SIMPS: Search Into Metal-Poor Stars

Welcome to the SIMPS blog page! This is will be a compilation of all the physics (research, coding, analysis etc) that 5 astrophysics undergraduates will be undertaking over 10 weeks!

What is the project? We will be looking at the work that was done by two previous generations of metal-poor star projects, LAMPSS and WARP. These groups identified 23 potential candidates, performing colour based data cuts on the COSMOS catalogue. Their blog pages and final reports are available for more information. As a group, we decided to take a different route and look more in depth into these potential candidates, hoping to investigate chemical abundances further (aiming for the classification of carbon-enriched metal-poor stars (CEMPS)), look into the metallicity distribution function, and other features of metal-poor stars.

What are metal-poor stars? They are Population II stellar objects with a low metallicity. We will be defining metallicity as the ratio of iron to hydrogen, [Fe/H]. A metallicity less than -3 is defined as metal-poor. Metal-poor stars are a key in understanding the early Universe and galaxy evolution in terms of chemical enrichment.

We will keep all those interested up to date with weekly posts about our physics doings.

Meet the Group!

(Left to right) Paula Echemendia Fernandez – Administrator; Joshua Paterson – Task Lead; Leah Clare – Coordinator;
Joe Butler – Code Lead; James Forster – Theory Lead

Week 1

The first week was mainly getting to grips with what we plan to do with our project. Our theory lead, James, along with Paula and Joshua, hit the internet to search through the vastness of astrophysics papers in order to gain deeper understanding of what metal-poor stars are, why they form and what that implies about the early Universe. There were many papers available (some even referenced Sobral’s work!) and a lot of reading to do, but our theorists gathered the knowledge we would need to embark on our journey.

During our first lab session, Joshua and Paula got to work on our introduction for the final report (to lessen the workload later), whilst myself, Joe and James looked at how WARP cut the catalogue down to their potential candidates. This allowed us to get to grips with the software we will need, whilst familiarising ourselves with the contents of the COSMOS catalogue. We decided to use the WARP conditions and worked on TOPCAT to make the cuts. The first few cuts went without a hitch and we had the source numbers that WARP had gotten. However, we ran into some issues when working through the error conditions; we were ending with 628 sources, whereas WARP had 338. Although this wouldn’t be affecting any results that we would be reaching, it was interesting as the difference seemed so large. After consultation with David, we concluded that there may have been a mistake on either parties end. We reminded ourselves that the point wasn’t to get the same results as WARP, but to familiarise ourselves with the data and software. We produced an on-sky graph of the 628 sources we arrived at.

Figure 1: On-sky positions of the sources. White gaps are due to missing data and omission of very bright sources.

Joe, our code lead, then got to work on coding an algorithm that would produce stellar spectra for 81 candidates (identified by LAMPSS and WARP, of which they concluded 23 were metal-poor stars) and compare them to various model stellar spectra and give the best model for each candidate. There are 85 models, organised by metallicity and temperature, respectively. When looking at the low resolution spectra, we found that many appeared to be potentially galactic in nature (the spectra trended up, rather than down), meaning many of the 81 candidates may not be stars.

Our lab concluded with the algorithm partially complete. Joe went away and worked on it, but we ran into an issue with comparing the spectra to the models. We talked to David and he suggested we log the flux axes on both the model and the stars to make comparisons. More algorithm work was needed!

Week 2

This week consisted of tireless coding, lead by Joe (aided by Joshua and myself). We underestimated how much pre-processing would be required for the first algorithm. After fixing error bars and logging the flux density axes, we found that we needed to keep adding to the algorithm. We used a database that had all the filters that the models used and imported these into the code in order to give data points on the model spectra like the stellar spectra figures, which would allow us to compare the with the code. We then tried to use a method where we averaged the point distances on the model spectra. However, when we looked at one of the model spectra (made from log(flux density) against wavelength from the available .fits file), we find our points were too low. We conversed with our supervisor, who said we had the order of operations in our code wrong. After fixing this, the issue remained. Further investigation lead to the conclusion that we need to integrate and then divide by the Full Width Half Maximum (FWHM) for each filter. This fixed our issue.

Figure 2: The red is the spectrum of a model (spectral type A, temperature=7500K, metallicity=0.0) obtained from the .fits file, while the blue are the data points obtained using filter data.
Figure 3: The filters used in the model spectra. Data for these are available online.

Then, we needed to use the chi-squared method to give a value when comparing the candidates to each model, before selecting the smallest value to give the best method. Upon completion of this, it seemed the many of the candidates were best matched to a G5000 model. We had a meeting with David, who suggested that it was due to a lot of candidates being galaxies and the first part of the spectrum fitting best to this model. He suggested we make a cut based off a lower and upper chi-squared value. As a group, we realised this would likely leave us with fewer candidates than we anticipated.

Meanwhile, James and Paula worked some more on the report and readings.

In order to still get results from this project, we decided to slightly change our tasks. With the hopes we would still have some candidates, we decided we would eventually split into two sub-groups. One would do in depth scientific investigation on the final candidates, whilst the other group would go back to the full COSMOS catalogue and use the first two WARP conditions (to remove most galaxies) and then perform cuts based on spectra (with the aid of our algorithm) rather than colour (like LAMPSS and WARP did). The latter should leave us with less galaxy contamination.

Week 3

Week 3 hit off with us realising that our flux for the candidates was in Jansky, whereas the units for the models were in erg/s/cm/Å. This was the reason that many of the spectra (like above) appeared to be galactic in nature. We had to make a unit conversion in order to make appropriate. comparisons between the model and candidates. After fixing this issue and propagating our errors, our spectra seemed more stellar in nature.

We then worked on getting the algorithm to produce a 3D plot that plotted reduced chi-squared, temperature, distance and metallicity. This would visually show us which model is best at a certain distance for each candidate.

We obtained a distance estimate based on radius, temperature and flux (Boltzmann Law for blackbody radiation) in order to find the distance we would have to move the models up and down to obtain a reduced chi-squared.

Meanwhile, the theory team continued with CEMP and other research and made more progress on our report.

Week 4

The algorithm was almost finished, with the final processing and analysis in sight.

This lab consisted of applying the WARP colour conditions to obtain a reduced data set of 1220 sources with an estimated 6.1% galaxy contamination. This data set would come in to use after the 81 candidates had been studied, to see what other potential candidates we can find. The data was not in the form needed for use in the algorithm, so a script was produced to convert it into fluxes and corresponding up and down errors.

The algorithm was coded to produce the 3D reduced chi-squared plot and then produce files with data for the best fit model for each of the 81 candidates. A condition was also included to disregard any sources whose minimum reduced chi-squared was more than a value of 10; this would indicate it was a galaxy, not a star.

Figure 4: 3D plot showing how reduced chi-squared varies with temperature, distance.
Figure 5: Reduced-chi squared and how it varies for temperature, metallicity, and distance. Upon plugging in Star 1, the algorithm gave us the best fit as a G star of 5000K, metallicity -5.0, at 63kpc away.

A script to produce a 3D plot of RA, Dec, and distance was made so that a visual representation of our sources and their metallicities could be produced. We would then be able to see if there any groupings of particular metallicities.

At our supervisor meeting, David gave us some wavelengths (~4200Å and 4600Å) we could explore to see if there are any characteristic dips that would indicate the star was potentially carbon-enhanced. We plan to do this in the next lab, hoping the filters are sensitive enough to show us such structure.