Leiden Observatory. University of Leiden FACULTY OF MATHEMATICS AND NATURAL SCIENCES. Saskia van den Broek

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1 Leiden Observatory University of Leiden FACULTY OF MATHEMATICS AND NATURAL SCIENCES Constraining the carriers and formation mechanisms of the infrared XCN-ice feature Saskia van den Broek Supervised by: Prof. Dr. Ewine van Dishoeck & Karin Öberg Minor research thesis October 8, 2008

2 Cover image: The ρ Ophiucus molecular cloud. A significant part of the sources used in this report is located in this cloud. Image credit: NASA/JPL-Caltech/Harvard-Smithsonian CfA

3 Contents 1 Introduction Molecular clouds Interstellar ices The XCN-feature This report Sample 5 3 Methods Derivation of column densities CH 3 OH & NH Statistical analysis Molecule distributions Environmental parameters Abundances: Principal Component Analysis Abundances: correlation plots Results Derivation of column densities Column densities Comparison with laboratory spectra Statistical Analysis Molecule distributions Environmental parameters Principal component analysis Correlation plots Discussion Overview of results Constraints on OCN formation Conclusions and future work Conclusions Future work A All correlation plots 57 i

4 ii CONTENTS

5 List of Figures 3.1 Spitzer spectra of several ρ Oph sources between 5.3 and 13.9 µm Spectra of ρ Oph sources between 8 and 10.5 µm Spectra between 8 and 10.5 µm after removal of the local continuum The robustness of peak position and FWHM when changing the wavelength regions chosen for fixing the local continuum Fits of laboratory spectra to the observed spectrum of IRS Fits of laboratory spectra to the observed spectrum of IRS Distribution of the abundances of molecules on a logarithmic scale Distribution of the abundances of molecules on a logarithmic scale. Continued Correlations of the abundances of OCN and CH 3 OH with the CO temperature tracer Correlations of the abundances of OCN and CH 3 OH with the CO 2 temperature tracer Correlations of the abundances of OCN and CH 3 OH with the column density of H 2 O Radial map of ices in the Oph-F core PCA plots of the first versus the second principal component PCA plots of the first versus the third principal component PCA plots of the second versus the third principal component Correlations of OCN with NH Correlations of OCN with CO 2 and its components Correlations of OCN with C1, C3 and C Correlation of OCN with HCOOH Correlations of OCN with CO and its components Correlation of OCN with CH 3 OH Correlation of NH + 4 with NH Correlations of NH + 4 with C2, C3, C4 and C Correlations of NH 3 3 with C Significant correlations with CH 3 OH Correlations of HCOOH with CO and C Correlation of NH 3 with CH A.1 The correlations between the column densities of all molecules and the column density of H 2 O iii

6 iv LIST OF FIGURES A.2 The correlations between the column densities of all molecules and the column density of H 2 O. Continued A.3 The correlations of the column densities between all molecules A.4 The correlations of the column densities between all molecules. Continued. 61 A.5 The correlations of the column densities between all molecules. Continued. 62 A.6 The correlations of the column densities between all molecules. Continued. 63 A.7 The correlations of the column densities between all molecules. Continued. 64 A.8 The correlations of the column densities between all molecules. Continued. 65 A.9 The correlations of the column densities between all molecules. Continued. 66 A.10 The correlations of the column densities between all molecules. Continued. 67 A.11 The correlations of the column densities between all molecules. Continued. 68 A.12 The correlations of the column densities between all molecules. Continued. 69 A.13 The correlations of the column densities between all molecules. Continued. 70 A.14 The correlations of the column densities between all molecules. Continued. 71 A.15 The correlations between the abundances of all molecules and the column density of H 2 O A.16 The correlations between the abundances of all molecules and the column density of H 2 O. Continued A.17 The correlations of the abundances between all molecules A.18 The correlations of the abundances between all molecules. Continued A.19 The correlations of the abundances between all molecules. Continued A.20 The correlations of the abundances between all molecules. Continued A.21 The correlations of the abundances between all molecules. Continued A.22 The correlations of the abundances between all molecules. Continued A.23 The correlations of the abundances between all molecules. Continued A.24 The correlations of the abundances between all molecules. Continued A.25 The correlations of the abundances between all molecules. Continued A.26 The correlations of the abundances between all molecules. Continued A.27 The correlations of the abundances between all molecules. Continued A.28 The correlations of the abundances between all molecules. Continued A.29 Distribution of the abundances of molecules on a linear scale A.30 Distribution of the abundances of molecules on a linear scale. Continued.. 87

7 List of Tables 2.1 Band strengths used in this report and those by Gibb et al. (2004) Three sets of wavelength regimes for making the local continuum fit Column densities, abundances and FWHM of NH 3 and CH 3 OH Ice abundances with respect to solid H 2 O of the source sample Ice abundances of the CO 2 components Ice abundances of the CO components Optical depths of the components of the 5 8 µm complex Percentage of sources within 0.5, 1.0, and 2.0 medians from the center for each molecule v

