T = data[:, :, b,l] T1 = T[0] Tmask1 = ma.array(T1, mask=False) 64

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# Test loop that ensures that the pixel has a spectrum associated with it and is not an array of NaN (Not a Number). if abs(sum(T1)) >= 0:

# Initial calculation of the rms value and initial masking. The rms is set to be calculated from the channels after the highest relevant velocity channel. A mask is made for the spectrum, such that true values will be considered invalid data and will not be considered in the fitting of the polynomial. Originally the masked region is the entire region of interest, channels between the excess velocities as given in the input. rmslist1 = [] for i in range(chanMax): rmslist1.append(T1[chanMax-i]) rms = std(rmslist1) vmask = ma.masked_inside(v, chanMin, chanMax) maskTmask = Tmask1.mask + vmask.mask Tmask = ma.array(T1, mask = maskTmask) vmask2 = vmask vmaskTest = vmask

# Loop that will step through the polynomial order until the order specified in the input is reached. For all polynomial orders higher than 2, there is an iterative process that will repeat the same polynomial fitting five times. for n in range(2, nfit+1): if n == 2: repeat = 1 else: repeat = 5 for r in range(repeat):

# Main loop that will run as long as the last two polynomial fittings did not return the exact same baselined spectrum. In practice, this has never activated, but it has been left in as an attempt to reduce the length required to run the script. if vmaskTest.mask.all() != True:

# The polynomial is fit to the spectrum. By compressing the array, only valid data points are going to be considered, not masked ones. T2 is the polynomial subtracted spectrum. poly_coef = numpy.polyfit(vmask2.compressed(), Tmask.compressed(), n) p = numpy.poly1d(poly_coef) T2 = T1-p(v)

# Determining the area of emission. This is accomplished by searching for "peaks" in the T2 spectrum. All channels that are followed by three channels with antenna temperatures greater that mrms (1.5 * rms) , but which are not themselves greater that mrms, are added to the empty array

Appendix

for peak values (peakrange). Channels also not greater that mrms, but proceeded by three channels that are, are added to the peak array. peakrange = [] for t in range(1, nch-1): if t <= nch-1: mrms = 1.5*rms if T2[t] >= mrms and T2[t-1] < mrms and t+3 <= nch-1 and T2[t+1] >= mrms and T2[t+2] >= mrms: if mean(T2[t:t+3]) > 3*rms: peakrange.append(t-1) else: peakrange.append(t-2) elif T2[t] < mrms and t-4 >= 0 and T2[t-1] >= mrms and T2[t-2] >= mrms and T2[t-3] >= mrms: if mean(T2[t-3:t]) > 3*rms: peakrange.append(t) else: peakrange.append(t+1) else: break

# Set the first channel in the peak array to the first channel (0) if it had been set to (1). This is done because in the previous peak detection loop, the first channel has to be excluded from consideration as python can not check a negative channel. if len(peakrange) >= 2 and peakrange[0] == 1: peakrange[0] = 0 else: pass

# Limits the peaks to a width of 300 km/s. Any peak that is found to exceed 300 km/s is reset so that it extends 150 km/s on both side of the channel with the greatest antenna temperature. This was done to ensure that initially false peak detections can be removed as no feature, or combination of two features should ever exceed 300 km/s. As this is an iterative process, later some peaks will be merged into a singular peak due to their proximity. maxGal = abs(300/velPerChan) if len(peakrange) >=2 and mean(T2[peakrange[len(peakrange)- 2]:peakrange[len(peakrange)-1]]) >= 15*rms and peakrange[len(peakrange)-1]- peakrange[len(peakrange)-2] >= maxGal: tooWide = [] for i in range(peakrange[len(peakrange)-1]-peakrange[len(peakrange)- 2]): tooWide.append(T2[len(peakrange)-2+i]) maxp = peakrange[len(peakrange)-2]+tooWide.index(max(tooWide)) if maxp-(maxGal/2) <= 0: peakrange[len(peakrange)-2] = 0 else: peakrange[len(peakrange)-2] = maxp-(maxGal/2) if maxp+(maxGal/2) >= len(nch+1): peakrange[len(peakrange)-1] = len(nch+1) else: peakrange[len(peakrange)-1] = maxp+(maxGal/2) 66

