Organic chemistry around young high-mass stars Allen, Veronica Amber
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high-mass stars
Observational and Theoretical
PhD thesis
to obtain the degree of PhD at the University of Groningen
on the authority of the Rector Magnificus Prof. E. Sterken
and in accordance with the decision by the College of Deans.
This thesis will be defended in public on Friday 12 October 2018 at 9:00 hours
by
Veronica Amber Allen
born on 13 March 1986
in Omaha, Nebraska, United States
Prof. P. D. Barthel
Assessment committee
Prof. E. F. van Dishoeck
Prof. I. E. E. Kamp
Prof. P. Caselli
And to all who follow their dreams even
though the road is long and winding.
This research makes use of data collected at the Atacama Large (sub)Millimeter Array (ALMA), the IRAM NOrthern Extended Mil- limeter Array (NOEMA), and the IRAM 30m telescope. ALMA is a partnership of ESO (representing its member states), NSF (USA) and NINS (Japan), together with NRC (Canada), MOST and ASIAA (Tai- wan), and KASI (Republic of Korea), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by ESO, AUI/NRAO and NAOJ. IRAM is supported by INSU/CNRS (France), MPG (Ger- many) and IGN (Spain).
Cover design: Nebula by Alexi Elconin, Molecules by Nick Oberg Printed by: Gildeprint
ISBN: 978-94-034-1004-3
ISBN: 978-94-034-1003-6 (electronic version)
1 Introduction 1
1.1 Star formation . . . . 3
1.1.1 The first stages . . . . 3
1.1.2 Low-mass star formation . . . . 4
1.1.3 High-mass star formation . . . . 6
1.1.4 Circumstellar disks . . . . 9
1.1.5 Hot Cores . . . . 9
1.2 Astrochemistry . . . . 10
1.2.1 A multidisciplinary field . . . . 10
1.2.2 Complex organic molecules . . . . 12
1.2.3 Gas and grain chemistry . . . . 12
1.2.4 Chemical modeling . . . . 16
1.3 Observational astrochemistry . . . . 18
1.3.1 Observing with sub-millimeter telescopes . . . . 18
1.3.2 Moment maps . . . . 20
1.3.3 Spectral modeling . . . . 20
1.4 Goals of this thesis . . . . 21
1.5 Outline . . . . 21
2 Chemical segregation in hot cores with disk candidates 23 2.1 Introduction . . . . 25
2.2 Observations and methods . . . . 27
2.2.1 Observations . . . . 27
2.2.2 Line identification process . . . . 29
2.2.3 Image analysis . . . . 32
2.3 Results and analysis . . . . 35
2.3.1 Line detections . . . . 35
i
2.3.4 Molecular column densities . . . . 43
2.4 Discussion . . . . 51
2.4.1 Overall chemical composition . . . . 51
2.4.2 Chemical segregation in G35.20 . . . . 53
2.4.3 HNCO and Formamide co-spatial emission . . . . . 57
2.4.4 Deuteration . . . . 57
2.4.5 Comparison to other hot cores . . . . 60
2.5 Conclusions . . . . 61
Appendices 63 2.A Properties of detected lines . . . . 63
2.B Line properties per core (organized by species) . . . . 76
2.C XCLASS fit errors . . . . 99
2.D XCLASS analysis details . . . 106
2.D.1 S-bearing . . . 106
2.D.2 O-bearing organics . . . 106
2.D.3 N-bearing organics . . . 108
2.D.4 H-, N-, and O-bearing organics . . . 109
2.D.5 Vibrationally excited transitions . . . 109
2.D.6 Isotopologues and deuteration . . . 111
2.E Line ID xclass fits . . . 111
3 Complex cyanides as chemical clocks in hot cores 133 3.1 Introduction . . . 135
3.2 Chemical model . . . 137
3.2.1 Model setup . . . 137
3.2.2 Initial conditions . . . 140
3.2.3 Modeling approach . . . 142
3.3 Results . . . 142
3.3.1 Fiducial model . . . 145
3.3.2 Reaction-diffusion competition excluded . . . 147
3.3.3 Varying the initial temperature . . . 148
3.3.4 Continuing with constant high temperature gas- phase chemistry . . . 148
3.3.5 HCN as an initial ice species . . . 151
3.3.6 Varying the cosmic-ray ionization rate . . . 151
3.4.1 General test differences . . . 156
3.4.2 Reproducing source B3 . . . 157
3.4.3 Warm-up times . . . 158
3.5 Conclusions . . . 158
Appendices 161 3.A Comparison with Garrod et al. (2008) . . . 161
3.B Initial conditions . . . 162
3.C Abundance ranges with errors . . . 163
3.D Time ranges . . . 163
4 Mechanical properties of the molecular outflows from the high-mass disk candidates G35.20-0.74 and G35.03+0.35173 4.1 Introduction . . . 175
4.2 Observations . . . 177
4.3 Results . . . 179
4.3.1 HCO + maps . . . 179
4.3.2 SiO maps . . . 179
4.3.3 H 13 CO + emission . . . 179
4.4 Outflow properties . . . 182
4.4.1 Methodology . . . 182
4.4.2 Results . . . 183
4.5 Discussion . . . 185
4.5.1 The nature of the G35.20 outflows . . . 185
4.5.2 G35.03 . . . 186
4.5.3 Outflow Properties . . . 187
4.6 Conclusions . . . 188
Appendices 189 4.A Comparing Outflow Properties with Wu et al. (2004) . . . 189
4.B Channel Maps . . . 193
5 An observational experiment to determine the precursor of interstellar formamide 197 5.1 Introduction . . . 199
5.2 Observations and Method . . . 201
iii
5.2.3 Line identification . . . 203
5.3 Comparison of formamide emission to possible precursors 206 5.3.1 Comparison of spatial distribution . . . 209
5.3.2 Comparison of the velocity field . . . 213
5.3.3 Comparison of the velocity dispersion . . . 217
5.3.4 Comparision of column densities and excitation temperatures . . . 221
5.4 Discussion . . . 224
5.4.1 Overall map trends . . . 224
5.4.2 XCLASS analysis . . . 226
5.5 Conclusions . . . 227
Appendices 229 5.A XCLASS results with errors . . . 229
5.B XCLASS fits . . . 229
5.C Histograms . . . 246
6 Conclusions and Outlook 259 6.1 Summary and conclusions . . . 259
6.2 Future Outlook . . . 261
7 Additional sections 263 7.1 English Summary . . . 263
7.2 Nederlandse samenvatting . . . 272
7.3 Acknowledgements . . . 281
1
Introduction
Galaxies are made up of more than just stars, they also contain gas and dust in clouds with a variety of densities. The coldest, densest, and darkest of these clouds are composed mostly of molecular hydrogen (H 2 ). In these molecular clouds, the formation of new stars takes place.
Depending on a number of factors, including gas temperature (typically 10-100 K), density (> 10 4 atoms/cm 3 ), interstellar radiation field (ISRF – from G 0 =1 locally to 10 5 in Orion), and extinction (A V >2), we find that the chemical composition of these clouds and their substructures can be considerably different. The interdisciplinary sciences of astro- chemistry and astrobiology have experienced a surge of interest in recent years. This is due, in part, to the detection of a multitude of extra- solar planets (see the NASA Exoplanet archive – Akeson et al. (2013)) which could harbor life and to the discovery of living organisms in un- expected parts of the Earth – such as the deep oceans (Gerringer et al.
