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  • I can process data and produce plots now. Here are some light curves:
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Thursday, 20 June

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  • Plotted the color diagram for all the objects in stacked gpc1v3. The diagram gets too cluttered if I plot all the objects, so I limited it to points where the error bars are small (<0.002 mag).
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Stubbs comments, June 30 2013.

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Ok I guess my main question now is, what kind of plots should I be making now?

 

Jul 31

As far as I understand, my tasks now are the following:

  1. Use stacked images from photpipe to build a catalogue of stars with low error in all bands, no close neighbors and are close to the stellar locus.
  2. Check how (airmass corrected) stellar color in that catalogue change with time, and if so if that change is dependent on median stellar color.

So currently I am very close to attaining the first goal. The second goal is a bit tricky because of the mass of data I have to churn through, but hopefully I'll be able to get the code right soon.

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Stubbs' comments July 31 2013:

I'm unclear on whether you're still working with MDF images or have shifted over to NCP data. The advantage of the latter is that apart from clouds the magnitudes should be essentially the same from night to night, and so the effects of water content variation should be easy to pick out. Here's a re-iteration of what I sent via email on July 26:

1) make sure you can identify a common set of stars that have many data points
2) extract a table of magnitudes vs. time. The g and r bands should be insensitive to water, while i,z,y 
3) find the instances of data being taken in photometric conditions (a common stable zeropoint)
4) see what the range in airmass is, since the field of view is a few degrees, and plot colors vs. airmass. Correct if necessary
5) then plot the set of airmass-corrected colors vs. time, and look for variations on the timescale of a few days that might be
due to water vapor.
6) for each star, determine the median colors (r-i), (r-z), (r-y), and (z-y). Plot the departures from the medians as a function of 
the median color. Do redder stars have bigger excursions? 
7) make some SLR plots and see if there is a measurable signal there.
I think the specific response to your question about what to plot is in items 5-7.  

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Aug 2

I was working with MDF data but not I'm switching over to NCP data. For MDF data, I have been able to identify a common set of stars (with low errors in all passbands and no nearby objects) and collect data about them over time (from the IPP data). I identified two objects to be identical if they are within 1 pixel of each other, but that condition seems to be too tight (I'm not getting much data at all) and I'm going to relax this to 5 pixels. This shouldn't be a problem because the stars are not supposed to have any neighbors within 20 pixels. The code for that is currently running and might take some time. I have also written code for plotting data, but right now there isn't much to plot because I set the identification condition to be too tight.

Anyhow, I'm pretty sure the code is working, so I'm going to go ahead and look at the NCP data. Identifying a common set of stars from that data is probably going to be more difficult because we don't have stacked images, but my idea now is to look for a few "good" images taken in photometric conditions and identifies the stars from there. Will try this tomorrow.

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So I have been playing around with MDF data for a while more because the stacked data from photpipe produces a nice catalogue, and that trims down the amount of data I have churn through by a lot. Anyway I found the color residue (I used instrumental colors minus the colors calibrated using SLR) against g-r calibrated color. There does not seem to be much of a trend at all:

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I limited the data to only points with small error bars, but still there's no trend:

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So this seems to suggest that the error in color is not a function of stellar color? I will try this again with unstacked ipp data soon.

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Aug 22

I've tried looking for the effects of water vapor (greater deviation from mean i-z color for redder stars) in the unstacked data, but found nothing. Here's the graph (only stars which have no close neighbors and accurately calibrated colors from stacked data were plotted):

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To ensure that this is no fluke, I looked at the changes in stellar color of a particular star (chosen because we have many observations of it). I found that on a single night, the g-r color (which is not supposed to be affected by water vapor) varied as much as (if not more) than the i-z color (which water vapor is supposed to affect):

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I also checked for the possible effects of airmass. That does seem to affect anything either:

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So unfortunately, I haven't found any evidence that water vapor affects stellar color (or I guess I should say, I have not found any evidence that water vapor is the limiting factor on the precision of PS1 photometry) so far (sad)

I made a presentation of my results for PRISE, and the powerpoint is available here: https://www.dropbox.com/s/7larxgd1jiun4k5/gutter.pptx

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Data access on Odyssey:

  1. Run JAuth.jar to get login key
  2. ssh -Y into to odyssey.fas.harvard.edu, or herophysics.fas.harvard.edu, using the electronic key. 
  3. run tcsh
  4. source .myrcstubbs
  5. data are at /n/panlfs/data/MIRROR/ps1-md/gpc1/
  6. nightly science uses individually warped images, nightly stacks run on stacked frames
  7. image types: wrp is warped. 
  8. see available modules with "module avail"
  9. load a module with "module load hpc/ds9-5.6"
  10. photometry is in .cmf files, as FITS tables. 
  11. in python: 
    1. import pyfits as p
    2. p.open('filename')
    3. print a[0].header
  12. or, imhead on command line
  13. a[1].data.AP_MAG for aperture magnitudes
  14. PSF_RA and PSF_DEC are in the skycell files. 
  15. make a scratch directory for data in /n/panlfs

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