8 vi LIST OF TABLES

9 Chapter 1 Introduction 1.1 Molecular clouds The interstellar medium is filled with a very dilute gas with average density of 1 particle per cm 3. This gas is however not distributed uniformly over space, but rather clumped together. These clumps form clouds of several types, each with their own physical and chemical properties. The densest type of cloud is the molecular cloud which is also the site for star and planet formation. Molecular clouds have densities of particles per cm 3 and temperatures of K. As the name suggests, these clouds are dominated by molecules of which H 2 is the most abundant. Among the molecules that have been observed and securely identified are solid H 2 O, solid CO 2 (Pontoppidan et al., 2008), solid CO (Pontoppidan, 2004), and solid CH 4 (Öberg et al., 2008) and a wide variety of gas phase molecules includes CO (see Mitchell et al. (1988) for a summary). Despite the very low temperatures and densities compared to earth (T 293 K, n cm 3 ), a surprisingly rich chemistry takes place inside these clouds. This astrochemistry plays a vital role in the physical and chemical evolution during star formation. The conditions in molecular clouds allow for star formation. The relatively high densities and low temperatures result in that self-gravity overcomes pressure forces such as gas pressure, magnetic fields and turbulence. The criterion for a cloud to collapse under its own gravity is that its mass M c be greater than the Jeans mass M J, M c > M J. The Jeans mass scales with density n and temperature T as M J T 3/2. Therefore, molecular clouds have the n 1/2 right conditions to support star formation. When a cloud collapses both the temperature and density increase. The Jeans mass, however, increases as well since it depends more strongly on temperature. In order to keep the cloud collapsing and start the formation of protostar, cooling is necessary. Molecules play a key role in this process. Molecules can be thermally excited into higher rotational levels and subsequently make a transition downward while emitting photons. This radiation carries away energy and thus cools the cloud. This cooling mechanism is very efficient and it is a crucial ingredient in the star formation process. Specific types of radiation can destroy certain molecules through photodissociation and enhance the formation of other molecules by providing the energy necessary to overcome reaction barriers. As a consequence, the strength of internal and external radiation fields can be derived from the abundances of the affected molecules. Furthermore, from the line profile of absorption or emission features the kinematics of the molecular cloud can be deduced. 1

10 2 CHAPTER 1. INTRODUCTION 1.2 Interstellar ices In dense star forming cores up to 90% of the chemistry takes place in the icy mantles of dust grains. Atoms freeze out on the grain, meet other atoms with which they can react and form new molecules. These can then evaporate again into the gas-phase. Ices hence act as catalysts and alter the chemical composition of the clouds significantly. Freeze-out on dust grains and evaporation into the gas-phase are controlled by the ambient temperature and radiation fields. Therefore, the abundances of molecules in interstellar ices provide information on these physical conditions. One of the key questions in astrochemistry is the chemical composition of the interstellar ices. This composition traces the chemical and physical history of molecular clouds and their evolution. Furthermore, the ices may become incorporated in comets and finally act as a reservoir of molecules for planets. Simple neutral molecules such as H 2 O, CO, CO 2, CH 4 and CH 3 OH can form through freeze-out on dust grains and subsequent hydrogenation and oxygenation on the grain surface. These molecules have been positively identified towards low and high mass stars and extincted background stars whose lines of sight trace quiescent dense clouds. The identification and formation pathways of other molecules and ions such as NH + 4, OCN and HCOO are less certain (Grim et al., 1989; Allamandola et al., 1999; Novozamsky et al., 2001; Schutte & Khanna, 2003). 1.3 The XCN-feature The 4.62 µm feature, generally assigned to OCN, was detected in the spectrum of W33A by Soifer et al. (1979). Many different formation pathways have been proposed. Moore et al. (1983) reproduced the interstellar feature in laboratory studies by proton irradiation of interstellar ice analogs composed of H 2 O, CO and NH 3. Lacy et al. (1984) reproduced the feature by Vacuum ultraviolet (VUV) photoprocessing of CO:NH 3 ice. Later, laboratory experiments revealed that also acid-base chemistry of HNCO:NH 3 ices is a possible formation pathway (Demyk et al., 1998; Novozamsky et al., 2001; Raunier, 2003a,b). van Broekhuizen et al. (2004) doubt the efficiency of UV-photolysis since it requires high UV fluence and high initial NH 3 abundances. They claim that the most favourable formation pathway is through thermal processing of HNCO-containing ices, even at temperatures as low as 15 K. At the moment, the following formation paths are considered most likely. The fist is the photoprocessing of H 2 O:CO:NH 3 ice mixtures. The second pathway involves the photoprocessing or thermal heating of HNCO containing ices. van Broekhuizen et al. (2004) propose the following reactions: HNCO + NH 3 OCN + NH + 4 HNCO + H 2 O OCN + H 3 O + A possible formation path for HNCO is analogous to one of the formation paths of CO 2. In this case CO reacts with a nitrogen atom rather than with an oxygen atom and subsequently with a hydrogen atom. The assignment of the 4.62 µm feature to OCN in space is still controversial, however, and many other carriers have been proposed (see Pendleton et al. (1999) for a summary).