2017) or extremely acidic (Johnson & Schippers 2017) or alkaline (Grant et al. 1990) environments. Studying the chemical processes involved in producing organic molecules in space, especially in star-forming regions where these raw materials can be delivered to young planets, will lead to a better understanding of how life itself can begin. The study of chem- istry in space is also important to chemists as it probes conditions that are not possible on Earth. In this work, we treat astrochemistry and star formation both as tools and subjects – using astrochemical models to in- terpret star-formation observations, and using observations to address the chemistry near star-forming regions.
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1
Figure 1.1: The cycle of gas in galaxies. Diffuse gas clouds become dense molecular clouds, which fragment and collapse into protostars. Protostars gather mass through their accretion disks and will eventually form a main-sequence star, possibly with a planetary system. At the end of the star’s life, it loses mass to the interstellar medium where diffuse clouds are formed from which the cycle begins again. Image by Bill Saxton (NRAO/AUI/NSF).
In Figure 1.1 we see how the gas in galaxies is recycled and is chem-
ically enriched through the life cycles of stars. The cycle begins with
a diffuse atomic gas cloud (typical hydrogen densities: n H ∼100-500
atoms cm −3 ) becoming a dense molecular cloud (n H > 10 4 atoms/cm 3 ),
where stars begin to form. The protostar gathers mass facilitated by an
1
accretion disk of gas and dust until it reaches the main sequence and begins fusion. The circumstellar disk can become a planetary system.
Throughout the star and planet formation process, molecular species are forming, which could lead to prebiotic molecules being delivered to a planet and kickstarting life. At the end of a star’s life, it sheds its outer layers, returning its chemical complexity to the interstellar medium and eventually to diffuse clouds from which new stars form. The cycle can then begin again, driving the evolution of galaxies.
1.1 Star formation
1.1.1 The first stages
The earliest stages of star formation begin within molecular clouds.
These objects tend to be tens of parsecs in size with gas densities of 10 3 − 10 6 cm −3 and at cold temperatures (10-50 K). Most recently, Her- schel Space Observatory observations showed that these tend to be com- posed of elongated structures, earning them the name “filaments” (André 2017 and references within). Filaments typically have many stars forming within them that have not had a chance to interact with their surround- ings and are therefore good locations to find pre-stellar cores (PSCs), the phase during which the collapsing core is gravitationally bound but has not yet formed a central hydrostatic object.
Important constituents of giant molecular clouds are Infrared Dark Clouds (IRDCs), discovered in the late 1990s (Perault et al. 1996). These are very cold (<25 K) filamentary structures that are optically thick at mid-IR wavelengths due to their high column densities (N col , gas density in a column along the line of sight) of 10 23 - 10 25 cm −2 . They are several parsecs in length and it is thought that these clumps (self- gravitating structures that will fragment into clusters Tan (2017)) house numerous pre-stellar cores. Figure 1.2 shows the Snake nebula G11.11- 0.12, a commonly studied IRDC. Wang et al. (2014) found that in two bright regions (P1 and P6) there were multiple stars forming on small scales. Shipman et al. (2014) found there were several objects within the dark regions of the Snake nebula, including a more evolved protostar with signs of an outflow and a younger source that is possibly a high-mass pre-stellar core.
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1
Figure 1.2: Top: Spitzer (GLIMPSE/MIPS) image of the Snake nebula, where the dark region is the IRDC. Bottom: Herschel (HiGal) image of the cold dust associ- ated with the Snake. P1 and P6 are star forming regions within the Snake nebula containing several young high- and low-mass stars. Images by Ke Wang (ESO).
1.1.2 Low-mass star formation
Low-mass (M< 2 M ) star formation has been fairly well understood for over 30 years based on the ideal isolated case presented by Shu et al.
(1987). Figure 1.3 summarizes this process. A rotating stellar core is
formed when part of a molecular cloud exceeds the mass where gravita-
tional potential energy is balanced by the kinetic energy from the internal
thermal pressure, magnetic fields, and turbulence. This is known as the
1
Figure 1.3: Top: The stages of low-mass star formation. a) Dense cores form within dark molecular clouds. b) Large-scale gravitational collapse c) A protostar forms within an envelope of infalling material with a accretion disk and bipolar outflow. d) Mass accretion slows and the star becomes a T Tauri star – young low-mass pre-main sequence stars known for their variability (presumably due to accretion). e) Accretion stops and a pre-main-sequence star and circumstellar disk forming planets continues to evolve. f) The star reaches the main sequence and becomes a stellar system.
Bottom: Details of the accretion stage. Gas and dust fall from the envelope onto the circumstellar disk, which feeds the protostar. A bipolar outflow allows radiation to escape without halting accretion. (Images from Greene 2001)
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Jeans Mass (M J ) and is given by M J = Gµm 5kT
H
3/2 3 4πρ
C1/2
where k is the Boltzmann constant, T is the temperature of the core, µ is the mean molecular mass, m H is the mass of hydrogen, and ρ C is the density of the core. Observations show that, within filaments, collapse tends to be- gin with the shortest axis, then the longer axis, before finally becoming spherical. As the spinning core collapses, it develops a disk of infalling (accreting) material and a shock front, which generates most of the lumi- nosity of these protostars, and bipolar outflows, which help to carry away excess angular momentum (see bottom panel of Figure 1.3). Eventually, the outflows become powerful enough that matter will fall preferentially onto the disk rather than onto the star, causing the protostar to cease accretion, though the surrounding circumstellar disk is still a source of excess infrared emission. The surrounding disk will likely develop plan- ets that clear away much of the gas and dust until the star reaches the main sequence and its stellar wind blows the remaining material away, leaving a young stellar system possibly with planets.
1.1.3 High-mass star formation
While low-mass star formation can be studied relatively easily, studies of high-mass (M> 8 M ) star formation have been troubled by physical and observational limitations (see review by Zinnecker & Yorke (2007), and more recently Tan et al. (2014) and Motte et al. (2017)). High- mass stars form deeply embedded in their molecular cloud and cannot be observed at optical wavelengths until well after they have reached the main sequence. This complicates the detection of new high-mass star-forming regions and the study of known ones. Additionally, high- mass stars are intrinsically rare (as seen from the long tail on the Initial Mass Function) and evolve quickly, making them even harder to detect.
This is best illustrated by comparing the Kelvin-Helmholtz time (τ KH = GM 2 /RL) with the free-fall timescale (t ff = 32Gρ 3π
0
1/2
). The Kelvin- Helmholtz time is, effectively, the length of time before fusion begins (∼
10 4 years for high-mass stars) and the free-fall timescale is the length of time a cloud takes to collapse to a point (∼ 10 5 years for n = 10 5 cm −3 ).
