...
It is very difficult for me to see a correlation. It is especially unfortunate that only the first two months of data had these numbers recorded since most of those images are in focus anyway.
Update 4/2/15
Beginning analysis of entire set
I let the code run continuously for a few days, to see how many of the files it could analyze and to see if any errors need to be fixed in the code.
For each day of data (~1000 images), it analyzes every image and looks for a complete spectrum of Polaris. If a full spectrum is found, it goes through all the steps previously described to extract the equivalent widths of the six features (Ox A, Ox B, and 4 water features). For each day, it writes a text file with each equivalent width. It then calculates the average equivalent width for each feature over the entire day, along with a purely statistical uncertainty, and keeps that info in a separate text file.
Below I plot the average equivalent widths of the six features over a ~three month period (took three days of computation on my Mac).
The two plots are over the same time period, I simply divided the six lines into two plots for clarity (Ox A is in both for reference).
I think a promising sign from these plots is the correlation in the water lines. While the oxygen lines stay constant over time, not only do the four H2O lines vary significantly over time, but they generally vary in the same way. This makes me think the code is doing a good job at extracting the equivalent widths without too much noise.
A few tweaks to do before applying the code to the entire set:
- Only ~20% of the images have a complete, "good" spectrum with all features in the CCD. Is there a way to increase this number? I.e., can we make the "chopped" or cut-off spectra described in previous entries useful?
- Decrease noise by improving how the algorithm calculates dλ/dpixel. It currently calculates dλ/dpixel by finding Ox A and Ox B in pixel space, and using their known wavelengths. I have confidence that the code finds Ox A very well, but about ~10% of the time it fails to find Ox B. Matt gave a few suggestions for improving how to find Ox B (e.g. cross correlation with typical "good" spectra) that I can quickly implement to improve efficiency.
- Compare three different methods for finding equivalent width of a certain line:
- My current method (using a spline as the continuum; probably the most accurate but computationally intensive)
- fit a sloped line using the spectrum just before and just after the feature, and use that line as the continuum
- fit a flat line, i.e. same as above except simply use median of neighboring points. Computationally quick but probably less accurate since some water features are at high slope.
Update 4/6/15
FWHM of all files
I collected all header info and FWHM of every file in the set (sans darks and flats). Below is a scatter plot of FWHM vs date for all files:
(The two plots are the same data, just different y-scales). The bottom plot shows that there is still plenty of good data even towards the end, though the top plot shows that the number of out-of-focus images does increase with time. There may even be some periodicity in the FWHM going out of focus.
I was not sure how to best display such large datasets (~800k data points here), so if there are any suggestions feel free to let me know.
I will start analyzing the equivalent widths of all files tonight.
Update 4/14/15
Filters
Each file has 5 different filter numbers that are stored both in the header and in the file name. Below are examples of the filters; all these images are within the same few minutes
- Filter 0: no filter
- Filter 1:
- Filter 2
- Filter 3
- Filter 4
Filter distribution over all images:
In the first few months + last few months, the images alternate between all 5 filters. There is no date for most of 2012, and all the data in 2013 only uses filter 4.
To-do:
- find out the wavelength cut-off for these filters, which will make the analysis much easier and computationally faster.
- Analyze all images using filter 4, performing a polynomial fit on the four passes in each image
Equivalent widths for all filter 0 data
As shown above, the only data with no filters are the first few months and last few months. First I plot all the filter 0 data, to illustrate the gap in data. Then I follow with the data from the first few months and last few months individually.
Note: These data are averages over entire days, i.e. each data point is the average equivalent width of that line over all images in that day. There are associated statistical uncertainties, but for clarity I have not plotted them below. I will plot them in a different section.
In total:
First few months: this is mostly similar to the plot under the section "Beginning analysis of entire set"
Last few months: this is all new data.
The first thing I noticed is the behavior in all lines starting at the end of May 2014. The stable Ox A and Ox B lines sharply dip down, and oscillate. The water lines also begin to oscillate but do not dip down. I plan on looking into this by looking at the physical images and checking if my algorithm ran into an issue here.
Equivalent widths for filter 0 data, with statistical uncertainties
These are the same as the plots before, but now with error bars that represent the standard deviation associated with the average taken over every day.
First few months:
Last few months:
