Galactic H2CO Densitometry I: Pilot survey of Ultracompact HII regions and methodology

[ ADS ] [ Full Version ]

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We examined 24 UCHII regions using the GBT to observe the 2-2 line of ortho-Formaldehyde. We measured the local gas density and compared to GMCs and other galaxies. We found densities in GMCs that are 1-2 orders of magnitude higher than the mean density in GMCs, implying that extreme overdensities are common even in "quiescent" (non-star-forming) GMCs. This work was almost entirely my own, but was seeded by Jeremy Darling Some highlight figures from the paper:

A sample spectrum. On the top, black is Formaldehyde 1-1, red is Formaldehyde 2-2. On the bottom, blue is 13CO, red is H77α, black is H110α.

Histograms of GMC mean densities and the densities measured directly from our survey. Note that even for "serendipitious" line of sight GMCs, the densities are MUCH higher than typical GMC densities.

Comparison of the measured column densities of formaldehyde and inferred number densities of H₂.

The optically bright post-AGB population of the LMC

[ ADS ] [ arXiv ]
E. van Aarle, H. Van Winckel, T. Lloyd Evans, T. Ueta, P. R. Wood, and A. G. Ginsburg

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A catalog of post-AGB stars in the LMC, useful in particular because they are at a common distance. Post-AGB stars are quite luminous (> 1000 L_O usually), so easily detected in the LMC.

My contribution to this work was years ago; I worked on the SAGE project for a few months at the University of Denver with Toshiya Ueta. I generated a catalog of post-AGB objects and an online catalog with automatic SED plotter. It was a pretty neat project, but I left before I was able to convince others that my catalog was definitive; nonetheless it was eventually used in this publication. As an aside, that was my first foray into data languages, and I ended up using the Perl Data Language long before I learned of python and before I got a free IDL license.

Characterizing Precursors to Stellar Clusters with Herschel

[ ADS ] [ arXiv ]
C. Battersby, J. Bally, A. Ginsburg, J.-P. Bernard, C. Brunt, G.A. Fuller, P. Martin, S. Molinari, J. Mottram, N. Peretto, L. Testi, M.A. Thompson 2011

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The paper includes a careful characterization of the properties of both dense and diffuse regions within the two Hi-Gal Science Demonstration Phase fields (l=30 and l=59). It demonstrated that star formation tracers are more common at higher temperatures, and that IRDCs exist on both the near and far side of the galaxy, where the difference between near and far is around 5-7 kpc.

I helped develop the iterative background subtraction method and contributed to the discussion and conclusions; most of the work was Cara's but my experience with iterative flux estimation from the BGPS pipeline proved useful.

IMF and number of massive stars per cluster

Some experiments determining what fraction of massive stars form in clusters above a given mass, given a few assumptions about the cluster initial mas function. I'm sure this has been done in papers, but I needed to do the exercise for myself.... and I don't know which papers off the top of my head (but I should add them to this post if/when I find them).

In [1]:

from agpy import imf
figsize(12,8)

reg_gal2cel requires coords & pyregion
Region Photometry requires pyregion
cubes.py requires pyregion for getspec_reg
cubes.py requires pywcs for some subimage_integ,aper_wordl2pix,getspec, and coords_in_image

In [2]:

# From a Schecter mass function with some cutoff, generate a collection of clusters
# Then, determine whether "most" massive stars are in massive clusters or if they're evenly distributed with mass
mrange = logspace(1,5,1000) # from 10 msun to million msun clusters
schec = imf.modified_schechter(mrange,m0=1e4,m1=100) # cutoff at low mass
figure()
loglog(mrange,schec,label="Modified Schecter $m_1=10^2$, $m_0=10^4$")
loglog(mrange,imf.schechter(mrange,m0=1e5),label="Schecter $m_0=10^5$")
loglog(mrange,imf.schechter(mrange,m0=1e4),label="Schecter $m_0=10^4$")
xlabel("Cluster Mass")
ylabel("Fractional Number of Clusters")
legend(loc='best')

Out [2]:

<matplotlib.legend.Legend at 0x10e81b950>

In [3]:

nclusters = 50000
clusters = []
obcount = []
ocount = []
mclusters = []
for rn in np.random.rand(nclusters):
    mcluster = imf.inverse_imf(rn, massfunc=imf.modified_schechter, mmax=1e7, mmin=10, m1=100, m0=1e5)
    cluster = imf.make_cluster(mcluster, silent=True)
    obcount.append((cluster>8).sum())
    ocount.append((cluster>20).sum())
    mclusters.append(mcluster)
    clusters.append(cluster)

