deletions | additions       diff --git a/.gitignore b/.gitignore  old mode 100644  new mode 100755  diff --git a/IntroductionMath.ipynb b/IntroductionMath.ipynb  old mode 100644  new mode 100755  diff --git a/Makefile b/Makefile  old mode 100644  new mode 100755  index bbda12f..ed012cf  --- a/Makefile  +++ b/Makefile 
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${LATEX} thesis.tex  ${BIBTEX} thesis  ${LATEX} thesis.tex  cp thesis.pdf thesis_`date +%Y-%m-%d_%H:%M:%S`.pdf  mwe:   ${LATEX} mwe.tex  diff --git a/README.md b/README.md  old mode 100644  new mode 100755  diff --git a/Table1A_Measured.tex b/Table1A_Measured.tex  old mode 100644  new mode 100755  diff --git a/Table1B_Measured.tex b/Table1B_Measured.tex  old mode 100644  new mode 100755  diff --git a/Table2_Inferred.tex b/Table2_Inferred.tex  old mode 100644  new mode 100755  diff --git a/Table3_Derived.tex b/Table3_Derived.tex  old mode 100644  new mode 100755  diff --git a/Table3_Inferred.tex b/Table3_Inferred.tex  old mode 100644  new mode 100755  diff --git a/Table4_Other.tex b/Table4_Other.tex  old mode 100644  new mode 100755  diff --git a/Table_RRLs76.tex b/Table_RRLs76.tex  old mode 100644  new mode 100755  diff --git a/aastex_hack.sty b/aastex_hack.sty  old mode 100644  new mode 100755  diff --git a/ch_boundhii.tex b/ch_boundhii.tex  old mode 100644  new mode 100755  diff --git a/ch_conclusion.tex b/ch_conclusion.tex  old mode 100644  new mode 100755  diff --git a/ch_h2co.tex b/ch_h2co.tex  old mode 100644  new mode 100755  diff --git a/ch_h2colarge.tex b/ch_h2colarge.tex  old mode 100644  new mode 100755  index 1b25cde..c4eb6e3  --- a/ch_h2colarge.tex  +++ b/ch_h2colarge.tex 
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\input{preface}  %\standalonefalse  \chapter{\formaldehyde observations of BGPS sources not previously observed with Arecibo}  \label{ch:h2colarge} 
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\subimport{/Users/adam/work/h2co/maps/paper/}{h2co_maps}  \subimport{/Users/adam/work/h2co/lowdens/paper/}{h2co_lowdens}  \input{solobib} %\input{solobib}  \end{document}  diff --git a/ch_iras05358.tex b/ch_iras05358.tex  old mode 100644  new mode 100755  diff --git a/ch_software.tex b/ch_software.tex  old mode 100644  new mode 100755  diff --git a/ch_v2.tex b/ch_v2.tex  old mode 100644  new mode 100755  diff --git a/ch_w5.tex b/ch_w5.tex  old mode 100644  new mode 100755  diff --git a/deluxetable.sty b/deluxetable.sty  old mode 100644  new mode 100755  diff --git a/emulateapj.sty b/emulateapj.sty  old mode 100644  new mode 100755  diff --git a/figures_chH2CO/DensityHistogram.png b/figures_chH2CO/DensityHistogram.png  old mode 100644  new mode 100755  diff --git a/figures_chH2CO/Derived_1.1mmVsContinuumRatio_brightest.png b/figures_chH2CO/Derived_1.1mmVsContinuumRatio_brightest.png  old mode 100644  new mode 100755  diff --git 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\input{preface}  \section{Introduction}  Turbulence Nearly all gas in the interstellar medium  is important. supersonically turbulent. The  properties of this turblence are essential for determining how star formation  progresses.  There are now predictive theories of star formation that include formulations  of the Initial Mass Function  \citep{Hopkins2012b,Chabrier2010a,Hennebelle2011a,Hennebelle2013a,Padoan2012a,Padoan2011b,Padoan2007a,Krumholz2005a}.  The distribution of stellar masses depends critically on the properties of the  turbulence. It is therefore essential to measure the properties of turbulence in the  molecular clouds that produce these stars.  Recent works have used simulations to characterize the density distribution  from different driving modes of turbulence  \citep{Federrath2013a,Federrath2011a,Federrath2010a,Federrath2009a,Federrath2008a,Kritsuk2011a}.  These works determined that there is a relation between the mode of turbulent driving and the width  of the turbulent distribution, with $\sigma_{\ln \rho} = \ln\left(1+b^2 \mathcal{M}^2 \frac{\beta}{\beta+1}\right)$,  where $\beta=2 (\mathcal{M}_A / \mathcal{M})^2 = 2 (c_s/v_A)^2$.  This equation can also be expressed in terms of the compressive mach number  $\mathcal{M}_c = b \mathcal{M}$, with $b\approx 1/3$ corresponding to  solenoidal forcing and $b = 1$ corresponding to purely compressive forcing  \citep{Federrath2010a,Konstandin2012a,Molina2012a}.  %However, \citet{Hopkins2013a} notes  %that the lognormal approximation of the turbulent density distribution  All of the turbulence-based theories of star formation explicitly assume a  lognormal form for the density probability distribution $P_V{\ln \rho}$ of the  gas. However, recent simulations \citep{Federrath2013a} and theoretical work  \citep{Hopkins2013a} have shown that the assumption of a lognormal distribution  is often very poor\footnote{The simultaneous assumption of a lognormal  mass-weighted and volume-weighted density distribution is also not  self-consistent \citep{Hopkins2013a}. }, deviating by orders of magnitude at  the extreme of the density distributions. Since these theories all involve an  integral over the density probability distribution funcion (PDF), skew in the  lognormal distribution can drastically affect the overall star formation rate  and predicted initial mass function.   While simulations are powerful probes of wide ranges of parameter space, no  simulation is capable of including all of the physical processes and spatial  scales relevant to turbulence. Observations are required to provide additional  constraints on properties of interstellar turbulence and guide simulators  towards the most useful conditions and processes to include.  \citet{Kainulainen2013a} and \citet{Kainulainen2012a} provide some of the first  observational constraints on the mode of turbulent driving, finding  $b\approx0.4$, i.e. that there is a mix of solenoidal and compressive modes.  % However, these observations still attempted to characterize a lognormal  % distribution.  Formaldehyde, \formaldehyde, is a unique probe of density in molecular clouds.  Like CO, it is ubiquitous, with a nearly constant abundance wherever CO is  found \citep{Tang2013a,Mangum1993a}. The lowest rotational transitions of  \ortho at 2 and 6 cm can be observed in absorption against the cosmic microwave  background or any bright continuum source \citep{Ginsburg2011a,Darling2012b}.  