For high-mass stars, τ KH <t ff , so the star will reach the main sequence
while still embedded in its natal cloud. Each stage in the low-mass star
formation sequence has clear observational signposts, generalized using
1
Figure 1.4: Theoretical pre-main sequence life stages of a massive star: First, mass condensations form in a molecular cloud (top left) and collapse begins (top right).
Hot molecular cores are formed (middle left) and radiation escapes from the poles in outflows. The radiation from the new stars forms hyper and ultra-compact HII regions (middle right). Finally, the new cluster of massive stars produces a photon- dominated region (bottom). (Figure from van der Wiel thesis 2011)
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the shape and peak of the spectral energy distribution (SED), but this type of metric does not yet exist for high-mass star formation. This is because all of the pre-main sequence stages of high-mass star formation are embedded, therefore the SEDs look very similar whereas only the very earliest stage of low-mass star formation is embedded. There have been different attempts to classify young high-mass stars based on their near-, mid-, and far-infrared colors, as in the Red MSX Source (RMS) survey (Lumsden et al. 2013), or based on luminosity and clump mass (Molinari et al. 2016), among others.
There are two leading theories about the formation of high-mass stars: core accretion and competitive accretion. In the core accretion (also turbulent core or monolithic collapse) theory (McKee & Tan 2003), a massive near-virial-equilibrium starless core will collapse to form a sin- gle massive star or close binary system, forming an accretion disk as a low-mass star would. A disk allows material to fall onto the star shielded from radiation pressure, while providing an outlet for radiation in the polar directions (Krumholz et al. 2009). In this theory, the accretion rate would have to be high, so models favor a very dense (surface den- sity ∼1 g cm −2 ), turbulent core. In these massive starless cores, support against fragmentation would be provided by turbulence, radiative feed- back, and magnetic fields. A high-mass prestellar core has not yet been unequivocally identified, though numerous candidates have been pro- posed (Pillai et al. 2011; Beuther et al. 2013; Tan et al. 2013; Cyganowski et al. 2014; Ohashi et al. 2016; Sanhueza et al. 2017).
In the competitive accretion theory (Bonnell et al. 2004; Bonnell
& Smith 2011), one clump in a molecular cloud will form many low- mass cores, which will funnel the previously unbound gas preferentially onto the most massive core, forming a cluster of many low-mass stars surrounding a few high-mass stars. This process was shown to require a very-low-turbulence environment to form a massive star in the required timescale (Krumholz et al. 2005).
A third theory – coalescence – where two or more low-mass protostars
come together to form a very massive star, may be applicable to some of
the most massive stars discovered. Its authors (Bonnell & Smith 2011)
admit that it is not likely to be a common formation scenario due to the
extremely dense stellar clusters needed.
1
1.1.4 Circumstellar disks
The circumstellar disk is necessary for star formation in order to allow angular momentum to be transferred from the collapsing cloud while facilitating mass accretion. In young low-mass stars, disks have been consistently detected with a velocity field that shows Keplerian rotation, even at the youngest stages (Tobin et al. 2012). These disks tend to have radii of 100-300 AU, but disks with radii as large as 800 AU have been detected (Piétu et al. 2007).
Circumstellar disks around young high-mass stars have been more elusive. There have been several young high-mass stars housing Keplerian- like disks detected in recent years (Sánchez-Monge et al. 2014; Beltrán et al. 2014; Johnston et al. 2015; Cesaroni et al. 2017), two of which are the focus of Chapters 2 and 4 (G35.20-0.74N and G35.03+0.35). These tend to have radii of thousands of AU, though some as small as 300 AU have been reported. Many of these show asymmetries or inhomo- geneities, which could be a sign of clumps or spiral structure, or could arise from interactions with other nearby objects as high-mass stars tend to form in groups. For a recent review of observations of circumstellar disks see Beltrán & de Wit (2016). A definitive detection of a high-mass protostar with a Keplerian disk and outflows would be a huge step in the study of high-mass star formation.
1.1.5 Hot Cores
After the earliest stages of high-mass star formation, there is a chemically-
rich phase during which molecular species are released from the icy man-
tles covering dust grains that have been warmed by the protostar. Al-
ternatively, molecular species could form in the warm gas surrounding
the protostar. In high-mass stars, this is called a hot molecular core
(HMC; Figure 1.4 – middle left) and the low-mass equivalent is a hot
corino. It is during this stage that we observe complex organic molecules,
molecules with at least 6 atoms containing both carbon and hydrogen
(Herbst & van Dishoeck 2009). The detection of these species marks an
important age milestone for high-mass protostars, as they are quickly
(within 100 kyr) destroyed by the radiation from the star. It is expected
that some of these complex organic molecules will remain in ice in the
outer reaches of a star-forming region or freeze out within the mid-plane
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of a protoplanetary disk at a later stage of stellar evolution and end up in comets or other planetary system bodies. This ice is important in an astrobiological sense, given that the delivery of organic compounds to a primitive Earth is likely to have been key in the origin of life.
At this stage, the envelope surrounding the protostar is heated by the collapse and much of this extra energy is radiated away through molec- ular transitions. These objects also have powerful outflows where high energy molecular and atomic transitions can be seen (upper energy lev- els of >500 K). As hot cores are empirically identified (as warm, dense, compact regions containing young high-mass stars showing complex or- ganic chemistry), it is unknown whether this emission arises in the inner envelope, a circumstellar disk, or outflow cavity walls. The hot-core stage ends when the star begins ionizing its surroundings and becomes a hyper-compact HII region (Figure 1.4 – middle right), signposted by the detection of hydrogen recombination lines and free-free radio continuum emission.
The main focus of this thesis is the chemistry associated with hot cores. The current major questions about this stage of high-mass star formation concern the physical nature of HMCs and whether the young star also hosts an accretion disk during this stage.
1.2 Astrochemistry
1.2.1 A multidisciplinary field
Astrochemistry is the study of reactions and interactions between atoms
and molecules in space. In the field of astrochemistry, observers, theo-
rists, and experimentalists are tightly bound to each other (Figure 1.5)
and communication between the three groups is necessary but can be
difficult. Experimentalists test the effects of different astronomical phe-
nomena (such as high energy radiation) on chemical reactions in a vac-
uum (or as close to a vacuum as we can achieve on Earth). These results
are used by theorists to create chemical models that can show how the
abundances of molecular species change with changing parameters (like
gas temperature and density) and/or over time. Observers study spec-
tra from different astronomical phenomena and rely on laboratory-tested
or theoretically calculated frequencies to determine which spectral lines
1
Figure 1.5: Astrochemistry requires observers, theoreticians, and experimen- talists to work together in concert. This thesis include both observational and theoretical results. (Adapted from the Widucus Weaver group website http://chemistry.emory.edu/faculty/widicusweaver/overview.html)
arise from which species and determine the chemical composition of their
object. Theorists and observers work together using models to under-
stand how the observed sources got their chemical composition, which at
the same time tests if the models can reproduce reality. Often, spectral
lines are observed for which there is no clear identity, and it may then
be the case that the database is incomplete for some species and the
experimentalists must seek to discover which species may be associated
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with a particular transition using spectroscopy. The work in this thesis strives to bring these communities together.
