Week 3 saw a slight change to the groups, as Pascale joined Ciara, Charlie, and Phoebe to use python to plot graphs using the SDSS data. The day started with Dr Sobral giving the group a crash course in the python script we had been provided with, showing us how to change the variables plotted, format the graphs produced, and (most importantly) how to debug the script when it inevitably went a bit wrong.
“The 2 Peas in a Pod”, as Pascale and Phoebe were beautifully christened by Charlie and Ciara, set about making graphs that showed the percentages of each galaxy type (AGN and SF) against mass, and then did another series of graphs that showed the galaxy fraction vs element ratios. The ratios used were [OIII 5007/Hβ flux], [NII 6584/Hα flux], [OIII 4959/Hβ flux], and [NeII 3869/Hγ flux]. As this is astrophysics, all these ratios were of course done using a logarithmic scale. These ratios are often used in astronomy and astrophysics as the difference in the wavelengths between the two elements is small, which reduces the possibility of different degrees of reddening due to dust in the local environment of the galaxy.
The mass graph clearly showed that galaxies with higher stellar masses were almost always AGN galaxies, with SF galaxies having lower masses. Most of the element graphs showed clear trends in the way different galaxy types had different element distributions, but the NeII graph showed no strong trend at all. Phoebe and Pascale tried to replot the graph, thinking that there was a problem in the code, but the answer was a lot more fundamental: NeII line ratios were not a good method of distinguishing between AGN and SF galaxies.
Meanwhile, the other half of the group had hit a snag: the star formation rate graphs that were being plotted suggested that 85% of galaxies with an SFR of 0 were in fact those that had been classified as star forming galaxies. As we would expect these galaxies to be contributing most to star formation, this graph obviously threw up some interesting questions. After checking the code for errors, it soon became clear that the calculations for star formation rate were not totally accurate, and that the difference in the number of SF galaxies and AGN galaxies was likely to be skewing the data.
Dr Sobral then suggested that starburst galaxies were included in the conditions, which improved the graph, and Ciara and Charlie figured out that the unexpected shape was due to the fact that SFR is calculated from H-alpha lines, but that AGN can give off large amounts of H-alpha without forming stars. This meant that many AGN galaxies were shown as having high SFR even when this isn’t the case. To rectify this, AGN galaxies were removed, and plots were done of SFR against the fraction of SF galaxies and starburst galaxies instead.
The star formation rate (SFR) of star forming (SF) and starburst (SB) galaxies
For the third week of our investigation, we continued to work in 2 groups.Jonathan, Tom and John continued investigations into what information we could gleam from the CLOUDY simulation and our data set, with each person investigating something unique.
Jonathan began investigations into the physical limits of the CLOUDY simulation. CLOUDY works to predict line spectral data with various inputs such as Temperature, Ionisation power, metallicity, and density. Each CLOUDY Model, BB, BPASS and PLAW use different starting conditions for these simulations, and we expect that most of these starting positions will represent galaxies that are un-physical.An example of this is shown below:
In the graph above, we can see that in grey is all the value CLOUDY predicts using the PLAW model, and in blue are all the values that correspond to real AGN galaxies (determined via comparisons of predicted OIII/HBeta and NII/HAlpha flux ratios). There are many grey values visible here, and all of these correspond to simulation inputs that have no association with real conditions in our galaxy data. Tom looked a bit closer at our data set this week and noticed that there were a significant number of data points who were uncategorised by the SDSS survey.
The initial thought was that perhaps these galaxies were passive (little to no star formation) given that most of these galaxies span across the SF and AGN regions of our galaxy, though upon further investigation it was revealed that most of these values are just data points with an unusually high error within the measurements of their flux. By taking our data sample with a tolerance of the Flux must be greater than 50x the error of the flux, the following graph was created, which reduces the number of unclassified (yellow) galaxies significantly.
John investigated a large range of other quantities with relation to both CLOUDY, and our data set itself. From CLOUDY, it was noticed that there were clear patterns and relations in the Carbon/Oxygen ratios that seemed to match with our data relatively well. While initially the reason for this was unknown, it turns out that this is a good representation of the metallicity of a galaxy.
In addition to this, the SFR (Star Formation Rate) across our data was considered due to the work of Group 1 last week. The results were not what was expected however, with a seemingly uniform distribution appearing across our data. This is likely because of the way the SFR is calculated, using the Hydrogen Alpha emission lines as the measurement. As H-Alpha lines can be emitted in ways other than just star formation, this is the reason that our data set looks uniform, as opposed to the SF regions having the higher SFR,