Next Generation Astronomy

A brief commentary on a recent article Next Generation Astronomy :

I'm probably one of the folks who will be working on making that future happen, in the sense of being the intermediary between the end-user and the instrumentalist. Scientists are still needed to build instruments, but those who build the instruments don't necessarily have the time or expertise to build the software pipelines that generate high-quality data.

At the same time, the STARLINK folks do a much better job than I do, and I already spend about half of my time comparing different archival data sets... so I think his crystal ball is overall very accurate.

Converting a CLASS-created .fits file to a real (FITS-compliant) FITS file

This post is to remind me, the next time I go looking, how the hell to convert from a GILDAS CLASS fits spectrum (created by fits write blah.fits /mode spectrum) to a FITS-compliant spectrum.

First, remember the FITS-WCS spectral definitions: http://www.aanda.org/index.php?option=com_article&access=bibcode&Itemid=129&bibcode=2006A%2526A...446..747GFUL


And the peculiar CLASS definitions: http://iram.fr/IRAMFR/GILDAS/doc/html/class-html/node84.html

Key points:
CLASS stores the CDELT parameter as DELTAV in m/s instead of km/s and the velocity offset of the spectral frame in VELO-LSR also in m/s.

Things to set:
CTYPE = VRAD
SPECSYS = SOURCE
SSYSSRC = LSRK
VELOSYS = frame velocity (VELO-LSR or CRVAL1)

This information is subject to change...

Observing @ GBT: Signs of good & bad data

So far, all of the observations for the H₂CO densitometry project have been performed at the Green Bank Telescope. During a 10-day long observing trip here, I've learned a lot about diagnosing bad data.

This first image shows TSYS vs Airmass for good data. The high outliers are just sources with continuum in them - the continuum is the source of the extra signal, not atmosphere.  The receiver temperature is a nice 20.6 K, and you get about 5 K extra per airmass, suggesting a zenith optical depth of 0.018 assuming a round atmospheric temperature of 300K.

In the same style plot, there is a set of observations with low system temperatures: that stuff is good. There is also a set with clearly rising system temperatures, even at constant elevation. These data are bad. During this observation, the "blowers" that are meant to keep dew off of the receivers failed. Dew buildup on the receiver covers lead to higher optical depths and therefore system temperatures.

Finally, this data set was totally useless. Ku-band is not particularly sensitive to water in the atmosphere... but it's still not a good idea to observe during a rain storm.  Note that the fitted receiver temperature TREC is nonsensical.

Galactic Formaldehyde Densitometry Project

The Galactic Formaldehyde Densitometry Project is finally official and on its way. I am PI of a joint Arecibo / Green Bank Telescope project to survey formaldehyde, H2CO, in a sample of ~400 "dust clumps" in the Galactic Plane. The clumps represent condensations within larger molecular clouds that are likely to be forming stars, particularly massive stars and star clusters.

Formaldehyde is uniquely useful for measuring densities of these gas clumps. Because of a collisional selection effect, the low-lying K-doublet transitions of H2CO undergo "anti-inversion", which means their lower-energy states are overpopulated relative to the higher states, and it can therefore be seen in absorption against the cosmic microwave background. This unique property removes a key bias present in emission line studies in which variable excitation conditions can dominate the observability of a line.

For the next few posts, I will include calibration and data reduction information from the observations in this survey. The Green Bank observations are well underway, with nearly half of the sources observed (though many at poor sensitivity) and the Arecibo observations have been approved.

Neat new things....

1. sptool is a quick way to compare standards to stellar spectra. Nice, I'd been looking for a tool like that.
2. GNU screen captions are useful especially when working in a screen-within-a-screen environment (who does that, really?)
3. finally got SPLAT to work... turns out I just hadn't reduced my damned data
4. kill -STOP and kill -CONT are really useful ways to pause programs that are sucking up resources if you want to resume them later. Haven't tried this on "real" code yet.

Filled step plots in matplotlib

It's not possible to do a simple filled step plot in matplotlib using default
commands. Workaround:

def steppify(arr,isX=False,interval=0):
    """
    Converts an array to double-length for step plotting
    """
    if isX and interval==0:
        interval = abs(arr[1]-arr[0]) / 2.0
        newarr = array(zip(arr-interval,arr+interval)).ravel()
        return newarr

plot(xx,yy,linestyle='steps-mid',color='b',linewidth=1.5)
fill_between(steppify(xx[x1:x2],isX=True),
    steppify(yy[x1:x2])*0,
    steppify(yy[x1:x2]),
    facecolor='b',alpha=0.2)

My starred reader articles

Just posting up a few papers I found interesting.

Peng Wang and Tom Abel's paper on outflow feedback in clusters
A somewhat less interesting follow-up to the previous

HARP mapping of the Serpens cloud core

Neal Evans' review of low mass star formation observations

the MNRAS paper on supermassive stars in the LMC

Identification of molecular clouds from the FCRAO OGS