1.2.2 Complex organic molecules
In chemistry, the term “organic” refers to species containing carbon and hydrogen atoms. Historically, this is because the chemists of the eigh- teenth century believed that organic matter from living things had chem- ical properties to differentiate it from inorganic matter. This turned out to be the presence of carbon and hydrogen and we continue to call molecules containing C and H organic molecules. Complex organic molecules, those consisting of 6 atoms or more (Herbst & van Dishoeck 2009), are a signpost of the hot-core stage, especially methyl formate (CH 3 OCHO) and ethylene glycol [(CH 2 OH) 2 ].
Around young high-mass stars, there are three phases that are im- portant to the formation of complex organic molecules: a cold phase (T ∼10 K), a warm-up phase (T =10-100 K), and a hot core phase (T >100 K – high enough for gas-phase chemistry only).
In the cold phase, once gravitational collapse has begun but the tem- perature is still low, synthesis of complex organic molecules can begin in the icy mantles of dust grains as modeled by Charnley (2001) where a host of organic species are created by hydrogen addition reactions start- ing from CO in the ices (see Figure 1.6).
In the warm-up phase, more energetic transitions are seen as the radiation from the young star heats the dust and sublimates some of the ice mantle (Viti & Williams 1999; Viti et al. 2004; Garrod & Herbst 2006;
Garrod et al. 2008). At these higher temperatures, the more complex molecules released from the dust grains drive a new, richer gas-phase hot core chemistry where many complex organic species can be formed.
Chapters 3 features chemical models of the warm-up phase.
1.2.3 Gas and grain chemistry
In the laboratory, molecular species are investigated to determine the en- ergies and frequencies associated with various rotational, vibrational, or electronic transitions for spectroscopy. Two major databases for these re- sults are the JPL molecular spectroscopy database 1 (Pickett et al. 1998)
1
http://spec.jpl.nasa.gov/
1
Figure 1.6: Organic molecules synthesized on grain surfaces. Broken arrows indicate activation energy barriers and the addition of 2H implies a barrier penetration reaction followed by an exothermic addition. Blue species have been detected in star-forming regions. (From Herbst & van Dishoeck 2009, based on Tielens & Charnley 1997)
and the CDMS (Cologne Database for Molecular Spectroscopy 2 ) (Müller
2
http://astro.uni-koeln.de/cdms
13
1
et al. 2001). Data concerning the collisional rates between molecules and H 2 or electrons are necessary for radiative transfer calculations but these are difficult to determine. Experimentalists determine collisional rates under laboratory conditions, but these must be scaled to lower pressure environments. Important databases where these data can be found are LAMDA (Leiden Atomic and Molecular DAtabase 3 ) (Schöier et al. 2005) and BASECOL 4 (Dubernet et al. 2013). Theoreticians cre- ate chemical networks by determining the formation and destruction pathways of a large number set of molecules and their corresponding reaction rates. Such models estimate the abundances of each species based on the density and temperature of the gas, the UV field to which it is exposed, cosmic-ray ionization rate, and other physical conditions.
Two databases for chemical reactions are KIDA (KInetic Database for Astrochemistry 5 Wakelam et al. 2012) and the UMIST database for as- trochemistry 6 (McElroy et al. 2013). All of these databases are needed to get a complete picture of the chemistry in star-forming regions linking ob- servations (transition frequencies) with models (abundances from chem- ical networks) and radiative transfer (excitation and collisional cross- sections).
In a pre-stellar core (the stage before gravitational collapse takes over to form a central object), atoms and molecules freeze onto dust grains.
Here grain-surface chemistry proceeds and more complex molecules are built and occasionally released back into the surrounding gas. At mid- infrared wavelengths we can study the vibrational transitions of the ices frozen onto the surfaces of dust grains (Boogert et al. 2015). Common species found in the ice mantles of dust grains are carbon monoxide (CO), methanol (CH 3 OH) and water (H 2 O). During this stage, areas with lower gas-phase CO abundance can be seen as a consequence of CO molecules freezing out of the gas onto the grain surfaces. CO is one of the main destructive partners of H 2 D + so as a result, the deuterium fraction of the surrounding gas can increase significantly (Caselli & Ceccarelli 2012).
Most gas-phase reactions in dark clouds and hot cores are two-body reactions between atoms, molecules, ions, and electrons where two of
3
http://home.strw.leidenuniv.nl/∼moldata/
4
http://basecol.obspm.fr/
5
http://kida.obs.u-bordeaux1.fr/
6
http://udfa.ajmarkwick.net/index.php
1
Figure 1.7: Diagram depicting the four important grain surface processes (from Cup- pen et al. 2017).
these reactants combine to form one or more products. There are several types of two-body reactions based on the reactants and the products like neutral-neutral (A + BC → AB + C), ion-neutral (A + + B → AB + ), and dissociative recombination (AB + + e − → A + B), among others.
Photo-processes are also important with processes like photo-dissociation (AB + hν → A + B) and photo-ionization (AB + hν → AB + + e − ).
Cosmic-rays become an important ingredient to the chemistry of dark regions where UV photons cannot penetrate. Cosmic-rays can directly ionize atoms and molecules but can also induce photo-processes.
On grain surfaces there are four important processes: adsorption (an
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atom or molecule “freezing out” onto the surface), desorption (an atom or molecule sublimating off the surface), diffusion (an atom/molecule mov- ing around on the surface), and reaction (Figure 1.7). Various types of neutral-neutral reactions take place between atoms, molecules, and radi- cals (atoms or molecules containing at least one unpaired electron). The simplest of these reactions is hydrogenation: adding a hydrogen atom to another atom or molecule. Radical-radical reactions generally proceed easily, as they have no activation barrier. Irradiation of ices by photons or cosmic rays opens up new avenues for chemistry to proceed. The two most important mechanisms for chemical reactions on grain surfaces are the Langmuir-Hinshelwood mechanism and the Eley-Rideal mecha- nism. The Langmuir-Hinshelwood mechanism is where both species in the reaction are moving over the surface and react upon meeting. The Eley-Rideal mechanism is when one stationary reactant on the grain surface is hit by another species from the gas-phase.
Some complex organic molecules are formed predominantly on grain surfaces and others in the gas phase, but generally there are contributions from both phases to the final gas abundances. The main formation route for the eight complex organic molecules studied in Chapter 3 is via grain surfaces.
1.2.4 Chemical modeling
Theoretical astrochemists use computer-based chemical models to better
understand how different species form in different astronomical environ-
ments. Cuppen et al. (2017) give a recent review of gas-grain mod-
els which are summarized here. The two most common approaches
are Monte Carlo models and rate-equation-based models. Microscopic
Monte Carlo models typically follow the formation of molecules on the
surface of a dust grain and show the physical distribution and composi-
tion of each layer of ice on a dust grain. The chemical networks in Monte
Carlo models are very limited (100-200 species and a few hundred surface
reactions). Rate-equation-based models use a series of ordinary differ-
ential equations describing the rates of two-body, photon-induced, and
cosmic-ray induced reactions (as well as rates for desorption and adsorp-
tion) to show the overall gas- and (sometimes) ice-phase abundances of
all species included in the model. This type of model does not indicate
on which layer of ice molecules form, but gives the concentration over
1
time. Recent examples of rate-equation based studies are by Walsh et al.
