Playing Around with Clusters

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Preamble

There is a lot of astronomy exercises that can be done once you have a list of cluster members. Now that there is plenty of Gaia data, there are lots of lists of stars that people think are members of various clusters. Some of the prep work that has to be done is extracting the data and converting it into a state that is straightforward for your students to use. Along with a framework of things to do with the clusters, I've done the prep work here for three clusters, along with an explanation of what I've done "behind the scenes" to get these data to this point. I've provided the data from the papers I've used, and then pointers to even more data that you could use to expand this project. Students, if you're reading this, this page could be the seeds for a really impressive science fair project.

First, I just have the skeleton for the lab exercise, then I have more of the infrastructure stuff below that.

Introduction

Astronomers use the 3-D motion of stars in the sky to identify stars that are moving as a group, e.g., clusters, also "moving groups", "open clusters", myriad other names for stars that apparently were born together and are still associated with each other. Now that there is plenty of Gaia data to be had, the race is on to use all that data to identify members of known clusters and even identify new clusters. Many, many papers have done this, and you may notice that not all the papers agree with each other! We're going to use the published tables from one group's work and take those lists of members as "truth".

Goals

We are going to load in the Gaia photometry for three clusters, the Pleiades, Praesepe, and NGC 6774. We are going to create optical color-magnitude diagrams for these three clusters and place them in relative age order. As an extension, we will match these stars to 2MASS and WISE data and create IR CMDs to see how they are different than optical CMDs.

Finding the relative ages

This directory has several files. Grab pleiades.tbl, praesepe.tbl, and ngc6774.tbl. Load them into IRSA Viewer as catalogs. IRSA Viewer will recognize the positions and plot them on the sky on an image on the left, but also plot positions on the sky on the right. Change what is plotted to be an optical color-magnitude diagram, G vs. B-R, for each catalog (gmag vs. bpmag-rpmag). No need to compensate for distance if you don't want to. (Why do you think that is? What happens if you do compensate for distance?) Pin each CMD so that you can see all three CMDs at once. You may wish to make sure the titles are marked so that you know which diagram is which.

Which cluster is the oldest? Which is the youngest? How do you know?

Challenge: find any white dwarfs, giants, or binary stars in any of these clusters. Verify that you're right by finding the star in SIMBAD.

Moving into the IR

Now, we need to go and get the 2MASS data for these stars. For each of these three clusters, go to the Catalog Search Tool, and use multi-object searching and one-to-one matching to find 2MASS point source catalog matches. Save the tables to disk so that you can get the catalogs back into the same IRSA Viewer session as before.

The Catalog Search Tool takes all the same columns you had, appends "_01" to the column name, and then appends the new columns from the catalog you asked it to match, in this case 2mass. So now you have catalogs that have the information from Gaia and now 2MASS as well. Make new CMDs, this time near-IR CMDs, say, J vs. H-K (j_m vs. h_m-k_m).

Are these plots different? How? (hint: Look at the range of the x axis.) Why are they so different?

Challenge: Make optical and NIR CMDs from the same catalog. Find the giants and WDs in both plots by using IRSA Viewer's capabilities. Where are they in both plots?


If you want to read more about what these authors did to get these members, the papers are these:




(challenge for later: explore ramifications of taking these lists as opposed to someone else's lists)

(can be ignored if you don't want to use the Pleiades)

The clusters that these papers consider are listed in one of the early tables in these papers. The data from each of the stars behind these papers are electronic tables and can be downloaded from the journals themselves (everything is open access) or from VizieR. These data files are in plain text format, but they are not yet in a format that IRSA tools can easily recognize. The way that I get them into a format that can be recognized is to import them into Excel, make sure I have the columns parsed properly (you will need to explicitly cast the Gaia number as a string; otherwise it thinks it's a large integer and truncates the value), and save it as a csv file. The columns with RA and Dec should have column headings "ra" and "dec", just like that, with no capitalization. Then, I can load the csv file into IRSA Viewer, and it will properly interpret the data tables. Note that the RNAAS paper above has provided the data table as a FITS table, which IRSA Viewer can read directly, but the columns are totally different than


do the same thing against 2mass and explore why the NIR CMDs look so different than the optical ones.


Places with cluster membership (far from exhaustive list; get into ADS to find more Literature searching) https://ui.adsabs.harvard.edu/abs/2018A%26A...616A..10G/abstract And extract the source list (cluster, ra, dec) for each star from the relevant tables http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/616/A10#/browse Separating them out into one source list per cluster, in the right IPAC table format. Then bounce that ra, dec against IRSA’s copy of the Gaia catalogs to get G, B, and R, then use that to make the CMDs. http://cdsarc.u-strasbg.fr/ftp/J/A+A/616/A10/ReadMe https://academic.oup.com/mnras/article/512/3/4464/6563713 https://www.aanda.org/articles/aa/full_html/2018/08/aa32843-18/aa32843-18.html#T3 https://www.aanda.org/articles/aa/full_html/2018/08/aa32843-18/F16.html 100 pc gaia catalog https://ui.adsabs.harvard.edu/abs/2021A%26A...649A...6G/abstract https://academic.oup.com/mnras/article/478/4/5184/5033414 and https://cdsarc.cds.unistra.fr/ftp/J/MNRAS/478/5184/ReadMe

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