Carrying on from last week’s preliminary plots, we dedicated this week’s lab session to looking at the data in more depth. To start we binned the sources based on red shift and morphology. We have six red shift bins of width 0.6 and three bins of morphology. For the morphology we have decided to combine type 0: point like, and 1: compact, into a single type called ‘compact’, this was done because the structures of point-like and compact galaxies are very similar, the only difference being their size, meaning if there is a correlation between galaxy structure and BHAR, 0 and 1 type galaxies should be the same. We also have five bins of Lyman alpha luminosity which we will be using next week. Lyman alpha luminosity was chosen over using either the X-ray or the radio luminosity because it was not used for the calculations of the BHARs and all AGN are Lyman alpha emitters so all have a value for this.
To make the plots we used a python script, this allowed us to plot all the data along with the median values of each bin on one plot. Taking an average would be more accurate provided we correctly identify outliers but using a median means that outliers do not dramatically affect our results.
An upward trend of increasing X-ray BHAR with red shift can be seen. More interestingly, we seem to see a correlation between morphology and X-ray BHAR within each red shift bin, with only the final bin showing something different. It also seems that irregulars tend to have the lowest average X-ray BHAR, but since the sample size for ‘disky’ galaxies is so small I would be hesitant to draw any conclusion from this.
This plot paints a very different picture to the previous one. Here we can see that, for most of the red shift bins, compact galaxies have lower than average BHARs when they are calculated using radio data. There is also a stronger correlation between red shift and radio BHAR than X-ray BHAR showed.
It may seem odd that calculating BHARs using different wavelengths produces different results but we know that the X-ray and radio data span different time scales so the two accretion rates being different for any one source suggests that there is some evolution of the galaxy that causes the accretion rate to change over time.
It may be useful for us to look at each type of morphology and compare the two BHARs to see if there is a correlation between morphology and how much BHAR changes over time. This is interesting as it would give insight into the evolution of different types of galaxies.
Within our data we have two outliers; a type four, X-ray detected source with an X-ray BHAR calculated at 3547 solar masses per year, and a type three source which was detected in both X-ray and radio with a radio BHAR calculated at 9228 solar masses per year. To put this into perspective, our second largest accretion rates for X-ray and radio BHAR are 22.8 and 69.1 solar masses per year, respectively. For now these have been ignored in our plots as they would skew the results too much. It is possible that these anomalies are simply errors. For example, the type four source was found very close to the edge of the field where the background noise is much less than is accounted for. Another explanation is that these are very bright, very luminous AGN; quasars. Even still, these accretion rates seem too high as for an average quasar you would be looking at an accretion rate of a few hundred solar masses per year, not a few thousand.