(2014, 2015); Drozdovskaya et al. (2014, 2015); Quénard et al. (2018) and Monte Carlo based studies are Vasyunin et al. (2009); Vasyunin & Herbst (2013).
Typically, rate-equation-based models are favored in large-scale mod- els for their computational convenience. Single-point two-phase (gas and grain surface) models with thousands of reactions can take less than one minute to run. Adding physical structure requires a couple of weeks of computing time, but including bulk ice chemistry (a third phase with deep layers of ice where diffusion is inhibited) can increase this time to months. Monte Carlo models are computationally expensive with sim- ple models taking days to weeks, but are useful for understanding the physical conditions on dust grains and interpreting both laboratory and astrophysical results.
The rate-equation based models in this work are used to determine differences in age between different high-mass hot cores by attempting to reproduce the observed molecular abundances with the model. Using this sort of model it is possible to test the effect of the physical environment on the chemistry. For example, changing the UV field, gas temperature, or gas density could change the abundances of various molecular species at a particular modeled time.
There are several known limitations to chemical models. For gas- phase processes, initial conditions can have an effect on the outcome of time-dependent models, chemical networks are not complete (and re- duced networks are often used), and the range of model parameters (in- put temperature, density, UV field, etc.) can vary widely (Agúndez &
Wakelam 2013). On grain surfaces, uncertainties are introduced simply by the fact that the processes involved are not well understood. Addi- tionally, binding energies of molecules, the number density of grains and distribution of grain sizes, and diffusion rates all have inherent uncer- tainties.
17
1
Figure 1.8: Some of the antennae from the ALMA observatory high on the Chajnantor Plateau in the Chilean Andes. (Credit: ESO/C. Malin)
1.3 Observational astrochemistry
1.3.1 Observing with sub-millimeter telescopes
Sub-millimeter (sub-mm) astronomy (wavelengths from 0.2-1 mm) has developed significantly in the past 30 years. Spectra from this wavelength range showcase molecular rotational-vibrational transitions, which can be used to determine the chemistry and physical conditions in cooler re- gions (up to a few 100 K) in the universe. Observing at these wavelengths requires precisely designed dishes with few imperfections (the irregulari- ties in the dish must be less than 1/10th the observing wavelength – ie.
to observe at 0.8 mm the dish must be smooth to 0.08 mm scales) and a location with little atmospheric water vapor.
The first ground-based sub-mm observatories were opened in the late
1980s, as the technology for more sensitive receivers and dishes with
higher surface accuracy improved. One of the first sub-mm telescopes
was the James Clerk Maxwell Telescope (JCMT) on Mauna Kea, Hawaii.
1
This was connected to the Caltech Sub-millimeter Observatory to form the first sub-mm interferometer. The first major Hawaiian sub-mm in- terferometer was SubMillimeter Array (SMA), which opened in 2003.
The Plateau de Bure interferometer (now NOEMA-NOrthern Extended Millimeter Array) began with three antennae in 1988 and has recently expanded to nine with the goal of a 12-dish interferometer by 2020. As technology continued to improve, the widely anticipated Atacama Large (sub)Millimeter Array (ALMA – Figure 1.8), located in the Atacama desert in Northern Chile, was inaugurated in March 2013, and receivers continue to be added. ALMA’s large number of antennae with highly sensitive receivers (currently 66) and long baselines allow for spatial res- olution down to 10 milli-arcseconds and velocity resolution as low as 50 m/s. Observational astrochemistry has experienced a boom in recent years with the advent of ALMA and the ability to detect very weak sig- nals from rare molecular species and many lines from common species including their optically thin isotopologs. This gives observers the ability to characterize astronomical environments better than before with better accuracy and potentially greater chemical complexity. In chapters 2 and 5, we use ALMA for high spatial resolution and highly sensitive observa- tions that allow us to resolve the structure of our young high-mass sources and detect weak signals from isotopologs and less abundant species.
There are several advantages of interferometric observations over sin- gle dish observations. First, additional antennae mean a larger collect- ing area and higher sensitivity. This allows the observer to detect very weak signals from rarefied gas. Second, the angular resolution is given by the distance between dishes so smaller structures can be resolved with more antennae at larger distances. Finally, several antennae linked together give two-dimensional maps with spectra at each pixel: a data- cube. Data-cubes are extremely useful in determining the small-scale structure in a star-forming region and observing the motion of the gas.
Data-cubes can also be obtained with single-dish observatories using “on- the-fly” mapping, but these lack the sensitivity and angular resolution of interferometers. Very large dishes can be more sensitive than interfer- ometers, but there is a limit to the size of dish that can be constructed.
Even a dish with a diameter of 100s of meters will have an observable angular resolution limited by its size, whereas interferometers can have baselines of several kilometers giving a much higher angular resolution.
19
1
The main disadvantage to observing with an interferometer is that the observation will have a maximum recoverable scale (MRS) – that is, the largest feature that the observations are sensitive to. The MRS is related to the shortest baseline used in the observations and any emission that is more extended than the MRS will be filtered out. This was a minor problem with the observations in Chapter 2, which showed areas of absorption (“holes”) that did not originate with a physical structure, but from extended emission being filtered out. For this reason, when observing the outflows associated with these sources (Chapter 4), we used additional single-dish observations to ensure that extended emission would not be filtered.
1.3.2 Moment maps
One method that observers use to understand the distribution and move- ment of different molecular species within a gas is the moment map.
The three most common are integrated intensity (moment zero), veloc- ity (moment one), and line width or velocity dispersion (moment two).
The moment zero map gives the total intensity of a particular spectral line at each pixel of the map, thereby showing the spatial distribution of the gas. The moment one map can be used to understand the motion of the gas and is particularly important when determining whether gas is rotating in an ordered way. The moment two map is important in understanding whether the distribution of gas velocities that are being observed is being affected by a temperature gradient or turbulence.
1.3.3 Spectral modeling
Spectral analysis of star-forming regions has given insight into the molec- ular species found in space and the composition and abundances thereof gives insight into the chemical history of the cloud. Spectral line strengths can be used to determine abundances of species while line profiles reveal the kinematics in a cloud. The intensity ratios between different tran- sitions of the same molecule can be used to determine the temperature and density of that species using local thermal equilibrium (LTE) radia- tive transfer calculations like that in the software packages Cassis 7 and
7
available from http://cassis.irap.omp.eu/
1
XCLASS (Möller et al. 2017) or non-LTE software like RADEX (van der Tak et al. 2007).
The XCLASS software package 8 models all transitions of a species at the same time while taking into account opacity and beam-filling fac- tor (source size) to determine the excitation temperature and column density. It is especially useful in analyzing hot cores as multiple molec- ular species can be modeled at the same time, aiding in disentangling blended lines. In Chapters 2 and 5, I use XCLASS to determine the chemical composition of several different hot cores based on single pix- els. XCLASS can also be used on data-cubes to analyze every pixel in a map and show variation in temperature and density.