11 1.4. THIS REPORT 3 Constraining the carriers and formation pathways of observed interstellar features is crucial for understanding ice chemistry. Ions play an important role in this because they are good tracers of ice chemistry and the local environment. OCN in particular is an interesting ion since it is a prebiotic molecule. For example, urea (H 2 NCONH 2 ) is formed after UV irradiation of a NH + 4 OCN salt (Raunier et al., 2004). 1.4 This report In this report, we concentrate on constraining the carriers and formation mechanisms of especially OCN. Since NH + 4 is the expected counterion of OCN, we also investigate this ion more closely. In addition, we will give some attention to NH 3, because it is the progenitor of NH + 4, and CH 3OH, because together with OCN, it is one of the most variable ice molecules. We will use the following statistical analyses to reach our goal. We compare the abundance distribution of OCN with the abundance distributions of other identified and unidentified ice features. In this way we can get an overview of which features are formed quiescently and which features depend on time or environment. In particular we can find to which of these two groups OCN belongs. By plotting the abundance of OCN versus potential temperature tracers and versus the H 2 O ice column density we can investigate respectively dependencies on temperature and total envelope mass. For comparison, we will also do this for CH 3 OH, which is known to be a highly variable ice feature. We use a statistical method called Principal Component Analysis (PCA) to find out if OCN groups together with other molecules, and if so, with which molecules. In this way we can ensure we do not miss any unexpected and interesting correlations or anticorrelations. We can also use this analysis to verify that we are not dominated by a specific cloud. We make correlation plots of (lack of) correlations that appear in the PCA plots and of other expected correlations involving OCN. We will also have a closer look at correlations involving the only other ion NH + 4, its progenitor NH 3, and the highly variable molecule CH 3 OH for comparison reasons. This study requires a large sample of ice abundances towards a variety of protostellar and interstellar environments. To achieve this we use overlapping ice surveys of low mass YSOs together with some surveys of high mass YSOs and background stars for comparison. For the low mass YSOs we use VLT surveys for the abundances of OCN and CO (Pontoppidan et al., 2003) and surveys in context of the Spitzer Legacy Program From Molecular Cores to Planet-Forming Disks ( c2d ; Evans et al. 2003) for the abundances of the other molecules. Additional data for the high mass objects are taken from an ice survey of high mass stars conducted by Gibb et al. (2004). Most of the data has already been analysed for ice abundances, but for sources in the ρ Ophiucus cloud CH 3 OH and NH 3 abundances are missing. Since NH 3 is the progenitor of NH + 4, the only other known ion, and CH 3OH is one of the most variable molecules, these abundances are needed for the statistical analysis. Hence we start off with the derivation of these abundances for nine sources in the ρ Ophiucus cloud from newly released Spitzer data.

12 4 CHAPTER 1. INTRODUCTION The outline of the report is as follows. In chapter 2 we introduce the source sample and the molecules that are included in the analysis. In chapter 3 we describe the methods we have used in this report. In 3.1 we describe the derivation of the column densities of NH 3 and CH 3 OH for a few sources in the sample. The statistical methods mentioned above for finding correlations between molecules and the dependencies on environment and thermal processing are discussed in 3.2. We show the results in chapter 4 and discuss their consequences in chapter 5. Finally, in chapter 6 we draw conclusions from the results of the previous chapters and comment on future work.

13 Chapter 2 Sample The sample consists of 51 low mass Young Stellar Objects (YSOs). We have also included eight high mass YSOs and two background sources for comparison. In this report we treat the high mass YSOs and background sources together in the same sample. The conclusions do not change when treating them separately. The core of our sample is the same as used by Boogert et al. (2008). In their sample, however, sources in the ρ Ophiucus cloud were absent, because the spectra were not available at the time the paper was written. These are now available in the Spitzer archive with a wavelength coverage of 5-35 µm and resolutions of R 90 and R 600. Therefore, our sample is extended with the sources in the ρ Ophiucus molecular cloud in the sample of Pontoppidan et al. (2008). For each source we have taken the abundances of eight molecules OCN (van Broekhuizen et al., 2005), NH 3 (this work; S. Bottinelli, priv. comm.), CH 3 OH (Boogert et al., 2008; this work), CO 2 (Pontoppidan et al., 2008), HCOOH, NH + 4 (Boogert et al., 2008), CH 4 (Öberg et al., 2008) and CO (Pontoppidan, 2004; Pontoppidan et al., 2008) and the column density of H 2 O (Pontoppidan et al., 2008; Boogert et al., 2008) from the literature where available. We have determined the column densities and abundances of CH 3 OH and NH 3 ourselves for nine sources in the ρ Ophiucus molecular cloud (see 3.1 for details). To calculate the abundances with respect to H 2 O we use the column densities of H 2 O derived by Pontoppidan et al. (2008). Abundances from the literature are adapted to these values for the column density of H 2 O. In addition to the molecules mentioned above we also include the abundances of the components of CO 2 (Pontoppidan et al., 2008) and CO (Pontoppidan, 2004; Pontoppidan et al., 2008) and the optical depths of the 5 8 µm complex (Boogert et al., 2008). The components of CO 2 and CO provide information on the type of ices these molecules are in. We include the 5 8 µm complex, since ions may contribute to some of these components. Abundances for high mass sources that are missing in above mentioned papers are taken from Gibb et al. (2004). Gibb et al. use different band strengths for OCN, CH 3 OH and the 4.27 µm CO 2 feature than the above papers. We have corrected for this by scaling the abundances of Gibb et al. to the same band strengths, so we can compare all abundances. In Table 2.1 we have listed the band strengths used in this report and those used by Gibb et al. In our sample there are for OCN 6 detections and 14 upper limits for low mass sources and 4 detections and 5 upper limits for high mass sources. For NH + 4 we have 39 detections for low mass sources and all 10 sources in the high mass sample. NH 3 has 13 detections and 6 upper limits for low mass YSOs and 4 detections and 5 upper limits for high mass YSOs. Finally, for CH 3 OH we find 14 detections and 34 upper limits for low mass YSOs and 5

14 6 CHAPTER 2. SAMPLE Table 2.1: Band strengths used in this report and those by Gibb et al. (2004). Dots in the Gibb column indicate that we did not need to use their band strengths in this report. Molecule A (10 17 cm molecule 1 ) This report Gibb et al. (2004) OCN 13 a 5 b NH c 1.3 c CH 3 OH 1.6 c... CO d 1.1 d HCOOH 0.26 e... NH f... CH g 0.73 h CO 1.1 d 1.1 d a van Broekhuizen et al. (2004). b Schutte & Greenberg (1997). c Kerkhof et al. (1999). d Gerakines et al. (1995). e Boogert et al. (2008). f Schutte & Khanna (2003). g Boogert et al. (1997), CH 4 in a H 2 O rich ice. h Boogert et al. (1997), pure CH 4. 5 detections and 5 upper limits for high mass YSOs.

