Playing Around with Clusters
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.
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".
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 such that once you click on a giant or a white dwarf in a cluster in an optical CMD then it is also highlighted in the corresponding NIR CMD. Where are they in both plots -- are they where you expect?
Challenge: Repeat this using WISE (you will have to use the original tbl files, not the ones output from the 2MASS merge). Make a plot of [W1] vs. [W1]-[W3] (w1mpro vs. w1mpro-w3mpro). You will most likely need to restrict it to only have the high signal-to-noise sources, e.g., filter down so that at least w3snr > 10). Why does this look like it does? Where are the giants/white dwarfs -- are they where you expect? Why are there outliers in this plot? What are the outliers? (Hints: worry about errors on the photometry? source confusion? Use Finder Chart? What are the ages of the clusters here - should these stars have IR excesses?)
Details and expansion options
If you want to read more about what these particular authors did to get these members, the papers are these:
- https://ui.adsabs.harvard.edu/abs/2021RNAAS...5..173L/abstract - just the Pleiades - note that RNAAS papers are not refereed.
The clusters that these papers consider are listed in one of the early tables in both of the first two 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, and you can download them, 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, load the csv file into IRSA Viewer, and it will properly interpret the data tables. For this example, I then used IRSA Viewer to filter down the table based on cluster name to be just Praesepe, then saved that filtered table, and then just NGC 6774, then saved that filtered table accordingly.
If you don't want to use the Pleiades in this project, you can ignore the last paper. I included it because the Pleiades is a benchmark cluster for SO MANY things. 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 what the other papers have. What I did was load the fits table into IRSA Viewer, use the table options (the gears) to turn off and on various columns, then save the table as an IPAC table file as a much slimmed down table containing just the columns I wanted. Then I edited the tbl file to have the same column headings as the other two saved tables such that, e.g., gmag is the same in all three tables for ease of plotting.
- Use different/additional clusters. The papers have lots and lots of clusters, and the tables include the ages for the clusters. Pick clusters of different ages, create catalogs via the same methods I used above, and see if your students can put them in the correct relative age order based on the Gaia CMDs.
- Use different clusters that are young enough that some will have IR excesses. This will make the expansion to WISE data more interesting, but you still need to worry about measurement error and source confusion.
- Explore ramifications of taking these member lists as opposed to someone else's member lists. Lists of just some of these papers below.
- For each cluster, explore the proper motions and/or true space motions. This is how the members were selected, so all the cluster members ought to have similar motions.
- Explore binaries in these clusters. Pull light curves from ZTF or TESS or ASAS-SN to see if they are eclipsing binaries.
Places with cluster membership (far from exhaustive list; get into ADS to find more Literature searching)
- And extract the source list (cluster, ra, dec) for each star from the relevant tables
- 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.
- 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