RADEX is a radiative transfer code that assumes an isothermal and homogeneous medium, treats optical depth with a local escape proba- bility, and uses collisional rate coefficients from the LAMDA database (Schöier et al. 2005). In this thesis, we use this software to calculate line intensity ratios for methyl cyanide (CH 3 CN) across a range of kinetic temperatures and densities.
1.4 Goals of this thesis
The focus of this thesis is understanding the chemistry surrounding young high-mass stars within the context of star formation. The ma- jor science questions that are answered are:
• How does the chemical composition of a source relate to its evolu- tionary stage in the process of star formation? (Chapters 2, 3)
• Do high-mass stars form via Keplerian disks and outflows? (Chap- ters 2, 4)
• How does the prebiotic molecule formamide (NH 2 CHO) form? (Chap- ter 5)
1.5 Outline
This section briefly addresses the outline of the thesis and the topics of the chapters.
8
Available from from: https://xclass.astro.uni-koeln.de/
21
1
In Chapter 2, we use Cycle 0 ALMA observations to determine the full chemical composition (using XCLASS and Cassis) of two high-mass star forming regions which have been proposed to contain Keplerian disks: G35.20-0.74N (G35.20) and G35.03+0.35 (G35.03). The disk can- didate within G35.20 shows an intriguing chemical separation between nitrogen-bearing species and oxygen- or sulfur-bearing species. We de- termine that either the disk was fragmenting on an unresolved scale into separate sources, or that the chemical timescale for change must be very short.
In Chapter 3, we use time-dependent rate-equation-based gas-grain chemical models to understand the chemical segregation in G35.20. We model a single embedded (A V =10) point, warming up the gas and chem- ically enriched ice from 10-500 K over different time periods (related to mass). We test the effect of changing different input parameters on the final chemical composition with the goal of reproducing the observed abundances across the disk candidate in G35.20.
In Chapter 4, we search for the outflows expected to be associated with G35.20 and G35.03 using NOEMA observations with complemen- tary IRAM 30m observations. We image SiO and HCO + , as these are tracers of outflow activity, and compare the results with other observa- tions of large-scale outflow tracers, infrared H 2 emission from shocks and radio emission showing knots of material.
In Chapter 5, we study the chemical origin of formamide (NH 2 CHO), a prebiotic molecule whose main precursor is thought to be either HNCO or H 2 CO. We analyze Cycle 2 ALMA data of three high-mass star- forming regions, with six total sub-sources, expected to contain young O-stars. We use XCLASS to determine whether the three species have any correlation between their observed abundances, and compare the in- tegrated emission peaks (moment zero), velocity structure (moment one), and line width (moment two) trends between our three focus species for each subsource.
Chapter 6 contains our conclusion and an outlook into future studies.
2
Chemical segregation in hot cores with disk candidates
V. Allen, F. F. S. van der Tak, Á. Sánchez-Monge, R. Cesaroni, M. T.
Beltrán (A&A 603, A133, 2017)
Abstract
Context: In the study of high-mass star formation, hot cores are empiri- cally defined stages where chemically rich emission is detected toward a massive YSO. It is unknown whether the physical origin of this emission is a disk, inner envelope, or outflow cavity wall and whether the hot core stage is common to all massive stars.
Aims: We investigate the chemical makeup of several hot molecular cores to determine physical and chemical structure. We use high spectral and spatial resolution submillimeter observations to determine how this stage fits into the formation sequence of a high-mass star.
Methods: The submillimeter interferometer ALMA (Atacama Large Mil-
limeter Array) was used to observe the G35.20-0.74N and G35.03+0.35
hot cores at 350 GHz in Cycle 0. We analyzed spectra and maps from
four continuum peaks (A, B1, B2 and B3) in G35.20-0.74N, separated by
1000-2000 AU, and one continuum peak in G35.03+0.35. We made all
possible line identifications across 8 GHz of spectral windows of molecu-
lar emission lines down to a 3σ line flux of 0.5 K and determined column
densities and temperatures for as many as 35 species assuming local ther-
23
2
modynamic equilibrium (LTE).
Results: In comparing the spectra of the four continuum peaks, we find each has a distinct chemical composition expressed in over 400 different transitions. In G35.20, B1 and B2 contain oxygen- and sulfur-bearing organic and inorganic species but few nitrogen-bearing species whereas A and B3 are strong sources of O-, S-, and N-bearing organic and inor- ganic species (especially those with the CN bond). Column densities of vibrationally excited states are observed to be equal to or greater than the ground state for a number of species. Deuterated methyl cyanide is clearly detected in A and B3 with D/H ratios of 8 and 13%, respectively, but is much weaker at B1 and undetected at B2. No deuterated species are detected in G35.03, but similar molecular abundances to G35.20 were found in other species. We also find co-spatial emission of isocyanic acid (HNCO) and formamide (NH 2 CHO) in both sources indicating a strong chemical link between the two species.
Conclusions: The chemical segregation between N-bearing organic species
and others in G35.20 suggests the presence of multiple protostars sur-
rounded by a disk or torus.
2 2.1 Introduction
Studying the formation of high-mass stars (> 8 M ) is important be- cause they drive the chemical evolution of their host galaxies by injecting energy, through UV radiation, strong stellar winds, and supernovae, and heavy elements into their surroundings (Zinnecker & Yorke 2007). In the study of high-mass star formation, several models have been pro- posed to explain the earliest processes involved. In particular, the work of McKee & Tan (2003) describes a process similar to that of low-mass stars including a turbulent accretion disk and bipolar outflows (see also Tan et al. (2014)), the model by Bonnell and Smith (2011) proposes that matter is gathered competitively from low-turbulence surroundings between many low-mass protostars funneling more material to the most massive core, and the model by Keto (2007) uses gravitationally trapped hypercompact HII regions to help a massive protostar to acquire more mass. All of these models predict the existence of disks as a mecha- nism to allow matter to accrete onto the protostar despite high radiation pressure (Krumholz et al. 2009). However, until recently only a few can- didate disks around B-type protostars were known. Several disks have been detected through the study of complex organic molecules (COMs), molecular species bearing carbon and at least six atoms, allowing for the detection of more disks (Cesaroni et al. 2006; Kraus et al. 2010; Beltrán
& de Wit 2016).
While the earliest stages of high-mass star formation have not yet been clearly determined, it is well known that a chemically rich stage exists, known as a hot molecular core (HMC; see Tan et al. (2014) for a review of high-mass star formation). In this stage COMs are released from the icy surfaces of dust grains or formed in the hot circumstellar gas (Herbst & van Dishoeck 2009). These hot cores are dense (n H >
10 7 cm −3 ), warm (100-500 K), and compact (< 0.05 pc) and are ex-
pected to last up to 10 5 years. The signpost of the hot core stage is a
rich molecular emission spectrum including many COMs like methanol
(CH 3 OH) and methyl cyanide (CH 3 CN). These species may be formed
on dust grain surfaces in a cooler place (or time) and released from
grain surfaces as the forming star heats the grains. Alternatively, they
may form in the hot gas surrounding these massive young objects as the
higher temperature allows for endothermic reactions to take place more
25
2
readily. In reality, it is likely that both formation paths are necessary to achieve the molecular abundances seen around hot cores. High spa- tial and spectral resolution observations can help us to disentangle the different COMs and their spatial distribution during this phase. Disks candidates have been discovered in a few HMC sources, suggesting a link between disks and HMC chemistry. Studying the chemistry of such re- gions can help us to understand the process of high-mass star formation as chemical differences across small physical scales provide clues to the different evolutionary stages involved.