15 Chapter 3 Methods 3.1 Derivation of column densities CH 3 OH & NH 3 The column densities for most molecules and sources have been taken from the literature. The methods used can be found in the corresponding papers. We determine the column densities of CH 3 OH and NH 3 for nine sources in the ρ Ophiucus cloud ourselves. The method used for the derivation of CH 3 OH and NH 3 is similar to Boogert et al. (2008) and Bottinelli et al. (2007). We have used an IDL-routine written by Bottinelli et al. and adapted and extended it for our specific needs. The nine sources for which we derive the column densities of CH 3 OH and NH 3 are all in the ρ Ophiucus molecular cloud. In Figure 3.1 the Spitzer spectra between 5.3 and 13.9 µm of these sources are shown. Since the CH 3 OH and NH 3 features are at 9.0 and 9.7 µm respectively we will focus on the µm region. In Figure 3.2 we plot the spectra in the µm region. In order to remove the underlying silicate feature we perform a 4 th order polynomial fit to the spectra to obtain a baseline for the local continuum. We have to make sure that we fit to parts of the spectrum that are not contaminated by other features than the silicate band. Three possible sets of points for defining the continuum are tabulated in Table 3.1. Throughout the paper we will use Set 1 to perform the local continuum fit for all sources. We will use Set 2 and 3 only to test the robustness of the derived peak positions and FWHM. The red curves in Figure 3.2 indicate the wavelength regimes used for fitting the local continuum, which are shown as the dotted lines. We can now determine the absorption features of CH 3 OH and NH 3 from the deviations of the spectra from the local continuum. The abundance of molecule X is given by [X] = N(X) 100% (3.1) N(H 2 O) with N(X) the column density of X and N(H 2 O) the column density of H 2 O. The column density is given by N(X) = A 1 τdν. (3.2) A is the integrated band strength determined through laboratory spectroscopy given by 7

16 8 CHAPTER 3. METHODS Figure 3.1: Spitzer spectra between 5.3 and 13.9 µm of the new sources in ρ Ophiucus not included in Boogert et al. (2008).

17 3.1. DERIVATION OF COLUMN DENSITIES CH 3 OH & NH 3 9 Figure 3.2: Spectra of the new sources between 8 and 10.5 µm. The fitted local continuum is indicated by the dotted blue line. The wavelength regimes used for fitting the local continuum are indicated by the red parts of the spectrum.

18 10 CHAPTER 3. METHODS Table 3.1: Three sets of wavelength regimes for making the local continuum fit. Wavelength regimes (µm) Set 1 Set 2 Set A = N is the total amount of molecules and τ the optical depth given by τdν N. (3.3) τ = ln I λ(0) I λ (s), (3.4) with I λ (0) the intensity according to the local continuum fit and I λ (s) the measured intensity from the spectrum. The spectra after removal of the local continuum are shown in figure 3.3. In case of a clear (3σ) detection, we perform a Gaussian fit to the feature (the red line in the plots). Using Equations 3.2 and 3.3 we determine the column density where we have used a band strength of A(NH 3 ) = cm molecule 1 for NH 3 (Kerkhof et al., 1999) and A(CH 3 OH) = cm molecule 1 for CH 3 OH (Kerkhof et al., 1999). Using Eq. 3.1 and the column densities of H 2 O determined by Pontoppidan et al. (2008) we calculate the abundances of the molecules with respect to solid H 2 O. We also derive the peak positions and FWHM from the Gaussian fit. In cases where there is no clear detection, we determine upper limits by assuming a Gaussian form for the feature with a FWHM equal to the average FWHM of the clear detections and a depth corresponding to a 3σ detection. To test the robustness of the local continuum fit we compare the derived peak positions and FWHM using different sets of points for the local continuum fit (see Table 3.1). The results are shown in Figure 3.4. The plots show that the uncertainties in the peak positions and FWHM of CH 3 OH are all smaller than 2 cm 1, which is comparable to the statistical uncertainties from the Gaussian fit. The uncertainties in the FWHM of NH 3 are 5 cm 1, which is somewhat larger than the statistical uncertainties from the Gaussian fit. 3.2 Statistical analysis Molecule distributions We can investigate the dependence on environment by looking at the spread in the abundances. We determine this spread by making histograms of the abundances. Since we have only 10 sources in the high mass sample, we perform this analysis only for the low mass YSOs. We have chosen to subdivide the histograms in 10 bins to ensure that we have enough sources per bin. In order to be able to compare the various molecules easily, we normalize the distributions. We choose to normalize with respect to the median rather than to the mean,

19 3.2. STATISTICAL ANALYSIS 11 Figure 3.3: Spectra for the sources between 8 and 10.5 µm after removal of the local continuum. The red line indicates a Gaussian fit to the features. The dotted blue line indicates the local continuum. The dotted vertical lines indicate the locations of the NH 3 and CH 3 OH features.