Appendix

elif len(peakrange) >=4 and mean(T2[peakrange[len(peakrange)- 4]:peakrange[len(peakrange)-3]]) >= 15*rms and peakrange[len(peakrange)-3]- peakrange[len(peakrange)-4] >= maxGal: tooWide = [] for i in range(peakrange[len(peakrange)-3]-peakrange[len(peakrange)- 4]): tooWide.append(T2[len(peakrange)-4+i]) maxp = peakrange[len(peakrange)-4]+tooWide.index(max(tooWide)) if maxp-(maxGal/2) <= 0: peakrange[len(peakrange)-4] = 0 else: peakrange[len(peakrange)-4] = maxp-(maxGal/2) if maxp+(maxGal/2) >= len(nch+1): peakrange[len(peakrange)-3] = len(nch+1) else: peakrange[len(peakrange)-3] = maxp+(maxGal/2)

# Repeated numbers are removed from peakrange by combining any peaks that had the common end point. pr = [] for i in range(len(peakrange)): if i+1 <= len(peakrange)-1 and i-1 >= 0: if peakrange[i] != peakrange[i+1] and peakrange[i] != peakrange[i- 1]: pr.append(peakrange[i]) elif peakrange[i] == peakrange[i+1]: pass elif peakrange[i] == peakrange[i-1]: pass elif i-1 < 0: pr.append(peakrange[0]) elif i+1 > len(peakrange)-1: pr.append(peakrange[i]) peakrange = [] peakrange = pr

# Overlap in peakrange in removed by combining peaks with overlapping regions into one peak. pr = [] for i in range(len(peakrange)): lenpr = len(pr)-1 if i+1 <= len(peakrange)-1 and i-1 >= 0: if i==1: pr.append(peakrange[1]) elif i % 2 == 0: if peakrange[i] < pr[lenpr]: pr[lenpr] = peakrange[i+1] else: pr.append(peakrange[i]) pr.append(peakrange[i+1]) elif i-1 < 0: pr.append(peakrange[0]) elif i+1 > len(peakrange)-1 and pr[lenpr] != peakrange[i]: pr.append(peakrange[i]) peakrange = [] 67

Appendix

peakrange = pr

# Excess amounts of the negative part of the spectrum is ensured not to be being counted as part of peak by limiting the peak range values to the region of interest, before chanMin. pr = [] for i in range(len(peakrange)): if i % 2 != 0 and peakrange[i] < chanMin: pass elif i % 2 != 0: pr.append(peakrange[i-1]) pr.append(peakrange[i]) peakrange = [] peakrange = pr

# Mask peaks on positive side Galactic peak if the peak is determined not to be noise. This is determined on the criteria that the peak has an antenna temperature greater than 1.7*rms. if len(peakrange) >= 2 and mean(T2[peakrange[len(peakrange)- 2]:peakrange[len(peakrange)-1]]) < 15*rms: if len(peakrange) >= 4 and mean(T2[peakrange[len(peakrange)- 4]:peakrange[len(peakrange)-1]]) < 1.7*rms: pr = [] for i in range(0, len(peakrange)-1): pr.append(peakrange[i]) peakrange = [] peakrange = pr else: pass else: pass

# Outer edge of the last peak is set to last channel if not otherwise specified. if len(peakrange) % 2 != 0: peakrange.append(nch-1) else: pass

# Masks channel separated by one channel; merges two such peaks into one peak. if len(peakrange) >= 4: pr = [] pr.append(peakrange[0]) pr.append(peakrange[1]) for i in range(2, len(peakrange)): lenpr = len(pr)-1 if i % 2 == 0 and peakrange[i]-peakrange[i-1] == 1 and pr[lenpr] == peakrange[i-1]: pr[lenpr] = peakrange[i+1] 68

Appendix

elif i % 2 == 0 and peakrange[i]-peakrange[i-1] != 1: pr.append(peakrange[i]) pr.append(peakrange[i+1]) peakrange = [] peakrange = pr else: pass

