GOC – Week 4

We are so close now to getting our catalogues from the reduced INT data of M16 and M67. We are able to reduce the data with bias subtraction and flat fielding and then fix the headers for use with SExtractor. We can run SExtractor on our images to create a catalogue of stars in the images, however our images in the different filters are still not aligned. We need to use Scamp in order to fix this but we are still having issues with this programme. Turns out the software was not installed properly on the computers in the astrolab and so we had spent a week trying to run something that wasn’t there! So, Harry started the install on Monday through installing atlas, which can take a long time! However, we are still not quite there and without this we cannot perform the astrometric calibrations needed in order to match the stars in the two filters for our colour magnitude diagrams.

While we are waiting for this, we decided to use already reduced data in the form of Hubble data to get some catalogues and some results! Since the INT data is going to be difficult to use for the globular clusters, M3 and M71, we decided to start looking at these with Hubble data instead. I started with looking at M3 and looked at images of this cluster in the infrared and visible filters. I am using two images of the cluster in each filter, shown above, one that looks at the cluster as a whole and one that looks at a zoomed in part of the central region. This will give me a better representation of stars in the cluster, as looking at the cluster as a whole we can see that is hard to resolve the central regions and when we run SExtractor on these images, it only picks up the outer regions of the cluster and cannot find sources in the core. I ran SExtractor on the two images in each filter, with the zero point of the images set to zero. The zero point needs to be determined in order to have a true catalogue to use for colour magnitude diagrams. This catalogue with ZP = 0 can be matched to a catalogue of known magnitudes using Topcat. We can then find the difference of the SExtractor measured magnitudes in our images to the true catalogue magnitude and plot a histogram of this difference in magnitude. From this histogram, we can find the zero point of each of our images by finding the median of this magnitude difference. Initially we took the zero point to be the peak of the histogram and assume that the two images of the cluster in the same filter have the same zero point as the peaks of the histogram appear very close. We didn’t realise the significance that a small change in magnitude for the point would have on our catalogue and upon reflection realise that we need to find the zero point by looking at the median of the magnitude difference as this should not be affected by outliers, as much. The catalogue I am using to match with measured magnitudes is ‘Stromgren photometry of M3 (NGC5272)’ (Massari+, 2016).

I have included the histogram for M3 in the visible filter. The diff_mag is calculated by comparing SExtractor magnitudes from the image with ZP = 0, with V magnitude from the Massari catalogue. The red shows the values for sources in the inner regions of M3 and the blue for sources in the outer regions. The peaks appear to line up and so we took the peak value to be 24.5 and set this as the zero point for our images of M3 in the visible filter. We now realise that looking at the median of the two different regions will give a better value for zero point, and will probably be different for each of the regions imaged. Initially we used the value of 24.5 for the zero-point calculated for both images in the visible and re ran SExtractor with this set as ZP to get a true catalogue for our image. The same was done with the infrared filtered images with the corresponding zero-point found for these. The two catalogues in the two filters, for both regions of M3, could then be matched in Topcat. You can see the grey points that correspond to sources found in both filters.

The large square corresponds to the image of the cluster as a whole and the smaller square in the centre corresponds to our second images that probe the core of the cluster on scales that mean SExtractor can resolve the different sources here. We then plot our final matched catalogue to get a colour magnitude diagram for this cluster. It is now apparent why the small difference between the two zero points of the two different regions is important, it can create a big difference when it comes to looking at our colour magnitude diagrams. The blue points show the catalogue for the outer regions of the cluster, using the image of the whole cluster, and the red points show the catalogue for the inner regions, using the second image that probes the clusters core. We can see a shift between the two main sequences which is probably due to assuming that the two images have the same zero point, hence why we now want to consider the median of our histogram (discussed earlier) in order to get more accurate measurements. We can see that the main sequence is very thin, stars don’t vary in v-I magnitude by more than 0.5 magnitudes and so if we have not measured the zero point to this level of accuracy we cannot be sure if the shift of main sequence between the two regions is due to this zero point measurement (most likely) or whether it is actually telling us about the different chemical compositions in the two regions of M3.

Another improvement for determining the zero point of these images is to, before matching the two catalogues, we can pick the stars we want to use so to make our measurements more accurate. We want to pick stars that are not in over dense regions in the image, otherwise when the stars are matched in Topcat with a tolerance of 1 arc second, we cannot be certain that the matched star is the same as the one we are looking at in our image. Also, we want to look at stars with an average magnitude, we don’t want to be looking at stars that are the brightest as they can be saturated or the lowest in magnitude, as these are not reliable. This should hopefully give us a better estimate for the zero point of our images so that we can produce better catalogues. We will implement and test these methods next week with M3 again and also looking at Hubble data for M71, then hopefully M16 and M67 INT data towards the end of the week.

One other aim for next week is to look at how we will analyse these colour magnitude diagrams. We will look into fitting isochrones to our data, either by eye or using models, time permitting. From this we will hopefully be able to determine an estimate for the clusters age and distance, as well as other parameters. We can convert our apparent magnitudes into absolute using distances to the clusters measured by the Gaia satellite. This shifts the main sequence vertically in our colour magnitude diagrams but results in no change horizontally, as long as all the stars belong to the cluster and thus are at the same distance away. Any stars that move horizontally can be discounted from the catalogue of cluster stars. We can use the isochrones fitting models, that work in absolute magnitudes, to try to get an estimate of the distance to the cluster from the main sequence shift between the isochrones and our measured apparent magnitudes. We plan then to compare this estimate to the values measured by Gaia for each cluster. We are looking into how we can learn about the metallicity of the clusters from their colour magnitude diagrams and know that it is best to look over a large wavelength range in filters to see these metal lines. We know there are more metal lines in bluer filters and so we could construct our colour magnitude diagrams using B-I magnitude instead of V-I to hopefully measure the metallicity of these clusters.

Our result of the week is the colour magnitude diagram of M3. The blue regions in this image shows the inner parts of the cluster sample and the green the outer parts of the cluster. We see that the two parts don’t align correctly due to our assumptions with the zero point that we will amend next week. However, although the plot is not strictly accurate it does produce a nice structure resembling a peacock and so we would like to rename the colour magnitude diagram a peacock diagram! See for yourself the likening between globular clusters and these beautiful birds.

Emma Dodd

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