Friday, August 12, 2011

New Implementation

I talked with Tim and Justin about my code. The problem seems to be that in my current program, I am overestimating the amount of background noise since I'm calculating noise by averaging the flux from individual pixels. What I should do instead was to average noise from little regions and calculate the noise from the average of those regions. It seems like I'm just working with slightly bigger pixels, but by first averaging the flux from each region, I am able to smooth out my background and ultimately decrease the noise in each image.

So now, I have to code a different approach to finding point sources. It will still use much of what I have already written, but it's definitely the biggest change yet to my code.

Here's a rough outline of what I'm about to do:
I will partition each annulus into little circular regions of data. I will then average the flux in each region, and then find the standard deviation from the regions. This will be used to create sigma. The background mean will also be calculated from the regions. Detection of a point source apart from the main source will determined by if the a region has an average intensity greater than thresh*sigma. My program might pinpoint the specific location of the point source less precisely, but at least it won't detect fake point sources that turned out to just be speckles. The contrast curve will still be created in much the same way. It's just the way I determine background noise that will change the most significantly. 

I'll start working on this next week. 

Thursday, August 11, 2011

Sanity Check

I've been debugging my program for the last few days without much success. I've sent some preliminary testing results to Tim, but the results are definitely not good.

Here are two main problems I noticed:
-The point source finder is too sensitive. It detects almost anything that is greater than thresh*sigma. Unfortunately, there are many pixels in the picture that seem rather "grainy" and are registered as false point sources. I need to somehow eliminate these false point sources.
-The range of delta mags (contrast) for the contrast curves are way too small. The delta mags themselves are also high values, often hovering just below 0. This indicates that my program is not finding too much contrast within the annuli of the star compared to the central flux of the star, which seems intuitively wrong. However, this is most likely caused by the small size of the cropped image. The small images size makes my program calculate the background noise as part of the annulus of the star, which is incorrect as there is light pollution from the star. The problem is somewhat fixed when I increase the size of the cropped image; however, compared to Justin's results the delta mags still seem too small.

Professor Johnson suggested I do some sanity checks on my math yesterday, and I've been trying to figure out what really is going on with the math behind the contrast curves.

I'll post some more if I find anything interesting, but for now, it is just alone time for me and a buggy program.

Tuesday, August 9, 2011

Code Testing

Another colleague, Justin, sent Tim and I a MATLAB fitswriter file, which turns matlab .fig files into .fits files.

I have been setting up Matlab and using it to convert images into .fits files. I am then able to test my code on these new files, and compare my results with those of Justin's. Unfortunately, they don't seem to match up. The images that Justin sent me also seem to focus on objects with large FWHM's, which ultimately means that I obtain less points for my contrast curve because of the way I've implemented my code.

I am currently trying to compile a .pfd document containing the contrast curves I obtained using my code, and comparing my data to Justin's data. Hopefully this will give me a clearer indication of what the differences are between our results, and how I should change my code.