Exercises

  1. Work through Examples 1-3. (a) Submit copies of your Gaia HR Diagram, and both your de-reddened and apparent color images of NGC 3766. (b) Enter the values of your estimated proper motion in RA and Dec, distance, Log(Age) AND age in years, metallicity, and E(B-V) for NGC 3766.
  2. Work through Example 4. (a) Submit copies of your Gaia HR Diagram, and both your de-reddened and apparent color images of IC 2948. (b) Enter the values of your estimated proper motion in RA and Dec, distance, Log(Age) AND age in years, metallicity, and E(B-V) for IC 2948.
  3. Work through Example 5. (a) Submit copies of your Gaia HR Diagram, and both your de-reddened and apparent color images of NGC 2682 (M67). If you are assigned problem 5 as well, DO NOT close your combined color image in Afterglow. (b) Enter the values of your estimated proper motion in RA and Dec, distance, Log(Age) AND age in years, metallicity, and E(B-V) for NGC 2682 (M67).
  4. Work through Example 6. (a) Submit copies of your Gaia HR Diagram, and both your de-reddened and apparent color images of NGC 3201. (b) Enter the values of your estimated proper motion in RA and Dec, distance, Log(Age) AND age in years, metallicity, and E(B-V) for NGC 3201.
  5. Step 2 of Example 5 noted the presence of two distinct populations of stars with different average proper motion values in the field of M67. Can you think of a simple explanation for why the two populations would have such different average proper motions?
  6. This Google Sheet contains the Scatter output data downloaded for NGC 2682 after field star removal. Four columns have been added:
    – column A simply calculates BP-RP;
    – column B calculates M_RP = RP – 5*log10(d/1 kpc) – 10 – 3.1*E(B-V) using the values estimated for d and E(B-V) through isochrone matching;
    – column D converts the right ascension to hours:minutes:seconds format;
    – and column F converts declination to degrees:minutes:seconds format.
    The sheet was then sorted by BP-RP so the bluest stars are at the top of the spreadsheet, and the first 26 columns were highlighted. A scatter plot of M_RP vs BP-RP was inserted, producing the cluster HR Diagram from Example 5. In the HR Diagram, the 26 bluest points are either blue stragglers or white dwarfs.

    Your task: go back to your combined colour image of NGC 2682 in Afterglow. Select the Custom Marker tool and select Centroid clicks. Then add custom markers for each of these blue stragglers and white dwarfs IF the star is in the image (they aren’t all in the image, so you just have to identify the ones that are). Increase the label radius to 30 for all labels, and don’t bother adding any text for labels since the text for these is too small to read. Click the Download Viewer Snapshot button to download a screenshot of the image which shows the custom markers you added (note that Export Image as JPG does not show the custom markers, so you have to download a viewer snapshot to see the markers), and submit this image showing all the blue stragglers labelled over the cluster image. Note that if these stars were removed, the overall appearance of the cluster would be redder, in better alignment with its actual age. If the right ascension and declination of any star falls within your image but you aren’t able to identify it in the image, explain why you think it’s missing.
  7. Some introductory astronomy textbooks make the generalization that clusters dominated by blue stars are young and clusters dominated by red stars are old, and the age of a cluster can be estimated by looking at the overall colour. List three examples you’ve learned about as you worked through Examples 1-6 that complicate this generalization, either by causing clusters to appear bluer than might be expected by their old ages, or to appear redder than might be expected based on their young ages. State whether each example you identify leads to a stereotypically “younger” or “older” appearance in a cluster’s color image.
  8. Observe one of the star clusters listed in this Google Sheet (note that there are separate tabs for open and globular clusters). You may wish to search Aladin Lite for DSS images of the star clusters. Telescope field sizes and recommended exposure durations in various standard filters are listed in the table. Collect at least 5 images per filter, in at least three different filters (e.g. B, V and R, or g’, r’, and i’). Create stacked images in each filter, following the steps outlined for image stacking in Afterglow in Example 7. Analyze your cluster following the steps you learned through Examples 1-6, following this procedure:
    • Photometer your stacked images in Afterglow and upload your batch photometry file to Clustermancer to start your analysis.
    • Estimate your cluster’s proper motion and distance with the Field Star Removal tool, then go to Archive Fetching and fetch Gaia and 2MASS photometry out to double your cluster’s catalogued radius.
    • Return to Field Star Removal and refine your cluster’s astrometric parameters there.
    • In Isochrone Matching, start with the Gaia HR diagram (RP vs BP-RP) to find an initial estimate of parameters (remember to use the distance distribution in FSR to help estimate your cluster’s distance).
    • Then create V vs B-V, R vs B-R, and H vs J-H plots, and use them to refine your isochrone matching parameters. Save all four graphs.
    • On the Results Summary page, download the CSV with your cluster’s parameters.
    • Return to Afterglow and generate combined colour images with both E(B-V) set to 0 and with the image de-reddened using the value you estimated through isochrone matching.In your submission for this problem, state your cluster’s name, then: (a) submit copies of your four HR diagrams and both your apparent AND de-reddened color images of your cluster; and (b) copy all values from your downloaded Results Summary CSV.
  9. Comparing your results to catalogued values! This exercise should be done after completing a cluster analysis, i.e. following Exercises 1, 2, 3, 6, or 8.
    The Milky Way Star Clusters Catalog is the result of a global survey of clusters in our Galaxy, published in 2012-2015, with cluster parameters estimated through isochrone modelling. This study was therefore done pre-Gaia, and the isochrone models used did not include metallicity as a variable. In principle, with the tools provided in this module your estimated parameters should provide a fit to the data that is at least as good as a model generated with the MWSC catalog parameters. In fact, if you were to study the full catalog, you’d find several examples of asterisms which turn out not to be clusters when Gaia data are incorporated into the analysis, indicating that there is still much research to be done to improve our knowledge of even the clusters we’ve already identified in our galaxy (let alone those we’ve yet to discover! This paper will give you a sense of current research happening in this field).
    The full MWSC catalog can be accessed here. Column A lists the cluster’s name; column M lists the number of cluster stars; column N lists the distance; column O lists E(B-V); column P lists log(Age); and, when available, column T lists metallicity.
    1. Load HR Diagrams of Gaia RP vs BP-RP and 2MASS H vs J-H graphs of your cluster into Clustermancer, setting both graphs to Frame on Data. Then set the isochrone model’s Distance (kpc), log(Age (yr)), Metallicity (solar), and E(B-V) (mag) values using the MWSC parameters. If a metallicity value is not listed, use -1.5 for globular clusters or 0 for open clusters. Save and submit both graphs.
    2. Visually compare the isochrone fit from 9.1 with the graph you generated previously. Discuss which fit appears to be more accurate, including any parameters in particular that seem to have been poorly estimated in the MWSC. Conclude whether your analysis improves upon previously published values for this cluster.