# New rms is calculated from the regions with no peak detection. This is done by setting the mask channels within the peak detection channels to True and compressing the array; thereby again excluding channels with a True value from the calculation. rms0 = ma.array(T2, mask = False) rmslist2 = rms0 for w in range(len(peakrange)/2): rmslist2.mask[peakrange[2*w]:peakrange[2*w+1]+1] = True rms = std(rmslist2.compressed())

# Reset vmask2.mask to False so that a new mask can be made from the newly found peaks. vmask2.mask[0:nch-1] = False

# Masking peaks; setting peak detection mask channels to True. for w in range(len(peakrange)/2): vmask2.mask[peakrange[2*w]:peakrange[2*w+1]+1] = True

# Mask peaks within 100 km/s of each other to ensure that wings or connections between peaks, that are not themselves at peak level, are not considered for polynomial fitting. This is check for spectra with 2, 3, or 4 peaks. No more than 3 peaks were ever the final peak count for any spectrum in this study. velMax = 100/velPerChan if chanMin == 0: pass else: for i in range(len(peakrange)/2): if len(peakrange) >= 4 and i % 2 != 0 and i+1 % 2 == 0 and peakrange[i+1]-peakrange[i] <= velMax: if i != len(peakrange)-2 and mean(T2[peakrange[i]:peakrange[i+2]]) >= 15*rms: vmask2.mask[peakrange[i]:peakrange[i+2]] = True else: pass if T2[peakrange[i]] < 0 and T2[peakrange[i]+1] < 0: vmask2.mask[peakrange[i]] = False elif T2[peakrange[i]] < 0 and T2[peakrange[i]-1] < 0: vmask2.mask[peakrange[i]] = False elif len(peakrange) >= 6 and i % 2 == 0 and i-1 % 2 != 0 and peakrange[i+3]-peakrange[i] <= 35: 69

Appendix

if i != len(peakrange)-4 and mean(T2[peakrange[i]:peakrange[i+4]]) >= 15*rms: vmask2.mask[peakrange[i]:peakrange[i+4]] = True else: pass if T2[peakrange[i]] < 0 and T2[peakrange[i]+1] < 0: vmask2.mask[peakrange[i]] = False elif T2[peakrange[i]] < 0 and T2[peakrange[i]-1] < 0: vmask2.mask[peakrange[i]] = False elif len(peakrange) >= 8 and i % 2 == 0 and i-1 % 2 != 0 and peakrange[i+5]- peakrange[i] <= 35: if i != len(peakrange)-6 and mean(T2[peakrange[i]:peakrange[i+6]]) >= 15*rms: vmask2.mask[peakrange[i]:peakrange[i+6]] = True else: pass if T2[peakrange[i]] < 0 and T2[peakrange[i]+1] < 0: vmask2.mask[peakrange[i]] = False elif T2[peakrange[i]] < 0 and T2[peakrange[i]-1] < 0: vmask2.mask[peakrange[i]] = False

# Masks the regions from channel 0 to chanMin and chanMax to the last channel if no peaks were detected, and sets up the test array to determine when to passes through the main loop yield the exact same baselined spectrum. if chanMin == 0: pass else: if len(peakrange) == 0: vmask2.mask[0:chanMin] = False else: vmask2.mask[0: peakrange[0]] = False vmask2.mask[chanMax:nch] = False vmaskTest = (vmask2 == vmask) mTmask = Tmask1.mask + vmask2.mask Tmask = ma.array(T1, mask = mTmask) else: pass

# After completing the main loop for all polynomial orders and iterations, the values that are to be output are set. data[:, :, b, l] = array(T2) maskout[:, :, b, l] = vmask2.mask rmsout[b, l] = rms else: pass

# Prints the b pixel value that was just completed so that the progress of the script can be monitored. print b

# Output files are created. 70

Appendix

pyfits.writeto(filein[:-10] + 'TestBase.cube.fits', data, header) pyfits.writeto(filein[:-10] + 'TestMask.cube.fits', maskout, header) pyfits.writeto(filein[:-10] + 'TestRms.cube.fits', rmsout, header)