blog_log #8

I was excited

To wear my birkenstocks with

Socks when it got warm

The week so far has not been too productive. After OOC meeting last week I went to revise my alignment code as I thought I was stacking, but in fact I was not. However my edits, as simple as they appeared, proved to be a little more involved than I had anticipated. My output_image function seems to be creating .fits files of entirely nans, which is frustrating to say the least. What I confuses me the most is why my procedure works for some data, but not the other. It mostly works for the TESS data. Which may be okay because it just occurred to me that is the data that I will be dealing with for the most part as Cassidy and I are dividing and conquering on our work.

After OOC meeting things started to roll. In a good way. Kim conjured up a solution to my nan image problem. After seems like inf or nan pixel values was blowing everything up. It could be a problem with the some bad pixels as I do not use the masking procedure. However, after implementing it in a night of reduction It helped, but the problem was not completely resolved. The shifting procedure only worked for some images and not others. Kim said it makes sense for it to work with mostly no problems for the I band sets as there isn’t as high a rate of pixel misbehaving. All it took was this fix:

def shift_image(image,xshift,yshift):
    '''
    shift_image
    -------------
    wrapper for scipy's implementation that shifts images according to values from cross_image    inputs
    ------------
    image           : (matrix of floats) image to be shifted
    xshift          : (float) x-shift in pixels
    yshift          : (float) y-shift in pixels    outputs
    ------------
    shifted image   : shifted, interpolated image. 
                      same shape as input image, with zeros filled where the image is rolled over    '''
    image2 = np.copy(image)
    image2[np.isnan(image) | np.isinf(image)] = np.nanmedian(image)
    return scipy.ndimage.interpolation.shift(image2,(xshift,yshift))

After meeting I hunkered down to align and stack everything for astroimageJ lightcurves using the solution above. My astroimageJ results are below.

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The attempt went as well as it could. I think there is a lot of guess and checking going on, which isn’t the worst thing. It would feel nice, though to have a more concrete procedure for making curves. Hopefully the Python routine alleviates some of that. I notice that the BD sources are far fainter in the R-band compared to I. These curves are being done with 1-night stacks. Is there any other way to try and boost the signals of these objects. At what point do we say no more stacking because now there is not enough data.

The other documents requested are attached below:

This is an outline of a properties table:

Do we have the property information for the TESS field. I know we got what we needed for Praesepe from the in-class vizier exercise, but there is no analogous catalog for the TESS field. So how will we get this information for those targets? After running the Buedrolt low-mass targets through the Kraus there is information about the overlapping targets that could be interesting to report on such as the proper motion as well as IDs from 2MASS that could possibly provide other information.