SNAG: Studying Nearby AGN & Galaxies: Post 2

Missed out first blog post? Click here to read it.

This week, the team split into two groups, in order to run two different projects side by side.


Charlie, Phoebe and Ciara worked with the SDSS catalogues, calculating values for characteristics to be studied, including recessional velocity, comoving distance, and star formation rate.Through manipulating the data in Topcat, we were able to add data for luminosity distance, comoving distance and recessional velocity. Topcat has built in functions for some of these, although the calculation for recessional velocity had to be triple checked… 35,000 km/s seemed way too high, but our calculations are correct! Redshift is a no-linear scale, so although we are working with relatively low redshift galaxies, they are hurtling away from us at thousands of kilometres every second.


The manual calculation of star formation rate was rather challenging, requiring several steps and using the flux of each galaxy, taking into account reddening from dust. After reaching some very unreasonable values (10^18 solar masses per year!), Dr Sobral tipped us off about the weird units used in some astronomical measurements that may be present in the catalogues. Although we had been wary of the distance conversions (Megaparsecs to cm), we weren’t aware of the use of “ergs” in the measurement of flux. A quick search of the SDSS Data Products website revealed flux was in fact measured in erg/s/cm^2 and scaled by a factor of 10^17 in the catalogue. Finally we calculated the actual star formation rate, and were able to plot this against the 50th percentile mass estimate for each galaxy…INSERT logMass IMAGEThis shows, on log scales, the Star Formation Rate (SFR) in solar masses per year, and the 50th percentile estimate of mass of each galaxy in log of solar masses (so on the graph it is logged again!). This graph is to be analysed in our lab session next week.


We were also able to make some lovely graphs using various flux ratios. The SDSS measures the flux of four Hydrogen lines (alpha, beta, delta and gamma), along with absorption line flux at 12 wavelengths for 8 different ions. To make ratios of metal to hydrogen as accurate as possible, the ratios should be taken with the closest hydrogen wavelength: for example, NII at 6548 Angstroms should be taken as a ratio with H-alpha at 6563 Angstroms; OIII at 4959 Angstroms should be taken as a ratio with H-beta at 4861, and so on. This reduces the effect reddening might have on the ratios, assuming similar reddening at similar wavelengths. Some examples are shown below.

4959,6548

Each has a characteristic curve with a ‘plume’ coming off it. The last graph has been shown broken down into galaxy types: the curve tends to consist of star forming (including starburst) galaxies, while the plume on the graph is due to the AGN. Similar graphs will be used by the CLOUDY group to overlay our data with the results of the simulation.

Ciara Lithgow

For the second week of our investigation of the nature of various galaxies we decided to split up into two groups. Pascale, Jonathan, Tom and John took finding correlations between our data and the simulations found using CLOUDY software as its main objective. The first part of this involved observing our data and splitting it up into equal sections to analyze each part. We ended up getting a star forming section, a starburst section and an AGN section. Before conducting our analysis, we observed that there were entire areas of our plotted data that weren’t at all covered by the simulations from CLOUDY. We deliberated with the whole group about what these plumes might have been. After copious speculation we concluded that these areas must have been noise in the data.  This was deduced from the fact that the data points in the areas were subject to very large errors making the points unreliable sources of information.

This is our data now that the noise has been removed. The 3 regions we will investigate are clearly visible.

With the data being reduced even further, it was time to move on to the comparison with the simulation. Each individual section was analyzed by a separate member of the subgroup. The starbursts section, concerning galaxies in which the star formation rate has momentarily increased considerably, was analyzed by Tom who observed doubly ionized oxygen within the region as well as a comparison between the alpha and beta lines in the Balmer series.

Upon inspection of these graphs several trends are already visible and could be the source of further speculation during the next couple of weeks.

Jonathan took a similar approach in the observations regarding the star forming galaxies. Holding a broad range of quantities to look at many plots were achieved comparing the data to various simulations in CLOUDY, specifically regarding density, metallicity, age of the galaxy and where possible temperature.

Amongst the most interesting graphs here we respectively have a plot of the comparison with the BB simulation observing temperature, a comparison with the BB simulation displaying metallicity and a comparison with the PLAW simulation displaying metallicity.

Finally, John focused on a similar task in the observation of AGN. An important observation was that the BPASS simulation, as we expected, had barely any common ground with the AGN data. Whilst this was expected due to the nature of the simulation it was satisfying to see that the simulations were working parallel to our expectations. An interesting comparison was drawn between the BB simulation and the PLAW simulation, of which the latter is supposed to be the most accurate for AGN.

The larger metallicity occupies a defined area in the PLAW simulation (the top plot) which is what we expect given the ratios we are plotting. Finally, a comparison was made regarding density and metallicity within the PLAW simulation.

John Pollard

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