With the advent of the Atacama Large Millimeter Array (ALMA), it is now possible to make highly sensitive, high spectral, and spatial resolution observations of less abundant molecular species. The search continues for the precursors of life, such as the simplest amino acid, glycine (H 2 NCH 2 COOH), but complex organic species with up to 12 atoms have already been detected 1 . These include important precur- sors to amino acids, such as aminoacetonitrile (H 2 NCH 2 CN), detected by Belloche et al. (2008); the simplest monosaccharide sugar glycolalde- hyde (CH 2 OHCHO), first observed in a hot molecular core outside the Galactic center by Beltrán et al. (2009); and formamide (NH 2 CHO) ex- tensively studied by López-Sepulcre et al. (2015). With ALMA we have the ability to detect hot cores and study their properties in detail to de- termine how the spatial distribution of COMs influences the formation of massive stars. Despite advances in technology, astronomers have yet to determine whether the emission from the hot core arises from the inner envelope (spherical geometry) or from a circumstellar disk (flat geome- try). It is also possible that these hot cores could be outflow cavity walls as has been recently modeled for low-mass stars by Drozdovskaya et al.
(2015).
In this paper we study the chemical composition and spatial distribu- tion of species in two high-mass star-forming regions, G35.20-0.74N and G35.03+0.35 (hereafter G35.20 and G35.03 respectively), which have been shown to be strong disk-bearing candidates. We present a line sur- vey of the hot core in G35.03 and in four continuum peaks in the G35.20 hot core containing ∼ 18 different molecular species (plus 12 vibrationally excited states and 22 isotopologues) of up to 10 atoms and >400 emission lines per source. We also present our analysis of the chemical segregation
1
https://www.astro.uni-koeln.de/cdms/molecules
2
within core B of G35.20 depicting a small-scale (<1000 AU) separation of nitrogen chemistry and temperature difference. A chemical separation on the scale of a few 1000s of AU within a star-forming region has been seen before in Orion KL (Caselli et al. 1993a), W3(OH) and W3(H 2 O) (Wyrowski et al. 1999b), and AFGL2591 (Jiménez-Serra et al. 2012).
The distance to both sources has been estimated from parallax mea- surements to be 2.2 kpc for G35.20 (Zhang et al. 2009) and 2.32 kpc for G35.03 (Wu et al. 2014). G35.20 has a bolometric luminosity of 3.0 × 10 4 L (Sánchez-Monge et al. 2014) and has been previously stud- ied in Sánchez-Monge et al. (2013a) and Sánchez-Monge et al. (2014) in which they report the detection of a large (r∼2500 AU) Keplerian disk around core B and a tentative Keplerian disk in core A. The bolometric luminosity of G35.03 is 1.2 × 10 4 L and was reported to have a Kep- lerian disk (r∼1400-2000 AU) around the hot core A in Beltrán et al.
(2014).
Table 2.1: Source continuum characteristics
Continuum peak Right ascension Declination Size
aS
νbT
kincN (H
2)
dMass
e(
00) (Jy) (K) (cm
−2) (M ) G35.20 A 18:58:12.948 +01:40:37.419 0.58 0.65 285 2.4 × 10
2513.0 G35.20 B1 18:58:13.030 +01:40:35.886 0.61 0.19 160 6.4 × 10
243.8 G35.20 B2 18:58:13.013 +01:40:36.649 0.65 0.12 120 3.3 × 10
242.2 G35.20 B3 18:58:13.057 +01:40:35.442 0.58 0.08 300 2.5 × 10
241.4 G35.03 A 18:54:00.645 +02:01:19.235 0.49 0.21 275 1.1 × 10
254.4
a
: Deconvolved average diameter of the 50% contour of the 870 µm continuum.
b
:Integrated flux density within the 10σ contour of the 870 µm continuum.
c
:Average kinetic temperature based on CH
3CN line ratios as calculated using RADEX. For details, see § 2.3.3.
d
:Calculated from source size, continuum flux density, and kinetic temperature (§ 2.3.3).
e
:Sources mass calculated as in Sánchez-Monge et al. (2014) using the average kinetic temperatures.
2.2 Observations and methods
2.2.1 Observations
G35.20 and G35.03 were observed with ALMA in Cycle 0 between May
and June 2012 (2011.0.00275.S). The sources were observed in Band 7
(350 GHz) with the 16 antennas of the array in the extended configura-
tion (baselines in the range 36-400 m) providing sensitivity to structures
27
2
Figure 2.1: Image of the 870 µm continuum emission from Cycle 0 ALMA observations of G35.20. Contour levels are 0.03, 0.042, 0.055, 0.067, 0.08, 0.10, 0.13, 0.18, and 0.23 Jy/beam (σ = 1.8 mJy/beam). The pixel-sized colored squares indicate each of the spectral extraction points. Ellipse denotes the synthesized beam.
0.4 00 - 2 00 . The digital correlator was configured in four spectral windows (with dual polarization) of 1875 MHz and 3840 channels each, providing a resolution of ∼0.4 km s −1 . The four spectral windows covered the frequency ranges [336 849.57-338 723.83] MHz, [334 965.73-336 839.99]
MHz, [348 843.78-350 718.05] MHz, and [346 891.29-348 765.56] MHz.
The rms noise of the continuum maps are 1.8 mJy/beam for G35.20 and
3 mJy/beam for G35.03. For full details, see Sánchez-Monge et al. (2014)
2
Figure 2.2: Image of the 870 µm continuum emission from Cycle 0 ALMA observations of G35.03. Contour levels are 8.6, 16.8, 24.9, 33, 41.2, 49.3, 57.4, 65.5, 73.6, 81.8, and 89.9 mJy/beam (σ = 3.0 mJy/beam). The pixel-sized colored square indicates the spectral extraction point. The cores identified in Beltrán et al. (2014) are labeled A-F. Ellipse denotes the synthesized beam.
and Beltrán et al. (2014).
2.2.2 Line identification process
Spectra were extracted from the central pixel of the continuum peak
in core A and the three continuum peaks in core B (B1, B2, B3) in
29
2
G35.20 and the continuum peak in core A in G35.03 using CASA 2 (see Figures 2.1 and 2.2 for spectra extraction positions and continuum lev- els and Table 2.1 for the J2000 coordinates and a summary of statis- tics). The other peaks (B-F in G35.03 and C-G in G35.20) were not analyzed because they do not show hot core chemistry, i.e., little or no emission from COMs. The three continuum peaks in G35.20 B were chosen to investigate the chemical structure across the disk shown in Sánchez-Monge et al. (2014) (who analyzed B as a single core); however, the disk in G35.03 A only has a single continuum point associated with the hot core, so analysis for this source was from this peak. G35.20 A was analyzed as the strongest continuum source in the region with hot core chemistry and was also analyzed at the single continuum peak.