The ratio of these lines is strongly sensitive to the local density of \hh, but  it is relatively insensitive to the local gas temperature  \citep{Wiesenfeld2013a,Troscompt2009a}. Unlike critical density tracers, the  \formaldehyde line ratio has a direct dependence on the density that is  independent of the column density.  However, the particular property of the \formaldehyde densitometer we explore  here is its ability to trace the \emph{mass-weighted} density of the gas.  Typical density measurements from \thirteenco or dust measure the total mass  and assume a line-of-sight geometry, measuring a \emph{volume-weighted}  density, i.e. $\rho_V = M_{tot}/V_{tot}$. In contrast, the \formaldehyde  densitometer is sensitive to the density that corresponds to the most mass,  i.e. $\rho_M = \int M \rho d M / M_{tot}$. The volume- and mass- weighted  densities will vary with different drivers of turbulence, so in clouds  dominated by turbulence, if we have measurements of both, we can infer the  driver.  Federrath, Kainulainen, Kritsuk, etc.  \section{Non-star-forming, low column-density clouds in absorption}  In \citet{Ginsburg2011a}, we noted that the \formaldehyde densitometer revealed  volume densities much higher than expected given the cloud-average densities  from \thirteenco observations. The densities were higher even than typical 
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statistical argument; here we attempt to demonstrate that the clumps in GMCs  are of very high density in individual clouds.  In order to detect low-column-density clouds, we must use bright background  illumination sources at 2 and 6 cm, i.e. HII regions. There are a few dozen of  these within the inner Galactic plane, including the sources observed in  \citet{Ginsburg2011a} and the majority of the bright sources in the BGPS  \citep{Ginsburg2013}.  As an example case-study, \section{Observations}  We report \formaldehyde observations performed at the Arecibo Radio  Observatory\footnote{The Arecibo Observatory is part of the National Astronomy  and Ionosphere Center, which is operated by Cornell University under a  cooperative agreement with the National Science Foundation. } and the Green  Bank Telescope\footnote{ The National Radio Astronomy Observatory operates the  GBT and VLA and is a facility of the National Science Foundation operated under  cooperative agreement by Associated Universities, Inc. } that will be  described in more detail in \citep{Ginsburg2011a}, with additional data to be  published in a future work. Arecibo and the GBT have FWHM$\approx50$\arcsec  beams at the observed frequencies of 4.829 and 14.488 GHz, respectively.  Observations were carried out in a position-switched mode with 3 and 5.5\arcmin  offsets for the Arecibo and GBT observations respectively.  The Boston University / Five-College Radio Astronomy Observatory Galactic Ring  Survey \thirteenco data was also used. The BU FCRAO GRS \citep{Jackson2006a}  is a survey of the Galactic plane in the \thirteenco\ 1-0 line with $\sim  46\arcsec$ resolution. We used reduced data cubes of the $\ell=43$ region.  \subsection{A non-star-forming molecular cloud}  % In order to detect low-column-density clouds,  we must use bright background  % illumination sources at 2 and 6 cm, i.e. HII regions. There are a few dozen of  % these within the inner Galactic plane, including the sources observed in  % \citet{Ginsburg2011a} and the majority of the bright sources in the BGPS  % \citep{Ginsburg2013b}.  We  examine the line of sight towards  G43.17+0.01, also known as W49. In the a  large survey, we observed two lines of sight towards W49, the second at  G43.16-0.03. Both are very bright continuum sources, and two GMCs are easily  detected in both  \formaldehyde absorption and \thirteenco emission. Figure \ref{fig:w49fullspec} shows the spectrum dominated by W49 itself, but with  clear foreground absorption components. The continuum level subtracted from the spectra  are 73 K at 6 cm and 11 K at 2 cm for the south component, and 194 K at 6 cm 
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% 2001ApJ...551..747S  \FigureTwo{figures/G43.17+0.01_H2CO_overplot_gbt9x.png}  {figures/G43.16-0.03_H2CO_overplot_gbt9x.png} {Spectra of the \formaldehyde \oneone (black), \twotwo (red), and \thirteenco  1-0 (green) lines towards G43.17+0.01 (left) and G43.16-0.03 (right).  The \formaldehyde spectra are shown continuum-subtracted, and the \thirteenco  spectrum is offset by 1 K for clarity. The GBT \twotwo spectra are multiplied  by a factor of 9 so the smaller lines can be seen. }{fig:w49fullspec}{1}  We focus on the ``foreground'' lines line  at $\sim40$\kms and $\sim65$  \kms, since they are it is  not associated with the extremely massive W49 region. It is difficult The cloud, known as GRSMC  43.30-0.33 \citep{Simon2001a}, was confirmed  to assess the level of have no associated  star formation within these clouds, since they lie  directly along the line of sight to W49, but additional in that work. Additional  \formaldehyde spectra ofthe  surrounding sources that are bright at 8-1100 \um and within the \thirteenco contours of  the cloud  show that they are all at the velocity of W49 and therefore are not associated with these foreground clouds.Additionally, the 40 \kms cloud, known as GRSMC 43.30-0.33  \citep{Simon2001a}, was confirmed in that paper to have no associated star  formation.  The 40 \kms cloud, \formaldehyde lines are  is observed in its outskirts, the outskirts of the cloud,  not at the peak of the \thirteenco emission. The cloud structure is vast, spanning spans  $\sim0.6\degrees$, or $\sim60$ $\sim30$  pc at $D=2.8$ kpc \citep{Roman-Duval2009a}. It is detected in \oneone absorption at all 6 locations observed in \formaldehyde (Figure \ref{fig:40kmscloud}), but \twotwo is only detected in front of the W49 HII region because of the higher signal-to-noise at that location. The detected \thirteenco and \formaldehyde lines are fairly narrow, with \formaldehyde FWHM $\sim1.3$-$2.8$ \kms and \thirteenco widths from 1.8-5.9 \kms. The \thirteenco lines are 50\% wider than the \formaldehyde lines. The highest \thirteenco contours are observed as a modest IRDC, infrared dark cloud  in Spitzer 8 \um images,  but no dust emission peaks are observed at 500 \um or 1.1 mm. mm associated with the dark gas.  