20 Figure 3.4: The robustness of peak position and FWHM when changing the wavelength regions chosen for fixing the local continuum. 12 CHAPTER 3. METHODS

21 3.2. STATISTICAL ANALYSIS 13 so that outliers have only a limited influence on the results. We make plots for both linearly distributed and logarithmically distributed bins. Molecules whose formation processes are sensitive to local environmental effects such as temperature or irradiation fields should form in very different amounts from source to source according to the prevailing environmental conditions. On the other hand, quiescent formation should result in similar amounts in each source. Hence, molecules with a quiescent formation process should have narrow distributions around the median while molecules with environmental dependent formation processes should have a broad distribution Environmental parameters Temperature tracers In this research we use two temperature tracers to look for temperature dependences. We use the temperature tracers proposed by Pontoppidan et al. (2003) (see also Pontoppidan et al. 2004, Tielens et al. 1991): N(pure CO) N(CO:H 2 O) which is in indicator for temperatures of T < 20 K. According to Pontoppidan the higher this ratio the higher the fraction of the ice with temperatures lower than 20 K. N(pure CO 2 ) N(CO 2 :H 2 O) which indicates temperatures higher than T 20 K. This ratio indicates what fraction of the ice has temperatures above 20 K. There are two ways of forming a pure CO 2 component. The first is segregation of CO 2 out of a mixture with H 2 O and possibly CH 3 OH, producing pure CO 2 inclusions in the ice matrix. This happens at temperatures T 60 K. The second formation path is through distillation of a CO 2 :CO ice mixture. At temperatures of T K CO desorbs, leaving a pure CO 2 ice behind. We make scatterplots of all molecules versus these temperature tracers and compute the Pearson correlation coefficients R for the high mass sample, the low mass sample and the entire sample. Throughout the report we use half the value of the upper limit to calculate the Pearson correlation coefficient if we have upper limits rather than detections. The results do not change significantly when assigning values between 0.5 and 1.0 to the upper limits. In the low mass case we have for each correlation at least 19 ice observations for each species in our sample, so that a correlation is significant at the 95% level or better if R 0.39 for 17 degrees of freedom. In the high mass case we have almost always 9 ice observations in our sample, so that a correlation is significant at the 95% level if R 0.58 for 7 degrees of freedom. Envelope mass Whittet et al. (2001, Figure 5) found that the H 2 O column density follows the envelope mass. We can hence test the dependence on envelope mass by making scatterplots of the abundances of all molecules versus the H 2 O column density. We compute the Pearson correlation coefficient in the same way as we did for the temperature tracers.

22 14 CHAPTER 3. METHODS Abundances: Principal Component Analysis PCA is a method, based on linear algebra, that can reveal the underlying structure of large data sets by reducing the dimension of the data set. This is done by projecting down the data on principal components, i.e. a change of basis or coordinate system. The first principal component is determined by the best fit line or direction of maximum variation to the, multidimensional data set. The second principal component is then determined as the next best fit and is orthogonal to the first component. Continuing in this way, all principal components can be determined. In this way the principal components are also ordered according to the amount of information they contain. For more information on the background of PCA one can find many introductory tutorials on the web 1 or see Shlens (2005) for a more thorough treatment. In the specific case at hand, we have a 20-dimensional data set, where each dimension stands for a molecule or a molecule in a specific environment. We therefore have a coordinate system where each axis represents a specific molecule. In this coordinate system all sources can be assigned a place according to their abundances. Using PCA we can reduce this 20-dimensional space to a lower-dimensional space which is much easier to interpret. This reduction of dimensionality is possible because the 20 dimensions or molecules we start with are not independent. Molecules that form under the same conditions or have similar formation paths will co-vary and hence depend on each other. In contrast, the principal components are independent. By only looking at the first few principal components, which contain the majority of the information, the reduction in dimensionality is achieved. Furthermore, the PCA analysis can also be used to see if the clouds are different and if the sources follow any patterns. For this analysis we subdivide the complete data set in a set containing only the low mass YSOs and a set containing only the high mass YSOs. As can be seen from Tables we have an incomplete data set. In order to perform the PCA these missing data need to be filled in. This has to be done in a way that influences the results as little as possible. We do this by setting the missing abundances equal to the mean of the abundances of the corresponding molecule. In this way it does not contribute to the variance from which the principal components are derived. We apply the IDL-routine PCA to both data sets. This routine scales the input vectors (the abundances in this report) to a mean of zero and a variance of one. We decide to use the first three principal components to unravel the underlying structure of the data sets. For the low mass YSO data set 55% of the variance in the data is explained by the first three principal components, for the high mass YSO data set this is 79%. We make two types of plots. The first type, called the score plot, is essentially the projection of the sources on the principal components, which now make up the new coordinate system. Sources that lie close together in the plot have similar properties regarding their molecular content. The second type, called the loading plot, shows how strongly the several molecules contribute to a principal component. Hence, molecules that are closely related to each other and thus contribute a similar amount to a principal component will lie close together in this type of plot. 1 e.g. MVA intro/c/1

