One of the challenges of observing in the near-IR / optical is field identification. In the case of the Apache Point Observatory 3.5m, the "raw" pointing - i.e., if you enter a coordinate and press "slew" - is usually good to within ~1 arcminute. However, once you're on the target field, it's up to you as the observer to identify the exact location within the 5x5' field to observe. This proves quite challenging in crowded fields, especially with rotation. It is far more difficult, though, in sparse fields when your target is a faint emission line feature - there will be no corresponding light in your guider image. But you still want ~arcsecond pointing accuracy. I think this can be achieved by using the astrometry.net source recognition and field identification tools, but highly constrained to be within ~10' of the target field. The WCS coordinates would then be applied to the guider image, and the target location identified and translated into a telescope pointing offset. This technique is probably not needed for most spectroscopic observations (e.g., of bright point sources). However, for slit-scan-mapping or observations of faint point sources, this approach provides the opportunity to do fast-switching with a bright calibration star so that telluric correction can be provided on a short (~5-10 minute) timescale, as is needed for truly accurate calibration.
Lognormal Density / IPython Notebook
An experiment with log normal density distributions (simple, pretty obvious) and a test of including ipython notebooks in blog posts. To do this, I created a gist (https://gist.github.com/3750781) and used the ipython notebook viewer (http://nbviewer.ipython.org/3750781/) to render the notebook in HTML. It worked pretty well; all I had to do was c&p the source and then change 2 lines of the style (background color -> black, foreground color -> white for the markup... maybe I should do it for the rest too).
Comparison of the derived power spectra and density PDFs for lognormal power distributions. Obviously (if you think about it), the power spectra are always flat for randomly distributed signal. The PDFs change significantly.
Research idea: NIR variation
I really want to know how near-infrared absorption lines correlate with emission lines; understanding this is essential for near infrared calibration.
The emission lines correlate reasonably well, but not perfectly. There are models out there of how these should behave, but they seem to be proprietary and non-free so I have no interest in them - I won't pay before I know they work, and I can't test them. But perhaps more careful measurements of the night sky lines could provide some information about the absorption, particularly in the Brackett-delta and Brackett-epsilon region we've been interested in lately. I'm pretty sure the change in slope observed above is from observing at different airmasses (2.3 vs 1.05)
Research Idea: Stacking Finders
Idea: Stack all of the finders from spectroscopic observations. Finder images tend to be on lower-quality CCDs with no filter, but they frequently produce very deep observations. For example, the open K-band finder on TripleSpec (though it's technically not a CCD).
In order to stack them, you would need to mask out the bad pixels (already done) and compute astrometic solutions for the CCD. Un-warping the images will take some work, but there should be plenty of information available from thousands of observations of different fields to make this computation nearly ideal. Similarly, it should be possible to calibrate different pixels on the imager based on response to 2MASS standards.
Applications? Very deep imaging of spectroscopic targets. Short- and long-term variability (typical finder cadence is ~a few seconds). Deep imaging around stars and galaxies of interest - probably far deeper than you could get with classical observing requests.
This project should be achievable by a motivated undergraduate, but I think the tools for astrometric solutions need to be in place first. Astrometry.net is a great tool for this, but I think operates on spatial scales that are too large. Once basic astrometric solutions are available (e.g., pointing center for the image), I think IRAF tools could be automated to compute the complete solution, which would then be applied to all images.
Calibration might end up being the most challenging component, since there is variable atmospheric emission (absorption) that is not filtered by the finder. Depending on the application, though, large calibration errors may be acceptable. i.e., for deep nebular observations, morphology will be more important than absolute brightness, since the line responsible for the brightness cannot be directly determined. Whereas, for variability, calibration is important, but it can be computed directly from other stars in the field.
#6 on Astro-ph today
IRAS 05358 on astro-ph. Check out my page on the region too.
Acquiring DSS images
This is the way to get DSS images: ESO's batch downloader
Another attempt to get 64 bit python on Snow Leopard
Sam Skillman posted his attempt to get 64 bit python on snow leopard. It worked, but you need to install python with --enable-framework and NOT --prefix and NOT --enable-universalSDK. It is 64-bit only, not universal: universal causes trouble.
apache/php/mysql on snow leopard
This post helped me out
API documentation on agpy
I finally processed agpy through sphinx and made some nice html documentation. http://agpy.googlecode.com/svn/trunk/doc/html/agpy.html
Applescripting out that VIM error
I have a frequent problem where my VIM window is too large for my macbook screen but it fits on my external monitor, so when I unplug the external the VIM file bar gets stuck behind the Mac OS menu bar. It's a huge pain to fix this normally, but I wrote/stole an applescript to fix the problem:
try tell application "Vim" activate end tell tell application "System Events" tell process "Vim" set size of the first window to {1000, 200} set position of the first window to {50, 50} end tell end tellend try