Searching for Metal Poor Stars-Week 4

Last week we mentioned how by using different filter combinations, regions could be defined and cuts could be applied to the catalogue so that we could restrict all light sources to the ones that are most metal poor. Before this, we provided insight into how theoretical spectra of stars with different metallicities looked on colour-colour diagrams, and how they seemed to follow a trend. With that being said, I would like to start the blog post of week 4 with a picture that demonstrates exactly how these regions look like, and how they relate and can be obtained from the theoretical spectra predictions.


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In the first plot, linear fits thought theoretical magnitudes and colors of spectra with different metallicities are shown, one trend for each metallicity ranging from 0 to -5. From these, regions are selected around the lines as to select light sources that probably have metallicities similar to the theoretical ones used. Straight lines and linear fits were used in this process, as they appeared to have a good correlation with the data. Meanwhile the second image demonstrates the regions with different metallicities when applied to the catalogue we have, hence labeling each light source with its most probable metallicity in this filter combination choice.

After doing this for the colours G-I and B-V, we pythoned our way into creating a script that changes the original catalogue and labels each source with their metallicity. This  makes a cut on the entire catalogue and selects light sources in these regions, hence allowing us to continue our work only with potential metal poor sources (stars and galaxies, for now). The next step was to further reduce the candidates by applying the J-K criteria explained on the previous week, and perfectioning it so it would not excluded good candidates. Joining this restriction to the previous one, we narrowed down our candidates even more, being now able to categorize them in galaxies, stars, and metal poor stars.


final cuts
All the restrictions were applied to the catalogue, thus selecting the metal poor light sources and allowing the distinction between stars and galaxies. Also, the theoretical spectra is kept to see how it still all agrees and is derived from it. Most stars are indeed located in the region we were expecting them to.


After this, we also tried to rule out the white-dwarfs that may be included in the stars. Doing this took a few days, as it required that theoretical spectra were found, calibrated, magnitudes calculated and plotted on the catalogue, so as to determine which cut to perform on the data. Basing our choice of colors and filters on a paper on The Pristine Survey, we found only two potential white dwarfs and removed them from the data. This revealed that the amount of work dedicated to some ideas (like this one, about three days from Alice’s side) may not have the expected outcome, but is still a good step to take and is worth it afterall.


Using the colours U-G and G-R, theoretical spectra of white dwards (blure squares) is ploted on top of our catalogue ( Red points). Based on the somehow linear trend they follow, we decided to exclude the sources in green, but only two of these turned out to be potential metal poor stars and removed from our candidates.


As these processes were performed, we never forgot to think of the physics behind. While struggling to understand, for example, why M and K stars are not likely to be pristine metal poor stars, we had a few Physics Short Lectures with David Sobral , including one about Supermassive Black Holes! After this, we understand why each cut is performed and why some star types are excluded. We get that the more massive stars (e.g. O and B) have such short lifetimes that they could only have formed from a more recent – and therefore more metal-enriched – interstellar medium, so we would expect them to be metal-rich. (Not to mention the metallic products of nuclear fusion that might have been dredged up from their cores to their surfaces.) – credits to Tom. After remembering Jean’s Mass and star formation processes, we know now that M and K  star types aren´t likely to be metal poor.  Also, the J-K galaxy cut is physically a very smart thing to do, as galaxies suffer red-shift and therefore most of their spectra appears to have high fluxes on the J and K filter range, towards the red end of the spectra. On the other hand, stars do not reveal red-shifts high enough to redden their spectra, as to enable significant detection in these fitlers, and hence J and K clearly separate galaxies and stars ( shown in the figures in the last blog post). To help with visualizing and understanding this, we created theoretical plots of spectra of stars in the J and K filters, after dealing with quite a handfull of errors and corrections. These helped us predict that stars should be located bellow J-K magnitudes of 0.0, previously demonstrated when the entire catalogue was observed in this color. (Again, Tom really deserves that we set him up for a future canonization for his miracles in this project ).


toms plot
Ploting K and J-K magnitudes for spectra of stars from ESO databse, their distribution can be studied and the trends extrapolated for future analise of real data. This helped us predict that stars should be located bellow J-K magnitudes of 0.0.

Reaching the end of the week, the criteria used were aproved and some numbers started to arise. Dividing the metal poor stars candidates according to their brightness in the NB392 filter ( that we now know has been named MURPHY , as in Murphy’s Law, after a quite a funny story), we obtained the following numbers of candidates:



The next step is to use Huble Space Telescope’s images to visualise each candidate individually and rule out sources that are not stars, such as galaxies that were not included in our cuts ( as we did not take into account errors in the measurements). To conclude, I leave you with the beautiful images that we’ve had access to so far and that personally make all the hard work worth it at the end of the day.

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