This is an indication that any star formation, if present, is weak - no clusters massive dense clumps  are presently forming  from present within  this cloud. %  % Full GRSMC GLON deg GLAT deg VLSR km/s DelV km/s Rad pc Mass Msun e_ Msun nH2 cm-3 Tex K tau Sigma Msun/pc2 alpha Note RD09 _RA.icrs deg _DE.icrs deg  % 1 G043.14-00.36 043.14 -00.36 41.17 3.13 3.9 6.8e+03 2.2e+03 431.4 5.66 1.92 144.8 0.91 i RD09 287.88 +08.91  % 2 G043.04-00.11 043.04 -00.11 41.59 3.48 4.2 8.3e+03 3.2e+03 394.6 5.68 1.77 145.7 1.02 i RD09 287.61 +08.94  % 3 G043.14-00.76 043.14 -00.76 59.02 2.92 9.8 3.0e+04 1.0e+04 117.6 5.78 1.28 100.4 0.45 i RD09 288.24 +08.72  % 4 G043.49-00.71 043.49 -00.71 41.59 1.84 1.9 1.3e+03 4.6e+02 645.7 5.23 1.85 108.8 0.84 i RD09 288.35 +09.06  %  % 6.8+8.3 = 15.1 x10^3 msun  % circle is closer to 0.3 degrees, radius=14.66 pc (0.3 * 3600 * 2800 / 206265.)  % In [105]: 1.5e4 * 2e33 / (2.8*1.67e-24) / (4/3.*pi*(15*3.08e18)**3)  % Out[105]: 15.532172896708314  %   % In [106]: 1.5e4 * 2e33 / (2.8*1.67e-24) / ((2*15*3.08e18)**3)  % Out[106]: 8.132626711097554  %   % In [107]: 1.5e4 * 2e33 / (2.8*1.67e-24) / (4/3.*pi*(15*3.08e18)**3*(1*1*0.1))  % Out[107]: 155.32172896708315  %   The cloud has mass $M_{CO} = 1.5\ee{4}$ \msun in a radius $r=15$ pc, so its  mean density is $n(\hh) \approx 15$ \percc assuming spherical symmetry. If we  instead assume a cubic volume, the mean density is $n(\hh)\sim8$ \percc. For  an oblate spheroid, with minor axis $0.1\times$ the other axes, the mean  density is $n\sim150\percc$, which we regard as a conservative upper limit.  \citet{Simon2001a} report a mass $M_{CO} = 6\ee{4} \msun$ and $r=13$ pc,  yielding a density $n(\hh)=100$ \percc, which is consistent with our estimates  but somewhat higher than measured by \citet{Roman-Duval2010a} because of the  improved optical depth corrections in the latter work.  %  It resembles, in that respect, the California molecular %  cloud. However, it is much smaller, with $M\approx8.3\ee{3}\pm3.2\ee{3} \msun$ %  compared to California's $\sim10^5$. \Figure{figures/W49_RGB_40kms_aplpy.png}  {The G43 40 \kms cloud. The background image shows Herschel SPIRE 70 \um (red), 
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optical depth infrared dark cloud associated with this GMC.}  {fig:40kmscloud}{0.5}{0}  \section{Modeling \formaldehyde}  In order to infer densities using the \formaldehyde densitometer, we use the  low-temperature collision rates given by \citet{Troscompt2009a} with RADEX  \citep{van-der-Tak2007a} to build a grid of predicted line properties covering  densities from $10-10^8$ \hh \percc, temperatures from 5-50 K, column densities  $N(\ortho)$ from $10^{11}-10^{16}$ \persc, and ortho-to-para ratios from  $10^{-3}-3$.  The \formaldehyde densitometer measurements are shown in Figure \ref{fig:h2codensg43}.  The figures show optical depth spectra, given by the equation  $$\tau \begin{equation}  \tau  = -\log\left(\frac{S_\nu -\ln\left(\frac{S_\nu  + 2.73}{\bar{C_\nu} + 2.73}\right)$$ 2.73}\right)  \end{equation}  where $S_\nu$ is the spectrum (with continuum included) and $\bar{C_\nu}$ is  the measured continuum.  \FigureTwo{figures/G43.16-0.03_40kms_h2codensfit.png}  {figures/G43.17+0.01_40kms_h2codensfit.png} continuum, both in Kelvins. The cosmic microwave background  temperature is added to the continuum since \formaldehyde can be seen in  absorption against it, though towards W49 it is negligible.  % G43.17  % [Param #0 DENSITY0 = 4.36419 +/- 0.0755311 Range: [1,8],  % Param #1 COLUMN0 = 12.4276 +/- 0.0417072 Range: [11,16],  % Param #2 ORTHOPARA0 = -1.25514 +/- 1.30736 Range:[-3,0.477121],  % Param #3 TEMPERATURE0 = 27.5313 +/- 18.9722 Range: [5,55],  % Param #4 CENTER0 = 39.5386 +/- 0.00108955 ,  % Param #5 WIDTH0 = 0.379159 +/- 0.000709161 Range: [0,inf)]  % stats_dict['DENSITY0']['CI'] = [9337.9885256493308, 23130.782791203092, 7697.2821104556024]  % G43.16  % [Param #0 DENSITY0 = 4.30989 +/- 0.108066 Range: [1,8],  % Param #1 COLUMN0 = 12.1953 +/- 0.0535173 Range: [11,16],  % Param #2 ORTHOPARA0 = -1.25075 +/- 1.31576 Range:[-3,0.477121],  % Param #3 TEMPERATURE0 = 28.037 +/- 19.9428 Range: [5,55],  % Param #4 CENTER0 = 40.3406 +/- 0.0102343 Range: [35,45],  % Param #5 WIDTH0 = 0.765835 +/- 0.0100109 Range: [0,inf)]  % stats_dict['DENSITY0']['CI'] = [10105.478740355829, 20412.420448321209, 11646.197755316312]  \FigureTwo{figures/G43.16-0.03_40kmscloud_MCMCfit_nolegend.png}  {figures/G43.17+0.01_40kmscloud_MCMCfit_nolegend.png}  {Optical depth spectra of the \oneone and \twotwo lines towards the two W49  lines of sight, G43.16 (left) and G43.17 (right). Thefitted parameters, along with the statistical 1-$\sigma$  errors, are shown in the legend. The  optical depth ratio falls in a regime where temperature has very little effect and there is no degeneracy between low and high densities \citep[see Figure 2 of][]{Ginsburg2011a}. For the right line,  it is also unaffected by lognormal turbulence, i.e. no matter what the width of  the density distribution, the measured density remains unchanged \citep[see  Figure 3 of][]{Ginsburg2011a}.} densities. }  {fig:h2codensg43}{1}  % fitted using ~/work/h2co/G43.17+0.01/fit_small_lines.py, specifically the MC40 million-long chains  % and G43.16-0.03/fit_small_lines.py  We performed line fits to both lines simultaneously using a Markov-chain  monte-carlo approach, assuming uniform priors across the modeled parameter  space and independent gaussian errors on each spectral bin.  The density measurements are very precise, with $n\approx1.56\times10^4 \pm  0.14\ee{4}$ $n\approx23,000 {}^{+9300}_{-7700}$  \percc (95\% confidence interval)  and $n\approx 1.98\times10^4 \pm 0.32\ee{4}$ 20,400 {}^{+12000}_{-10000}$  \percc for G43.17+0.01 and G43.16-0.03 respectively. At While this is a precise measurement  of gas density, we now need to examine exactly what gas we have measured the  density of.  %At  this level of precision, the density %density  measurements are dominated by systematics - especially gas systematic uncertainties in  temperature and %the ortho-to-para ratio of \hh.   %However...  % and  collision rate uncertainties - which limit the accuracy to $\sim50\%$ using %  the \citet{Green1991} rates %  \citep{Zeiger2010}. Nonetheless, the % The measured  density is much higher than the \thirteenco-measured cloud-average %  density $n\approx 400$ \percc \citep[for cloud %  GRSMC\_G043.04-00.11;][]{Roman-Duval2010a}, with %  $n_{\formaldehyde}/n_{\thirteenco} \approx 50$. The discrepancy is worse using %  the \citet{Simon2001a} cloud-averaged density $n\approx 100$ \percc. %  Our density measurements are about 4$\times$ higher than CO/CI LVG density %  measurements from \citet{Plume2004a}, though those measurements rely on %  uncertain abundances and are fairly sensitive to temperature. Since the W49 line of sight is clearly on the outskirts of the cloud, not  through its core, center,  such a high density is unlikely to be an indication that this line of sight corresponds to a centrally condensed density peak (e.g., a core). The comparable density observed through two different lines of sight  separated by $\sim 2$ pc also supports this idea.  %  Using %  Figure 4 of \citet{Ginsburg2011a}, we can `turbulence-correct' the density %  measurements, but even in the most extreme case with a turbulent density %  distribution lognormal width $\sigma_s = 1.5$, the correction is only a factor %  of 2.5, reducing the discrepancy to a factor of $\sim20$. % We should then ask, if there is gas at high density, how much is at this density?  % To address this question, we'll assume that the densities in all of the \formaldehyde 
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% total \formaldehyde column, even though it does not constrain the density  % without a corresponding \twotwo detection.  %  Comparing the integrated \formaldehyde lines to the integrated \thirteenco %  lines, the integrated \formaldehyde column densities are %  $N_{\ortho} = 2.03\ee{12} $ and $1.56\ee{12}$ \persc for G43.16 %  and G43.17 respectively. %  The \thirteenco integrated spectra have brightness $T_{MB} = 2.6$ K and $1.3$ K %  for G43.16 and G43.17 respectively. Using the cloud-averaged excitation %  temperature for this cloud, $\tau_{13}=2.3$ and $0.6$ respectively, so %  \citet{Roman-Duval2010a} equation 3 yields column densities $N_{13} = 6.2\ee{15} %  $ and $1.6\ee{15}$ \percc respectively. Assuming an a \thirteenco  abundance relative to \hh \hh,  %  $X_{13} = 1.8\ee{-6}$ \citep[consistent with ][]{Roman-Duval2010a}, the %  resulting \hh column densities are 3.5\ee{21} and 9.0 \ee{20} \percc %  respectively. The abundances of \ortho relative to \thirteenco are 3.2\ee{-4} %  and 9.8\ee{-4} respectively, or relative to \hh, 5.8\ee{-10} and 1.7\ee{-9}, %  which are entirely consistent with other measurements of $X_{\ortho}$. %These  %are relatively modest column densities, with $A_V=17$ and 4.5;  %these measurements are consistent with \citet{Plume2004a} if the different  %A_V/N(H_2) conversions are corrected.  %  These measurements for a specific cloud validate the statistical argument made %  in \citet{Ginsburg2011a}. %  However, upon closer inspection of the cloud %  morphology, the real explanation may be simple: the filling factor of gas %  within the GMC is small on large scales, not local scales. The implied volume %  filling factor from this analysis and the \citet{Ginsburg2011a} analysis is %  $\sim10^{-2}$; the assumption of spherical symmetry is therefore extremely %  poor. %  This low filling factor has major implications for the gas: if it is in %  gravitational collapse, the free-fall times are shorter by an order of %  magnitude than usually assumed. The long lifetimes of GMCs therefore implies %  that the cloud cannot be undergoing gravitational collapse, but instead %  maintains a turbulent equilibrium. \todo{Strengthen this argument...} %   %  It also demonstrates that density-based star-formation thresholds do not %  independently predict star formation \citep{Parmentier2011a}. Star formation %  cannot simply be driven by the free-fall time of gas, since apparently much of %  the gas above $n>10^4$ \percc is not in free-fall. % 3c111 is in california, not 3c123  % \subsection{Comparison to 3C123 and the California Nebula} 
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% functions of column density that have recently become popular  % \citep[e.g][]{Kainulainen2009}.  \section{Implications for Turbulence} \section{Turbulence and \formaldehyde}  Supersonic interstellar turbulence can be characterized by its driving mode,  Mach number $\mathcal{M}$, and magnetic field strength. Assuming the distribution follows a lognormal distribution, defined as  \begin{equation}  \label{eqn:lognormal}  P_V(s) = \frac{1}{\sqrt{2 \pi \sigma_s^2}} \exp\left[-\frac{(s+\sigma_s^2/2)^2}{2 \sigma_s^2}\right]  \end{equation}  where the subscript $V$ indicates that this is a volumetric density  distribution function.  The with width  of the turbulent density distribution is given by  \begin{equation}  \label{eqn:sigmas} 
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\end{equation}  where $\beta= 2 c_s^2/v_A^2 = 2 \mathcal{M}_A^2/\mathcal{M}^2$ and $b$ ranges  from 1/3 (solenoidal, divergence-free forcing) to 1 (compressive, curl-free)  forcing. forcing \citep{Federrath2010a}.  The parameter $s\equiv\rho/\rho_0$. $s$ is the logarithmic  overdensity, $s\equiv\ln(\rho/\rho_0)$.  The observed \formaldehyde ratio roughly  depends on the \emph{mass-weighted} probability distribution function (as opposed to the volume-weighted  distribution function, which is typically reported in simulations) simulations). We first  examine the implications assuming a lognormal distribution for the  mass-weighted density.  %  such that $p_m(s) = \rho \cdot p_s(s)$, or %  \begin{equation} %  \label{eqn:lognormal} %  p_m(s) = \frac{s}{\sqrt{2 \pi \sigma_s^2}} \exp{\left(-\frac{(s-s_0)^2}{2 \exp{\left(-\frac{(\ln(\rho/\rho_0))^2}{2  \sigma_s^2}\right)} %  \end{equation} %  where we have assumed a lognormal form for $p_m(s)$. Other %Other  forms of the density PDF will be addressed in Section \ref{sec:simpdfs}. We use LVG models of the \formaldehyde lines, which are computed assuming a  fixed local density, as a starting point to model the observations of  \formaldehyde in turbulence. Starting with a fixed \emph{mean} density, \emph{volume-averaged}  density $\rho_0$,  we compute the observed \formaldehyde optical depth in both the \oneone and \twotwo line by averaging over the mass-weighted density distribution.  \begin{equation}  \label{eqn:tauintegral}  \tau(\bar{n}) \tau(\rho_0)  = \int_0^\infty \tau(n) p_m(n) dn \int_{-\infty}^\infty \frac{\tau_p(\rho)}{N_p} P_m(\ln \rho/\rho_0) d \ln \rho  \end{equation}  where $\tau(n)$ $\tau_p(\rho)/N_p$  is computed for the optical depth \emph{per particle} at  a given density, where $N_p$ is the column  density assuming (\perkmspc) from the LVG model.  We assume  a fixed \emph{abundance} abundance of \ortho relative to \hh  (i.e., the \formaldehyde perfectly traces the \hh). Figure \ref{fig:lvgsmooth}  shows the result of this integral for an abundance  of \ortho relative to \hh, which \hh  $X(\ortho)=10^{-9}$, where the x-axis shows $\rho_0 = n(\hh)$ and the Y-axis  shows the observable optical depth ratio of the two \formaldehyde centimeter  lines.  %% pp removed: we don't really need to worry about the effect on the column density  %% for the theoretically computed plot since we have constraints on the column...  %which  necessarily implies a higher column %column  density of \ortho for the higher densities in Equation \ref{eqn:tauintegral}. %\ref{eqn:tauintegral}.  As long as the \formaldehyde lines are optically thin, this %this  approach should yield the right \emph{ratio} of the two lines, although the absolute %absolute  optical depths may be substantially smaller because of lower total \ortho %\ortho  columns. An example of this smoothing is shown in Figure \ref{fig:lvgsmooth}.  \Figure{figures/lognormalsmooth_density_ratio_massweight_logopr0.0_abund-9.png} %\ref{fig:lvgsmooth}.  % /Users/adam/work/h2co/pilot/plotcodes/lognormal_density_massweighted.py  % path: /Volumes/disk5/Users/adam/work/h2co/pilot/figures/models/lognormalsmooth_density_ratio_massweight_logopr0.0_abund-9.png  % cp ~/work/h2co/pilot/figures/models/lognormalsmooth_density_ratio_massweight_withhopkins_logopr0.0_abund-9.png figures/  % cp ~/work/h2co/pilot/figures/models/lognormalsmooth_density_ratio_massweight_withhopkins_logopr0.0_abund-9_withG43.png figures/  \Figure{figures/lognormalsmooth_density_ratio_massweight_withhopkins_logopr0.0_abund-9_withG43.png}  {The predicted \formaldehyde \oneone/\twotwo ratio as a function of \emph{mean} volume-weighted mean  density for a fixed abundance relative to \hh $X(\ortho) = 10^{-9}$ and \hh  ortho/para ratio 1.0. The legend shows the effect of smoothing with different  lognormal mass distributions as described in Equations \ref{eqn:sigmas} Equation \ref{eqn:sigmas}. %  and \ref{eqn:lognormal}. The solid line, labeled LVG, shows the predicted ratio with no smoothing. smoothing (i.e., a $\delta$-function density distribution).  The blue errorbars show the G43.17 \formaldehyde measurement and the GSRMC  43.30-0.33 mean density.  }  {fig:lvgsmooth}{0.5}{0}  \subsection{Turbulence and GRSMC 43.30-0.33}  We use the density measurements in GSRMC 43.30-0.33 to infer properties of that  cloud's density distribution.  We measure the abundances of \ortho relative to \thirteenco,  $X(\ortho/\thirteenco) = 3.2\ee{-4}$ and 9.8\ee{-4} for G43.16 and G43.17  respectively, or relative to \hh, 5.8\ee{-10} and 1.7\ee{-9}, which are  entirely consistent with other measurements of $X_{\ortho}$ and allow us to use  Figure \ref{fig:lvgsmooth} for this analysis. The observed formaldehyde line  ratio $\tau_{1-1}/\tau_{2-2} \sim 6$, while the volume averaged mean density of the cloud $8  \lesssim \rho_0 < 150$.  Assuming a temperature $T=10$ K, consistent with both the \formaldehyde and CO  observations \citep{Plume2004a}, the sound speed in molecular gas is $c_s=0.25$ $c_s=0.19$  \kms. The observed line FWHM in G43.17  is 0.95 \kms for \formaldehyde and 1.7 \kms for \thirteenco 1-0, so the Mach number of the turbulence is $\mathcal{M} \approx  3.8-6.8$. Assuming the thermal dominates the magnetic pressure ($\beta>>1$),  the allowed values of $\sigma_s$ range from 1.6-2.0 for $b=1$ and 1-1.3 for  $b=1/3$. If magnetic pressure is significant, the allowed values of $\sigma_s$  drop. 5.1-9.1$.  If we assume the density distribution is lognormal, we can determine the values  of the `compressibility coefficient' $b$ from Equation \ref{eqn:sigmas}.  Assuming the thermal dominates the magnetic pressure ($\beta>>1$),  the allowed values of $\sigma_s$ given the line-width based limits on  $\mathcal{M}$ range from 1.8-2.1 for $b=1$ and 1.2-1.5 for $b=1/3$. If  magnetic pressure is significant, the allowed values of $\sigma_s$ drop.  Given that the observed mean cloud density is $n(\hh)\sim10^2 $n(\hh)\lesssim10^2  \percc$, Figure \ref{fig:lvgsmooth} shows that only the most extreme values of $\sigma_s$ can  explain the mean density. Even if the cloud is extremely oblate, e.g. with a  line-of-sight axis $0.1\times$ the plane-of-sky axes, $\sigma_s \gtrsim >  1.5$ is required.  These In order to achieve a self-consistent mass and volume PDF, we use the  \citet{Hopkins2013a} distribution with the fitted relation $T = 0.25 \ln  (1+0.25 \sigma_s^4 (1+T)^{-6}$.  Using the $\sigma_s=2.5$ distribution, which is just consistent with the  observations, $T=0.29$, and based on \citet{Hopkins2013a} Figure 3, the  compressive Mach number $\mathcal{M}_c ~ 20 T \approx 5.8$. Compared to the   mach number restrictions from the line width, this $\mathcal{M}_C$ implies a  compressive-to-total ratio $b > 0.6$.  % In [74]: hopkins_pdf.T_of_sigma(2.5)  % Out[74]: 0.2906836447265763  %   % In [75]: hopkins_pdf.T_of_sigma(2.5) * 20  % Out[75]: 5.813672894531527  %   % In [76]: hopkins_pdf.T_of_sigma(2.0)  % Out[76]: 0.22018342653110817  %   % In [77]: hopkins_pdf.T_of_sigma(2.0) * 20  % Out[77]: 4.403668530622164  The  restrictions on $\sigma_s$ using either assumed density distribution  are strong indications that compressive forcing must be a significant, if not dominant, mode in this molecular cloud. % Since magnetic fields have the  % opposite effect of compressive turbulence on the density distribution, magnetic  % fields cannot explain the observations.  %  If magnetic fields are in balance with %  or dominate thermal pressure in this cloud \todo{Look at Crutcher's measurements of B-field with Zeeman OH  observations}, cloud,  $\beta\gtrsim2/3$, the forcing must %  be predominantly compressive, with $b>0.8$. $b>1/3$.  % Crutcher & others seem not to have detected Zeeman splitting in this cloud  All of the systematic uncertainties tend to require a \emph{greater} $b$  value. Temperatures in GMCs are typically 10-20 K: warmer temperatures  increase the sound speed, decrease the Mach number, and therefore decrease  $\sigma_s$. Stronger (i.e. non-negligible) magnetic fields decrease  $\sigma_s$.  \Figure{figures/lognormalsmooth_density_distributions_sigma2.0.png}  {Example volume- and mass-weighted density distributions with $\sigma_s=2.0$.  The vertical dashed lines show $\rho = 15$ and $\rho=10^4$, approximately  corresponding to the volume-averaged mean density of GRSMC 43.30 and the  \formaldehyde-derived density}  {fig:distributions}{0.5}{0}  % For G43.16-0.03:  % {'std': 0.11326554809612598, 'med': 4.3331511700465288, 'CI': [9368.5009376639355, 21535.312095032383, 11915.556844413139], 'quantiles': {2.5: 4.08517676736491, 97.5: 4.5244074037392616, 75: 4.4038773064784564, 84.134474606854297: 4.4358245264528868, 50: 4.3331511700465288, 15.865525393145708: 4.2122132860734167, 2.2750131948179195: 4.078075142949765, 25: 4.2563141956721111, 97.724986805182084: 4.5273461301744797}, 'mad': 0.10889113037076946, 'mean': 4.3251999406282646, 'logCI': [0.24797440268161886, 4.3331511700465288, 0.19125623369273281]}  %   %  \subsection{Simulated PDFs} %  \label{sec:simpdfs} %  Real turbulent PDFs are not truly lognormal, though often they are %  well-approximated as lognormals. We have used some of the PDFs from %  \citet{Federrath2012a} to perform additional smoothing and determine %  whether deviations from lognormal can explain the observed density contrasts. %   %  To perform the smoothing, we converted the simulation's volume-weighted PDF to %  a mass-weighted PDF using Equation \ref{eqn:lognormal} and used an identical %  PDF shape for each mean density (i.e., we kept the shape of the PDF the same %  but changed its mean for use in Equation \ref{eqn:tauintegral}). Results of this process %  are shown in Figure \ref{fig:rescalepdfs}. \FigureTwoAA{figures/federrath_pdfs_volume_mach10.png}{figures/federrath_pdfs_recentered_massweighted_fitted_mach10.png}  {PDFs from \citet{Federrath2012a}. (a) Volume-weighted PDFs for various  simulations with $\mathcal{M}=10$. (b) Mass-weighted PDFs from the same  simulations as (a). These PDFs have been recentered such that they  have a % ~/work/h2co/simulations/federrath_pdfs.py  % cp ~/work/h2co/simulations/VolumeVsMassWeighting.png figures/  %\Figure{figures/VolumeVsMassWeighting.png}  %{Mass-weighted  mean overdensity $s=0$.  }{fig:rescalepdfs}{1}{5in}  In order to simplify the application of these PDFs to the LVG models, we fit  the asymmetric distributions with the sum of two lognormals with different  means. This approach allows density vs volume-weighted mean density  foran easier exploration of parameter space.  An example demonstrating that two lognormals is  a good approximation of one variety  of %turbulent distributions. The black dashed line shows  the compressive simulations is shown in Figure \ref{fig:fittedpdf}.  \Figure{figures/federrath_mach10_rescaled_massweighted_fitted.png}  {The Mach 10 compressive simulation PDF from \citet{Federrath2012a} is shown in  blue with $\rho_M = \rho_V$  %relation. The other lines show  the best-fit single relationship between $\rho_M$ and $\rho_V$  %for different  lognormal in green widths $\sigma_s = \sigma_{\ln \rho}$  and sum values  of two lognormals in  red. $T$  %in the \citet{Hopkins2013a} distribution.  The two-lognormal approximation is a good fit to measured densities for  the simulated PDF.}  {fig:fittedpdf}{0.5}{0}  To use these fitted two-lognormal distributions, we create new PDFs consisting  of lognormals cloud  %GRSMC 43.30-0.33 are shown  with conservative error bars;  the sample amplitude \& width ratios and vertical bars show  %the 95\% confidence interval for  the same mean  differences as \formaldehyde density, while  the fit in Figure \ref{fig:fittedpdf}, but with %horizontal bars show  the total width  scaled. In Figure \ref{fig:compsmooth} [not included; see below], range $8-150$ \percc,  the reported widths for full range of geometrically  %allowed volume-averaged densities. However,  the ``compressive'' distributions are \formaldehyde density measurement  %is not strictly a mass-weighted density (see Equation \ref{eqn:tauintegral}),  %so  the widths positions  of the wider, lower distribution data  in\ref{fig:fittedpdf}.  Upon further inspection,  this approximation actually does figure are somewhat misleading.  %}{fig:volvsmass}{0.5}{0}  \section{Conclusions}  We demonstrate the use of  a poor job as it  fails to reproduce novel method of inferring  the tails, which are more important than shape of  the peak. density  probability distribution in a molecular cloud using \formaldehyde densitometry  in conjunction with \thirteenco-based estimates of total cloud mass.  Our data show evidence for compressively driven turbulence in a  non-star-forming giant molecular cloud. Such high compression in a fairly  typical GMC indicates that compressive driving is probably a common feature of  all molecular clouds.  \input{solobib}  \end{document}  diff --git a/h2co_maps.tex b/h2co_maps.tex  old mode 100644  new mode 100755  diff --git a/indentfirst.sty b/indentfirst.sty  old mode 100644  new mode 100755  diff --git a/introduction.tex b/introduction.tex  old mode 100644  new mode 100755  index 77051b2..5a3d6ee  --- a/introduction.tex  +++ b/introduction.tex 
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were taken as `follow-up' to the BGPS before it was completed.  This document primarily consists of a number of published papers centered  around a common theme of radio and millimeter observations of the Galaxy, but  without an obvious with  the  common driving question. question being `How do stars form?'  I have therefore added thesis-specific introductions to each section to describe where they fit in to  the bigger picture of this document. I've also included sections describing  work that is not yet published but (hopefully) soon will be. 