23 3.2. STATISTICAL ANALYSIS Abundances: correlation plots Apart from the PCA we also perform an analysis of the correlations between molecules themselves to quantify strengths of correlations indicated from the PCA. Furthermore, by using the first few principal components, a significant amount of information is missing. This can cause isolated but significant correlations not to show up in the PCA analysis while they do in the correlation plots. Finally, a close relationship in the space of a set of principal components may appear, while there is no correlation in the correlation analysis. This can be caused by the fact that there can still be a significant amount of variation in the other directions. If the apparent correlation does not show up in the correlation plots, then it will probably not be real and we can reject this apparent correlation. We visualize the correlations by making scatterplots. Because these scatterplots are generally difficult to judge by eye, and to be quantitative, we compute the Pearson correlation coefficient for all correlations. We make two types of correlations, between the column densities of all molecules, and between the abundances. Column density correlations are influenced by the total amount of ice present in the analysed line of sight. This is the reason for making correlations involving abundances. Since we have divided by the column density of H 2 O, abundance correlations are not influenced by the total amount of ice, i.e. we do not find higher abundance correlations just because there is more ice. For this reason, correlations in abundances are more robust than correlations in column densities. On the other hand, a lack of correlation in column density is more compelling than a lack of correlation in abundances, because then it does not even depend on the total amount of ice and so the two molecules are really unrelated. For each of these types we compute the Pearson correlation coefficient for both the high mass sample and the low mass sample, as well as for the entire sample.

24 16 CHAPTER 3. METHODS

25 Chapter 4 Results 4.1 Derivation of column densities Column densities The column densities, abundances and FWHM derived for the new sources in ρ Ophiucus according to the method described in 3.1 are summarized in Table 4.1. For NH 3 we find 5 detections and 4 upper limits, while for CH 3 OH we find 4 detections and 5 upper limits. The abundances of NH 3 lie in the range of %. The 3σ upper limits on the abundances of NH 3 fall in this range as well. The abundances of CH 3 OH are in the range %, with 3σ upper limits up to 4.5%. Bottinelli et al. (2007) find abundances of NH 3 in the range 2 7 % and abundances of CH 3 OH in the range 3 27 %. Therefore, our results for NH 3 agree excellently. The results for CH 3 OH agree well, although our range is somewhat smaller than that of Bottinelli et al.. Since we use the same method as Bottinelli et al. for the derivation of NH 3 and CH 3 OH there will be no systematical offsets between the sources in their sample and our ρ Ophiucus sources. A summary of all abundances used in this work is given in Tables Table 4.1: Column densities, abundances and FWHM of NH 3 and CH 3 OH. If no error is given for the FWHM, the stated FWHM is the average FWHM from the clear detections used to calculate upper limits for the column density and abundance (see 3.1). Source N(H 2O) a N(NH 3) [NH 3] FWHM NH 3 N(CH 3OH) [CH 3OH] FWHM CH 3OH cm cm 2 % cm cm 2 % cm 1 Elias ± 2.6 <0.93 < ± ± ± 3.6 IRS ± 2.0 <0.42 < ± ± ± 3.1 IRS ± ± ± ± 1.6 <0.56 < IRS ± ± ± ± 2.8 <0.53 < IRS ± ± ± ± ± ± ± 1.9 IRS ± ± ± ± 5.2 <0.37 < VSSG ± 2.5 <0.53 < ± ± ± 10.7 WL ± 3.0 <0.84 < <1.00 < WL ± ± ± ± 2.7 <0.89 < a Pontoppidan et al

26 18 CHAPTER 4. RESULTS Table 4.2: Ice abundances with respect to solid H2O of the source sample. Source N(H2O) a [OCN ] c [NH3] d [CH3OH] b [CO2] a [HCOOH] b [NH + ]b [CH4] f [CO] g cm 2 % % % % % 4 ]b % % % ρ Ophiucus IRS ± 2.0 <0.24 < ± 2.6 d 23.0 ± ± ± 3.0 IRS ± 4.0 < ± ± ± 6.6 IRS ± 4.0 < ± 0.9 <1.6 d 20.4 ± ± ± 2.7 IRS ± 2.0 < ± 2.1 <4.1 d 18.4 e ± ± 7.1 IRS ± ± ± ± 2.9 d 42.2 ± ± 26.9 IRS ± 3.0 < ± 2.5 <1.8 d 33.5 ± ± ± 13.7 WL ± <3.8 <4.5 d 19.6 ± ±5.9 WL ± ± 0.9 <2.1 d 22.4 ± ± 7.3 Elias ± 2.6 <0.33 < ± 4.4 d 27.2 ± ± 16.8 VSSG ± < ± 4.0 d 34.5 ± ± ± 40.7 CRBR ± 5.0 < < ± 3.1 < ± ± 16.8 RNO ± ± < ± 4.9 < ± ± ± 3.9 GSS 30 IRS ± ± ± ± 4.8 Elias ± 3.0 b <0.08 h <8.3 h < ± 4.3 h < ± 1.5 <2.6 h 5.6 ± 1.7 h Corona Australis RCRA IRS ± ± ± ± 3.4 < ± ± ± 8.3 RCRA IRS 7A ± 19.2 < < ± 4.0 < ± ± 3.9 RCRA IRS 7B ± 19.7 < ± ± 5.5 < ± ± ± 6.0 HH ± ± ± 1.3 e ± ± ± 3.7 a IRAS ± < ± 20.4 < ± ± HH ± 2.4 b 0.33 ± 0.05 <12.2 h < ± 5.9 h < ± ± 1.8 h 46.1 ± 5.7 Serpens EC ± 0.7 < < ± 15.5 < ± <58.7 SVS ± 11.3 < ± 3.2 e 25.2 ± ± ± ± ± ± 20.7 EC ± ± ± ± 3.7 < ± ± 7.7 EC ± ± 5.3 e < ± 6.4 < ± <59.0 a EC ± ± ± 5.3 < ± ± Taurus L ± 2.8 < < ± 2.4 < ± ± ± 1.9 DG Tau B 26.3 ± < ± 2.5 < ± ± 4.3 a IRAS ± 2.2 b ± 1.1 e < < ± HH ± 2.5 b < < ± Chamaeleon IRAS ± < ± 5.0 < ± ±