Line parameters (listed in Appendix B) were determined using Gaussian profile fits to spectral lines from each continuum peak via Cassis 3 , pri- marily using the Cologne Database for Molecular Spectroscopy (CDMS;
Müller et al. (2001)) database and Jet Propulsion Laboratory (JPL;
Pickett et al. (1998)) database for deuterated methanol (CH 2 DOH), ethanol (C 2 H 5 OH), NH 2 CHO, acetaldehyde (CH 3 CHO), and CH 3 OH (ν=2) transitions.
The process of identifying all species present in these spectra con- sisted of several parts. Bright lines (T B > 5 K) from known species were identified first (i.e., those from Sánchez-Monge et al. (2014): CH 3 OH, methyl formate (CH 3 OCHO), CH 3 CN, simple molecules) numbering ∼100 lines per source. The remaining bright lines (> 5 K) were identified by choosing the most likely molecular candidate, namely the transition with the higher Einstein coefficient that is limited to a minimum of about 10 −7 s −1 , or with a upper level energy (E up ) within the expected range, gen- erally less than 500 K, composed of C,H,O and/or N and within 2 km s −1 (∼2 MHz) of the rest frequency of the transition. This brings the total to about 200 per source. Finally, for any remaining unidentified lines > 3σ (∼ 0.5 K) a potential species was selected, then the entire spectrum was checked for nondetections of expected transitions of this species. The total number of identified lines was over 400 for each source, including partially blended and blended transitions for which it was ev-
2
Common Astronomy Software Applications is available from http://casa.nrao.edu/
3
CASSIS has been developed by IRAP-UPS/CNRS (http://cassis.irap.omp.eu).
2
Table 2.2: Line detections and measurements for H
2CS with errors in parentheses.
G35.20 A G35.20 B1 G35.20 B2
Transition Frequency FWHM T
peakFWHM T
peakFWHM T
peak(MHz) (km s
−1) (K) (km s
−1) (K) (km s
−1) (K)
H
2CS ν=0
10
1,10-9
1,9338083 5.9 (0.1) 51.7 (0.9) 2.7 (0.1) 27.3 (1.0) 2.4 (0.1) 28 (1) 10
1,9-9
1,8348532 5.8 (0.1) 59.2 (1.3) 2.6 (0.1) 31 (1) 2.3 (0.1) 31 (1)
H
2C
33S
10
1,10-9
1,9335160 7.5 (0.2) 3.91 (0.09) 1.6 (0.5) 0.4 (0.1) 1.5 (0.6) 0.4 (0.2) H
2C
34S
10
0,10-9
0,9337125 blended 1.2 (0.7) 0.7 (0.4) 1.5 (0.2) 1.0 (0.1) 10
4,6-9
4,5337460 blended in abs. feature 1.5 (0.4) 0.55 (0.09) 10
2,9-9
2,8337475 6.3 (0.3) 16.0 (0.3) 1.66 (0.08) 3.8 (0.2) 1.7 (0.1) 3.8 (0.2) 10
3,8-9
3,7337555 blended 2.0 (0.2) 0.78 (0.05) 1.7 (0.5) 0.6 (0.2) 10
3,7-9
3,6337559 blended 2.16 (0.09) 1.55 (0.04) 1.3 (0.1) 0.54 (0.04) 10
2,8-9
2,7337933 blended 1.2 (0.5) 0.8 (0.2) 1.8 (0.5) 0.7 (0.1)
G35.20 B3 G35.03 A
Transition Frequency FWHM T
peakFWHM T
peak(MHz) (km s
−1) (K) (km s
−1) (K)
H
2CS ν=0
10
1,10-9
1,9338083 2.54 (0.06) 44.3 (0.9) 6.6 (0.1) 21.0 (0.3) 10
1,9-9
1,8348532 2.58 (0.04) 52.1 (0.7) 6.33 (0.09) 21.3 (0.3)
H
2C
33S
10
1,10-9
1,9335160 2.5 (0.2) 1.47 (0.08) < 3σ H
2C
34S
10
0,10-9
0,9337125 1.9 (0.1) 2.5 (0.1) < 3σ
10
4,6-9
4,5337460 blended blended
10
2,9-9
2,8337475 3.1 (0.2) 4.9 (0.2) blended 10
3,8-9
3,7337555 2.03 (0.04) 2.35 (0.03) < 3σ 10
3,7-9
3,6337559 2.37 (0.06) 2.26 (0.03) < 3σ 10
2,8-9
2,7337933 2.3 (0.1) 2.4 (0.1) < 3σ
ident or implied by the line shape that another transition was present.
It is noted in Appendix 2.B if the line identity is uncertain in case of strong blending or multiple probable candidates.
The remaining total of unidentified and unclear identity (where there
is more than one potential species) lines is about 80 for the peaks in B
and G35.03 with an additional 30 in G35.20 A. These unknown transi-
tions could be either species whose transitions for this frequency regime
have not yet been measured/calculated or species whose likely identity
was not clear. The peak intensities of the unknown lines were all less
than 5 K. Line parameters were measured by fitting a Gaussian profile to
the emission line with the Cassis line spectrum tool. In some cases, par-
tially blended lines were fit together with one or more extra Gaussians
31
2
for a more accurate measurement, although in those cases the errors were larger. The full line survey can be found in Appendices A and B, but an example is given in Table 2.2, where the parameters obtained for thio- formaldehyde (H 2 CS) are listed. The line identities are first presented ordered by frequency, and then, to emphasize the chemistry of these ob- jects, the tables of measured line parameters are sorted by molecular species.
To validate the line identifications, fits were made simultaneously to all identified species via the XCLASS software Möller et al. (2017) 4 . This program models the data by solving the radiative transfer equation for an isothermal object in one dimension, taking into account source size and dust attenuation. The residuals between the fitted lines and observed spectra are between 5 and 25%, validating the XCLASS fits and our line identifications. The observed spectra and the XCLASS fits can be found in Appendix 2.E and further information about the XCLASS analysis is detailed in § 2.3.4.
2.2.3 Image analysis
To confirm our identifications of several complex organic species, maps were made of unblended transitions. Similar spatial distributions and velocity profiles of transitions with similar upper energy levels are con- sistent with these being the same species. Figure 2.3 shows integrated intensity (moment zero) maps of CH 3 OCHO ν=0 and ν=1 transitions, H 2 CS, (CH 2 OH) 2 , CH 3 CHO ν=0, and ν=2 transitions in G35.20 and Figure 2.4 shows the same transitions in G35.03. During this process, we discovered a difference in spatial extent between N-bearing species and O-bearing species in G35.20 core B. The N-bearing species peak at the location of continuum peak B3 and are generally not found at the other side of the disk near continuum peak B2. We comment on this difference in detail in § 2.4.2. Channel maps were made in CASA for 20 different species for interesting isolated lines with a range of upper energy levels (see Table 2.3) to determine the spatial distribution of var- ious species. Zeroth (integrated intensity), first (velocity), and second (dispersion) moment maps were also made for these species. A selection of integrated intensity maps can be found in Figures 2.3 and 2.4.
4