In [4]:

close(1)
figure(1,figsize=(18,10)); clf()
plot(sort(mclusters),np.cumsum(np.array(obcount)[argsort(mclusters)])/1./np.sum(obcount),'.',label="$M_0=10^5$ OB Mod Schec")
plot(sort(mclusters),np.cumsum(np.array(ocount)[argsort(mclusters)])/1./np.sum(ocount),'.',label="$M_0=10^5$ O Mod Schec")
semilogx(sort(mclusters),np.array(mclusters)*0+0.5,'r--')
xlabel("Cluster Mass $M_i$")
ylabel("Fraction of Clusters with $M<M_i$")

Out [4]:

<matplotlib.text.Text at 0x10e71b5d0>

In [5]:

imf.inverse_imf(0.99, massfunc=imf.modified_schechter, m1=100, m0=1e5, mmin=10, mmax=1e7)

Out [5]:

7619.788265653388

In [6]:

max(mclusters)

Out [6]:

311567.90346958087

In [7]:

np.cumsum(obcount)

Out [7]:

array([     1,      1,      1, ..., 307506, 307506, 307594])

In [8]:

argsort(mclusters)

Out [8]:

array([40808, 22411, 41334, ..., 23973, 21675, 31040])

In [9]:

nclusters = 50000
Sclusters = []
Sobcount = []
Socount = []
Smclusters = []
for rn in np.random.rand(nclusters):
    mcluster = imf.inverse_imf(rn, massfunc='schechter', mmax=1e7, mmin=10, m0=1e5)
    cluster = imf.make_cluster(mcluster, silent=True)
    Sobcount.append((cluster>8).sum())
    Socount.append((cluster>20).sum())
    Smclusters.append(mcluster)
    Sclusters.append(cluster)

In [10]:

fig = figure(1)
plot(sort(Smclusters),np.cumsum(np.array(Sobcount)[argsort(Smclusters)])/1./np.sum(Sobcount),'.',label="$M_0=10^5$ OB Schechter")
plot(sort(Smclusters),np.cumsum(np.array(Socount)[argsort(Smclusters)])/1./np.sum(Socount),'.',label="$M_0=10^5$ O Schechter")
legend(loc='best')

Out [10]:

<matplotlib.legend.Legend at 0x19f9b3810>

In [11]:

nclusters = 50000
S4clusters = []
S4obcount = []
S4ocount = []
S4mclusters = []
for rn in np.random.rand(nclusters):
    mcluster = imf.inverse_imf(rn, massfunc='schechter', mmax=1e6, mmin=10, m0=1e4)
    cluster = imf.make_cluster(mcluster, silent=True)
    S4obcount.append((cluster>8).sum())
    S4ocount.append((cluster>20).sum())
    S4mclusters.append(mcluster)
    S4clusters.append(cluster)

In [12]:

fig = figure(1)
plot(sort(S4mclusters),np.cumsum(np.array(S4obcount)[argsort(S4mclusters)])/1./np.sum(S4obcount),'.',label="$M_0=10^4$ OB Schechter")
plot(sort(S4mclusters),np.cumsum(np.array(S4ocount)[argsort(S4mclusters)])/1./np.sum(S4ocount),'.',label="$M_0=10^4$ O Schechter")
legend(loc='best')

Out [12]:

<matplotlib.legend.Legend at 0x10e7fead0>

In [13]:

sum(S4mclusters)

Out [13]:

3125486.249456272

In [14]:

# this scenario is where the smallest cluster is a single star, so the exponential turnover is at 1 msun, and the minimum cluster mass is 0.1 msun
# need to go to larger numbers for this one, of course
nclusters = 100000
allclusters = []
allobcount = []
allocount = []
allmclusters = []
for rn in np.random.rand(nclusters):
    mcluster = imf.inverse_imf(rn, massfunc=imf.modified_schechter, mmax=1e8, mmin=0.1, m0=1e4, m1=1)
    cluster = imf.make_cluster(mcluster, silent=True)
    allobcount.append((cluster>8).sum())
    allocount.append((cluster>20).sum())
    allmclusters.append(mcluster)
    allclusters.append(cluster)

In [15]:

fig = figure(1)
plot(sort(allmclusters),np.cumsum(np.array(allobcount)[argsort(allmclusters)])/1./np.sum(allobcount),'.',label="$M_0=10^4 M_1=1$ OB Mod Schechter")
plot(sort(allmclusters),np.cumsum(np.array(allocount)[argsort(allmclusters)])/1./np.sum(allocount),'.',label="$M_0=10^4 M_1=1$ O Mod Schechter")
legend(loc='best')

Out [15]:

<matplotlib.legend.Legend at 0x19f9ddd10>

In [16]:

A = argmin(abs(0.5-np.cumsum(np.array(allobcount)[argsort(allmclusters)])/1./np.sum(allobcount)))
sort(allmclusters)[A]
A = argmin(abs(0.5-np.cumsum(np.array(allocount)[argsort(allmclusters)])/1./np.sum(allocount)))
sort(allmclusters)[A]

Out [16]:

689.71604560136109

In [21]:

%pastebin 0-16 raw=False

Out [21]:

u'https://gist.github.com/3751918'