27 4.1. DERIVATION OF COLUMN DENSITIES 19 Table 4.2 Continued. Source N(H2O) a [OCN ] c [NH3] d [CH3OH] b [CO2] a [HCOOH] b [NH + ]b [CH4] f [CO] g cm 2 % % % % % 4 ]b % % % Perseus L1448 IRS ± < ± 25.4 < ± ± 52.9 a L1455 SMM ± < ± ± ± RNO ± < ± 4.3 < ± ± 11.7 a IRAS ± ± 1.8 e < ± 2.5 < ± ± ± 3.1 a IRAS ± ± 3.6 e < ± 6.1 < ± <21.9 a B1-a ± < ± 5.9 < ± <39.3 a B1-b ± 32.0 b ± ± ± ± B1-c ± < ± ± ± ± IRAS ± < ± 3.9 < ± ± 10.7 a IRAS ± < ± 4.9 < ± ± 12.8 a IRAS ± 22.6 b ± 1.7 e 4.2 ± < ± ± IRAS ± 56.5 b < < ± ± L1455 IRS ± 3.7 b < < ± IRAS ± 0.3 b < < ± BHR92 IRAS ± < ± 5.1 < ± Lupus IRAS ± ± 3.3 e 10.3 ± ± ± ± ± CB244 IRAS ± < ± 5.5 < ± B59 2MASSJ ± 32.0 a < ± ± ± IRAS ± 1.3 b ± < ± SSTc2dJ ± 28.2 a < ± ± L1014 L1014 IRS 71.6 ± 9.1 b ± ± ± ± High mass YSOs W33A ± ± ± 9.7 h 16.3 ± ± ± ± ± 1.1 h 12.6 ± 5.1 GL ± ± 0.11 <2.3 h 8.2 ± ± ± ± ± 0.8 h 7.6 ± 2.6 a GL ± ± ± 1.7 h 3.0 ± ± 1.6 < ± ± 2.4 h 22.5 ± 3.1 a W3 IRS ± 6.0 <0.08 h <5.1 h < ± 1.8 < ± 1.3 <1.2 h 5.7 ± 2.1 a S ± 1.9 <0.28 h <3.5 h < ± 2.5 < ± ± 0.51 h 5.3 ± 2.3 a NGC 7538 IRS ± 6.4 <0.72 h 16.4 ± 5.1 h 7.5 ± ± 3.0 < ± ± 0.36 h 4.0 ± 1.7 a GL 7009S ± 22.6 b 0.72 ± ± 13.0 h 31.3 ± ± ± 2.1 <5.9 h 15.1 ± 4.1 MonR2 IRS ± 1.5 b... <3.1 h < ± 3.0 h < ± 5.2 <23.6 h...

28 20 CHAPTER 4. RESULTS Table 4.2 Continued. Source N(H2O) a [OCN ] c [NH3] d [CH3OH] b [CO2] a [HCOOH] b [NH + ]b [CH4] f [CO] g cm 2 % % % % % 4 ]b % % % Background sources CK ± 2.5 < < ± 4.3 < ± ± 20.6 Elias ± 1.6 b <0.48 h <10.0 h < ± 53.5 h < ± ± 3.6 h a Pontoppidan et al. (2008) b Boogert et al. (2008) c van Broekhuizen et al. (2005) d this report, 3.1 and e Bottinelli, S., private communication. f Öberg et al. (2008) g Pontoppidan et al. (2003) h Gibb et al. (2004)

29 4.1. DERIVATION OF COLUMN DENSITIES 21 Table 4.3: Ice abundances a of the CO 2 components Source [CO 2 :H 2 O] [CO 2 :CO] [pure CO 2 ] [shoulder CO 2 ] % % % % ρ Ophiucus IRS ± ± ± ± 0.34 IRS ± ± ± ± 0.27 IRS ± ± ± ± 0.17 IRS ± ± ± ± 0.87 IRS ± ± ± ± 0.45 IRS ± ± ± ± 0.43 WL ± ± ± ± 0.40 WL ± ± ± ±0.24 Elias ± ± ± ± 0.22 VSSG ± ± ± ± 0.48 CRBR ± ± ± ± 0.23 RNO ± ± ± ± 0.29 GSS 30 IRS ± ± ± ± 0.55 Elias Corona Australis RCRA IRS ± ± ± ± 0.20 RCRA IRS 7A 12.1 ± ± ± ± 0.11 RCRA IRS 7B 17.3 ± ± ± 1.1 b 0.33 ± 0.11 HH ± ± ± ± 0.08 IRAS ± ± ± ± 1.8 HH Serpens EC ± ± ± ± 2.0 SVS ± ± ± ± 0.23 EC ± ± ± ± 0.24 EC ± ± ± ± 0.47 EC ± ± ± ± 0.28 Taurus L ± ± ± ± 0.16 DG Tau B 13.8 ± ± ± ± 0.35 IRAS HH Chamaeleon IRAS ± ± ± ± 0.32 Perseus L1448 IRS ± ± ± ± 1.3 L1455 SMM ± ± ± ± 0.19 RNO ± ± ± ± 0.81 IRAS ± ± ± ± 0.16 IRAS ± ± ± ± 0.22 B1-a 13.7 ± ± ± ± 0.11

