3rd Blog Post: Studying CR7 with ALMA

Following on from last week, we began trying to automate the whole method so that it was easier to determine sources from the slices of the cube quicker and easier. To do this we needed to make edits to the original scripts we were given by David so that it could automatically import data from a table and print the results into a file once we had identified all the possible sources on each slice in GAIA. We began by learning more about coding with the help of Google and after many tried and failed attempts we were able to get it to work so that the script would calculate the signal to noise ratio and the star formation rate for the positions of the potential sources.

We began our analysis of the slices by first using the noise measuring script over 50,000 apertures to give us the best value for 3σ to use to significantly reduce the noise in GAIA and make spotting candidates for real sources an easier job. This script printed off the RMS standard deviation in Jy km/s and counts/pixel – which was used as sigma. We then wanted to extract the X and Y pixel coordinates from the image, to be read by our modified script which creates its own apertures, and measures the CII flux and calculates many other properties of the apertures such as Star Formation Rate (SFR) etc, signal to noise etc.

We used GAIA’s aperture photometry tool to extract X and Y pixel coordinates from any candidate sources because this tool, when calculating the apertures, ‘snaps’ on to the region of highest flux. This will give us the best coordinates possible to use to measure the CII flux through any given aperture using our script. We placed these apertures over every single point, however bright or faint, knowing that regardless of the position the script will measure the signal to noise, and we can later determine a threshold of signal to noise at which we can discard all data below, to maximize statistical chance of the sources we observe being real.

ALMA_3_gif_1
Figure 1: 1-Raw ‘noisy’ data slice. 2-Removing pixel counts below 3σ. 3-Candidate sources. 4-Gaia apertures (PSF)

We individually followed this exact method through each of the ‘slices’ of the ALMA cube, so that we could then compare our results to improve their accuracy and demonstrate the reproducibility of the experiment. From this we were now able to get it to print into a file the coordinates of each source, the flux, luminosity, signal to noise ratio and star formation rate in a table.

This data was then taken and imported into Topcat so that we could make a plot of the cube with all the positions of the potential sources with  signal to noise greater than 3 sigma, 4σ and 5σ. This gave an image like the one below.

ALMA_3_gif_2
Figure 2: 1-3σ cut-off for our sources. 2-4σ cut-off for our sources. 3-5σ cut-off for our sources.

In each slice, we found many sources with 3σ significance, on average perhaps two to three with 4σ, and in only two slices we found 5σ sources, as can be seen in the gif above. Next week, we will be determining a cutoff point, by looking at the negatives of these slices. These negatives allow us to do this because by reversing the image values, all of the actual sources are removed, and the most negative (and hence noisiest) regions may become visible above the 3σ limit. By analysing these negative images we can go on to determine a threshold of S/N at which we will be able to determine our most robust sources.

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