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into many lower-mass cores.  The two main competing theoretical extremes to get around this problem are  known as the ``turbulent core'' \citep{McKee2003a,Krumholz2005a,Krumholz2009a,Tan2006a,McKee2007a}  and ``competitive accretion'' \citep{Klessen2000a,Bonnell2002a,Bonnell2004a,Bonnell2006a,Bonnell2008a}  models. In the former, an additional support mechanism, turbulence, prevents fragmentation in massive cores, allowing a single core with $M_{core}>M_J(thermal)$ to form into a single stellar system. By contrast, the competitive accretion model, in its most extreme form, asserts that all stars start their lives as $\sim M_J$ cores which exist within a collapsing cloud. They are then able to accrete additional material from the cloud and grow from their minimum mass to populate the initial mass function (see Section \ref{sec:massfunctions}). Neither theory is presently able to account for feedback from the formed stars.  Massive stars drastically affect their environment when they turn on, which can 
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The mass function of GMCs was determine from CO emission towards the Galactic  plane and in nearby galaxies (e.g., M33) where they can be resolved. The CMF  was measured in nearby ($D<500$ pc)  clouds where 30\arcsec\ beams easily resolve $\sim0.1$ pc cores. However, clumps are only found in large numbers in the Galactic  plane, where distances are uncertain. They cannot be resolved in other  galaxies (or at least, could not prior to ALMA). 
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populations are consistent with a Schechter distribution: a power-law  $\alpha\sim2$ with an exponential cutoff at large masses.  $$N(M)dM = C M^{-2} e^{-M} \left(\frac{M}{M_*}\right)^{-2} e^{-(M/M_*)}  dM$$ Since clusters are not drawn from the same parent distribution as GMCs (which  have $\alpha\sim1$, so $N(M) dM = \sim  C M^{-1} dM$), it is plausible that their precursors are, instead, the intermediate-scale `clumps' observed in the  millimeter continuum. However, the clump mass function has yet to be measured,  so even this first step of determining plausibility is incomplete. 
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therefore provide some of the most useful tools for understanding stars.  As with massive stars, massive clusters are rare. Only a handful of young  massive clusters (YMCs)  are known within our Galaxy, the most prominent being NGC 3603, the Arches cluster, and Westerlund 1 \citep{PortegiesZwart2010}. These  are the only locations in the galaxy known to be forming multiple stars near  the (possible) upper stellar mass limit. Despite their importance,   only a handful of these clusters are known and the population of such clusters is effectively unconstrained. The incomplete knowledge of clusters is due to  extinction and confusion within the Galactic plane at wavelengths where  the stars are directly observable.  diff --git a/macros.tex b/macros.tex  old mode 100644  new mode 100755  index c5e78c0..9b0ad1a  --- a/macros.tex  +++ b/macros.tex 
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\newcommand{\dv}{\ensuremath{\textrm{d}v}}  \def\secref#1{Section \ref{#1}}  \def\eqref#1{Equation \ref{#1}}  \def\facility#1{#1}  %\newcommand{\arcmin}{'}  \newcommand{\necluster}{Sh~2-233IR~NE}  diff --git a/mwe.tex b/mwe.tex  old mode 100644  new mode 100755  diff --git a/outerarm_outflowtable.tex b/outerarm_outflowtable.tex  old mode 100644  new mode 100755  diff --git a/outerarm_outflowtable_mnras.tex b/outerarm_outflowtable_mnras.tex  old mode 100644  new mode 100755  diff --git a/outflowsumtable_mnras.tex b/outflowsumtable_mnras.tex  old mode 100644  new mode 100755  diff --git a/outflowtable_mnras.tex b/outflowtable_mnras.tex  old mode 100644  new mode 100755  diff --git a/preface.tex b/preface.tex  old mode 100644  new mode 100755  diff --git a/regionspectratable_mnras.tex b/regionspectratable_mnras.tex  old mode 100644  new mode 100755  diff --git a/regiontable_mnras.tex b/regiontable_mnras.tex  old mode 100644  new mode 100755  diff --git a/rotating.sty b/rotating.sty  old mode 100644  new mode 100755  diff --git a/sfnewsletter.tex b/sfnewsletter.tex  old mode 100644  new mode 100755  diff --git a/solobib.tex b/solobib.tex  old mode 100644  new mode 100755  index 414c6d4..13b7edf  --- a/solobib.tex  +++ b/solobib.tex 
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\ifstandalone  \bibliographystyle{apj_w_etal} % or "siam", or "alpha", or "abbrv" %\bibliography{thesis} % bib database file refs.bib  \bibliography{bibdesk} \bibliography{thesis}  % bib database file refs.bib \fi  diff --git a/tables_chH2CO/Table1A_Measured.tex b/tables_chH2CO/Table1A_Measured.tex  old mode 100644  new mode 100755  diff --git a/tables_chH2CO/Table1B_Measured.tex b/tables_chH2CO/Table1B_Measured.tex  old mode 100644  new mode 100755  diff --git a/tables_chH2CO/Table2_Inferred.tex b/tables_chH2CO/Table2_Inferred.tex  old mode 100644  new mode 100755  diff --git a/tables_chH2CO/Table3_Derived.tex b/tables_chH2CO/Table3_Derived.tex  old mode 100644  new mode 100755  diff --git a/tables_chH2CO/Table3_Inferred.tex b/tables_chH2CO/Table3_Inferred.tex  old mode 100644  new mode 100755  diff --git a/tables_chH2CO/Table4_Other.tex b/tables_chH2CO/Table4_Other.tex  old mode 100644  new mode 100755  diff --git a/tables_chH2CO/Table_RRLs.tex b/tables_chH2CO/Table_RRLs.tex  old mode 100644  new mode 100755  diff --git a/tables_chH2CO/Table_RRLs75.tex b/tables_chH2CO/Table_RRLs75.tex  old mode 100644  new mode 100755  diff --git a/tables_chH2CO/Table_RRLs76.tex b/tables_chH2CO/Table_RRLs76.tex  old mode 100644  new mode 100755  diff --git a/tables_chH2CO/Table_RRLs77.tex b/tables_chH2CO/Table_RRLs77.tex  old mode 100644  new mode 100755  diff --git a/tables_chboundhii/boundhiitable.tex b/tables_chboundhii/boundhiitable.tex  old mode 100644  new mode 100755  diff --git a/thesis.bib b/thesis.bib  old mode 100644  new mode 100755  index bde3613..c42f6f8  --- a/thesis.bib  +++ b/thesis.bib 
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...