30 22 CHAPTER 4. RESULTS Table 4.3 Continued. Source [CO 2 :H 2 O] [CO 2 :CO] [pure CO 2 ] [shoulder CO 2 ] % % % % B1-b B1-c 23.0 ± ± ± ± 0.25 IRAS ± ± ± ± 0.27 IRAS ± ± ± ± 0.37 IRAS IRAS L1455 IRS IRAS BHR92 IRAS ± ± ± ± 0.09 Lupus IRAS ± ± ± ± 0.22 CB244 IRAS ± ± ± ± 0.07 B59 2MASSJ IRAS SSTc2dJ L1014 L1014 IRS High mass YSOs W33A 8.8 ± ± ± ± 0.55 GL ± ± ± ± 0.65 GL ± ± ± ± 0.15 W3 IRS ± ± ± ± 0.10 S ± ± ± ± 1.0 NGC 7538 IRS ± ± ± ± 1.8 GL 7009S MonR2 IRS Background sources CK ± ± ± ± 0.13 Elias a All abundances by Pontoppidan et al. (2008).

31 4.1. DERIVATION OF COLUMN DENSITIES 23 Table 4.4: Ice abundances a of the CO components Source [CO:H 2 O] [pure CO] [CO:CO 2 ] % % % ρ Ophiucus IRS ± ± ± 0.24 IRS ± ± ± 0.29 IRS ± ± ± 0.35 IRS ± ± ± 1.8 IRS ± ± ± 1.1 IRS ± ± ± 1.1 WL ± ± ± 0.71 WL ± ± ± 0.70 Elias ± ± ± 0.90 VSSG ± ± ± 6.7 CRBR ± ± ± 0.58 RNO ± ± ± 0.48 GSS 30 IRS1 5.7 ± ± 3.3 <0.71 Elias Corona Australis RCRA IRS ± ± ± 0.62 RCRA IRS 7A 5.2 ± ± ± 0.59 RCRA IRS 7B 10.2 ± ± ± 0.60 HH 46 b 14.9 ± ± ± 0.25 IRAS HH ± ± ± 0.33 Serpens EC 82 < ± ± 5.0 SVS ± ± ± 1.1 EC ± ± ± 0.89 EC 74 b < ± ± 2.4 EC Taurus L ± ± ± 0.33 DG Tau B b 7.2 ± ± ± 0.70 IRAS HH Chamaeleon IRAS Perseus L1448 IRS 1 b 31.0 ± ± ± 5.3 L1455 SMM RNO 15 b 25.3 ± ± ± 1.6 IRAS b 4.3 ± ± ± 0.45 IRAS b < ± 6.7 <1.8 B1-a b < ± 19.4 <0.79

32 24 CHAPTER 4. RESULTS Table 4.4 Continued. Source [CO:H 2 O] [pure CO] [CO:CO 2 ] % % % B1-b B1-c IRAS b 13.0 ± ± ± 1.4 IRAS b 19.3 ± ± ± 1.3 IRAS IRAS L1455 IRS IRAS BHR92 IRAS Lupus IRAS CB244 IRAS B59 2MASSJ IRAS SSTc2dJ L1014 L1014 IRS High mass YSOs W33A 9.7 ± ± 1.6 <0.12 GL 2136 b 6.2 ± ± 0.77 <0.29 GL 989 b 11.9 ± ± ± 0.71 W3 IRS 5 b 2.8 ± ± ± 0.30 S140 b 3.8 ± ± 0.75 <0.42 NGC 7538 IRS 9 b 11.8 ± ± ± 0.28 GL 7009S 11.5 ± ± ± 0.20 MonR2 IRS Background sources CK ± ± ± 1.2 Elias a All abundances by Pontoppidan (2004), unless stated otherwise. b Pontoppidan et al. (2008).

33 4.1. DERIVATION OF COLUMN DENSITIES 25 Table 4.5: Optical depths a of the components of the 5 8 µm complex Source τ C1 τ C2 τ C3 τ C4 τ C5 ρ Ophiucus IRS IRS IRS IRS IRS IRS WL WL Elias VSSG CRBR ± ± ± ± ± 0.02 RNO ± ± ± ± ± 0.02 GSS 30 IRS Elias ± ± ± ± ± 0.02 Corona Australis RCRA IRS ± ± ± ± ± 0.01 RCRA IRS 7A 0.08 ± ± ± ± ± 0.04 RCRA IRS 7B 0.19 ± ± ± ± ± 0.04 HH ± ± ± ± ± 0.02 IRAS HH ± ± ± ± ± 0.02 Serpens EC ± ± ± ± ± 0.01 SVS ± ± ± ± ± 0.03 EC ± ± ± ± ± 0.01 EC ± ± ± ± ± 0.01 EC ± ± ± ± ± 0.01 Taurus L ± ± ± ± ± 0.01 DG Tau B 0.07 ± ± ± ± ± 0.01 IRAS ± ± ± ± ± 0.01 HH ± ± ± ± ± 0.01 Chamaeleon IRAS ± ± ± ± ± 0.02 Perseus L1448 IRS ± ± ± ± ± 0.01 L1455 SMM ± ± ± ± ± 0.07 RNO ± ± ± ± ± 0.01 IRAS ± ± ± ± ± 0.01 IRAS ± ± ± ± ± 0.04 B1-a 0.33 ± ± ± ± ± 0.05