The previous two blog posts describe our work for the first two weeks of the project, where it’s easy to understand that so far we haven’t worked with the real data taken from the Cosmos Survey with the Isaac Newton Telescope. At first we were very confused by this, not really understanding the point or usefulness of all the simulations and scripts we were running. Also, since the beginning of the project that we knew it would be split in two: theoretical and observational; but we never really knew how both aspects were related. Two weeks later, we’ve started to realise the importance of all the work we’ve done so far, and that idea is what I’ll try to pass on with this post.
Remember our main goal for this internship? Finding metal poor stars from a Wide Field Survey using a Narrow band filter i.e. pointing a telescope to one of the biggest fields in the celestial sky- the Cosmos Field, and using a very specific and small wavelength range filter that looks at Ca H&K absorption lines to detect metal poor stars in the halo of our galaxy. The result of this survey is a catalogue with a bunch of numbers, that for a group of novices like us, mean almost nothing and do not tell us anything about their metal content. In a more scientific perspective, we do know these numbers represent magnitudes in several bands, taken from measurements of each light source’s spectrum. Therefore, the most logical step to follow next is identify the characteristics of a metal poor star’s spectrum and what distinguishes it from a regular star’s, and use these to predict how the magnitudes of such star would look like. In this way, we can look at the data and compare it with the predicted magnitudes, in order to identify which light sources could potentially be metal poor stars.
Here is where our work goes in: we took advantage of different spectra provided online and used our almost non-existent python skills to recreate the steps that lead to the calculation of magnitudes for different filters and estimation of colours. From online databases such as the Pollux one, which uses MARCS atmosphere models and Turbospectrum (Plez, 2008; Alvarez & Plez, 1998), we had the chance to produce artificial spectra of stars with whichever characteristics we pleased. Logically, we opted for choosing different metallicities for all spectra types ( the metallicities used were 0.0, -1.0, -2.0, -3.0, -4.0 and -5.0). After python started to work on our side (or us on its), the steps I mentioned were performed and are explained in the previous blog posts, leaving the following plot as a final outcome.
Plots like this are called colour-colour or colour-magnitude graphs, and by using certain filter colour combinations, one can differentiate several characteristics of stellar spectra. Here we had another important goal, figure out which colour combinations make the characteristic we want to study -metallicity- stand out the most . After a few experiments with filter combinations on TopCat, we concluded that, at least for now, the G-I and the U-B colours were the most suitable for the task. As can be seen from the graph bellow, each metallicity and spectral type was labelled, and a pattern for metallicity scattering is visible: it falls perfectly on straight lines for low metallicities and is well fitted, but with a larger standard deviation, for higher metallicities (i.e 0.0 category).
Current Situation: we have an idea of how to predict magnitudes and colours for different spectra and metallicity. Furthermore, we realised that the scatter is independent of source distance, when interstellar extinction is not considered, after plotting the above graph for light sources at a half halo distance (200 000 pc) and obtaining an exact replica of this graph. Here is where the link between observational and theoretical spectra takes place, because now we can take these regions where metal poor magnitudes are located and place them on top of real data, hence providing a cut on entire catalogues and restricting all light sources to the ones that are most likely to be Metal poor Stars. That was our job for the 3rd week, and for a first try we obtained the following cut.
Next steps to follow are to analyse theoretically more spectra with more detail, in order to perform bigger cuts and restrict even more the data and hence make it easier to locate Metal Poor Stars. Just to give an idea, we started with around 123,505 light sources and so far, with the cut shown above, we have 23880 potential stars (we know that most of them are likely to be galaxies). If we keep on studying characteristics of metal poor stars, we hope we’ll be able to reach a more reasonable number and analyse each potential star individually.
On a personal note, progress has been slow. Each day it looks like we are one step closer, or two, but with some struggle. We’ve got the hang of most programmes that we need to handle, but sometimes we hit a wall and it looks like we got lost. On the following day though, we surpass the problem and keep going. Again, progress is slow, but there is progress, and from seeing other people on the department we realise that maybe this is how its supposed to be like, this is how progress in science is made, and that is actually okay. Also, it is pretty mesmerising seeing theoretical astrophysics come to live in our own hands, thus making all the struggles and difficulties worth it.