Title = {{On the Star Formation Efficiency of Turbulent Magnetized Clouds}},  Year = 2012}  @article{Federrath2011a,  Author = {{Federrath}, C. and {Chabrier}, G. and {Schober}, J. and {Banerjee}, R. and {Klessen}, R.~S. and {Schleicher}, D.~R.~G.},  Journal = {Physical Review Letters},  Month = sep,  Number = 11,  Pages = {114504},  Title = {{Mach Number Dependence of Turbulent Magnetic Field Amplification: Solenoidal versus Compressive Flows}},  Volume = 107,  Year = 2011}  @ARTICLE{Federrath2010,  author = {{Federrath}, C. and {Roman-Duval}, J. and {Klessen}, R.~S. and   {Schmidt}, W. and {Mac Low}, {M.-M.}},  title = "{Comparing the statistics of interstellar turbulence in simulations and observations. Solenoidal versus compressive turbulence forcing}",  journal = {\aap},  archivePrefix = "arXiv",  eprint = {0905.1060},  primaryClass = "astro-ph.SR",  keywords = {hydrodynamics, ISM: clouds, ISM: kinematics and dynamics, methods: numerical, methods: statistical, turbulence},  year = 2010,  month = mar,  volume = 512,  pages = {A81+},  doi = {10.1051/0004-6361/200912437},  adsurl = {http://adsabs.harvard.edu/abs/2010A%26A...512A..81F},  adsnote = {Provided by the SAO/NASA Astrophysics Data System}  }  @article{Federrath2010b,  Author = {{Federrath}, C. and {Banerjee}, R. and {Clark}, P.~C. and {Klessen}, R.~S.},  Journal = {\apj},  Month = apr,  Pages = {269-290},  Title = {{Modeling Collapse and Accretion in Turbulent Gas Clouds: Implementation and Comparison of Sink Particles in AMR and SPH}},  Volume = 713,  Year = 2010}  @article{Federrath2010a,  Author = {{Federrath}, C. and {Roman-Duval}, J. and {Klessen}, R.~S. and {Schmidt}, W. and {Mac Low}, M.-M.},  Journal = {\aap},  Month = mar,  Pages = {A81},  Title = {{Comparing the statistics of interstellar turbulence in simulations and observations. Solenoidal versus compressive turbulence forcing}},  Volume = 512,  Year = 2010}  @article{Federrath2009a,  Author = {{Federrath}, C. and {Klessen}, R.~S. and {Schmidt}, W.},  Journal = {\apj},  Month = feb,  Pages = {364-374},  Title = {{The Fractal Density Structure in Supersonic Isothermal Turbulence: Solenoidal Versus Compressive Energy Injection}},  Volume = 692,  Year = 2009}  @article{Federrath2008a,  Author = {{Federrath}, C. and {Klessen}, R.~S. and {Schmidt}, W.},  Journal = {\apjl},  Month = dec,  Pages = {L79-L82},  Title = {{The Density Probability Distribution in Compressible Isothermal Turbulence: Solenoidal versus Compressive Forcing}},  Volume = 688,  Year = 2008}  @article{McKee2003a,  Author = {{McKee}, C.~F. and {Tan}, J.~C.},  Journal = {\apj},  Month = mar,  Pages = {850-871},  Title = {{The Formation of Massive Stars from Turbulent Cores}},  Volume = 585,  Year = 2003}  @article{Krumholz2009a,  Author = {{Krumholz}, M.~R. and {Klein}, R.~I. and {McKee}, C.~F. and {Offner}, S.~S.~R. and {Cunningham}, A.~J.},  Journal = {Science},  Month = feb,  Pages = {754-},  Title = {{The Formation of Massive Star Systems by Accretion}},  Volume = 323,  Year = 2009}  @article{Krumholz2005a,  Author = {{Krumholz}, M.~R. and {McKee}, C.~F. and {Klein}, R.~I.},  Journal = {\nat},  Month = nov,  Pages = {332-334},  Title = {The formation of stars by gravitational collapse rather than competitive accretion},  Volume = 438,  Year = 2005}  @article{Plume2004a,  Author = {{Plume}, R. and {Kaufman}, M.~J. and {Neufeld}, D.~A. and {Snell}, R.~L. and {Hollenbach}, D.~J. and {Goldsmith}, P.~F. and {Howe}, J. and {Bergin}, E.~A. and {Melnick}, G.~J. and {Bensch}, F.}, 
...
adsnote = {Provided by the SAO/NASA Astrophysics Data System}  }  @ARTICLE{Federrath2010,  author = {{Federrath}, C. and {Roman-Duval}, J. and {Klessen}, R.~S. and   {Schmidt}, W. and {Mac Low}, {M.-M.}},  title = "{Comparing the statistics of interstellar turbulence in simulations and observations. Solenoidal versus compressive turbulence forcing}",  journal = {\aap},  archivePrefix = "arXiv",  eprint = {0905.1060},  primaryClass = "astro-ph.SR",  keywords = {hydrodynamics, ISM: clouds, ISM: kinematics and dynamics, methods: numerical, methods: statistical, turbulence},  year = 2010,  month = mar,  volume = 512,  pages = {A81+},  doi = {10.1051/0004-6361/200912437},  adsurl = {http://adsabs.harvard.edu/abs/2010A%26A...512A..81F},  adsnote = {Provided by the SAO/NASA Astrophysics Data System}  }  @ARTICLE{Turner1989,  author = {{Turner}, B.~E. and {Richard}, L.~J. and {Xu}, {L.-P.}}, 
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adsnote = {Provided by the SAO/NASA Astrophysics Data System}  }  @ARTICLE{Shepherd2002, @ARTICLE{shepherd2002,  author = {{Shepherd}, D.~S. and {Watson}, A.~M.},  title = "{A Detailed Study of G173.58+2.45: an Intermediate-Mass Star-forming Region}",  journal = {\apj},  diff --git a/thesis.cls b/thesis.cls  old mode 100644  new mode 100755  diff --git a/thesis.tex b/thesis.tex  old mode 100644  new mode 100755  index e4b4292..6690085  --- a/thesis.tex  +++ b/thesis.tex 
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\begin{document}  \input{introduction}  %\input{ch_iras05358} %%%\input{ch_iras05358}  \input{ch_w5}  \input{ch_v2}  \input{ch_boundhii}  \input{ch_h2co}  \input{ch_h2colarge} % need to comment out everything in header of h2co_lowdens.tex for this piece of shit to work  \input{ch_software}  \input{ch_conclusion}  diff --git a/thesis_ginsburg_april19_2013.pdf b/thesis_ginsburg_april19_2013.pdf  old mode 100644  new mode 100755  diff --git a/w5_appendix.tex b/w5_appendix.tex  old mode 100644  new mode 100755