Tomoe Note 007: Full reduction of 20160411 = April 11, 2016

Michael Richmond
Apr 28, 2017

Executive Summary

The dataset for 20160411

The dataset "20160411" is based on images acquired during UT 2016 Apr 11. The images cover the span of time

    2457490.00034  ≤   JD  ≤ 2457490.27145

which cover about 6.6 hours.

The raw Tomoe "composite" files have names that look like:

    chip 0:   TMPM0108830.fits      -     TMPM0110080.fits
    chip 1:   TMPM0108831.fits      -     TMPM0110081.fits

    chip 7:   TMPM0108837.fits      -     TMPM0110087.fits

I'll refer to each of these "composite" files as a "chunk". For example, chunk 108840 is the second set of 360 FITS images collected by chip 0. A reference like "chunk 10884x" will refer to the data collected by all 8 chips during the period of time covered by chunk 108840.

There are 126 chunks for each chip, (11008x - 10883x), and so 126*8 = 1008 chunks in total for the night. The total size of the raw Tomoe composite files for this dataset is 1.7 GB.

"Weather" plots for the 8 chips

After running the pipeline to reduce the data, clean the images, find and measure stars, calibrate them astrometrically and photometrically, I used a script to look at properties of the data over the course of the night. You can read more about the "weather" in another note.

Below are links to the graphs produced for each of the 8 chips.

Below is an animated GIF showing all 8 graphs in succession; it loops over them repeatedly.

What do we learn from the "weather" graphs?

The primary -- and good! -- conclusion is that the properties of images in all 8 chips vary together: when one reports better seeing, they all report better seeing, for example. That confirms that the hardware is working properly.

Let's go through the panels of these graphs, one by one, and comment on features of interest.

Other information from the night

I made a histogram showing the distribution of magnitudes for all detections during the night. This is not a count of unique stars: if a star appeared in 360 images, it will contribute "360 units" to the graph below. But the distribution of magnitudes is clear.

An independent method of determining the limiting magnitude for one chunk of data (in the transient-finding routine ) typically estimates a limiting magnitude of 14.5 to 16.0. That agrees pretty well with the data shown here.