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# Isochrone Fitting

## Collecting Further Isochrones

##### This exercise consists of collecting several isochrones which are the same in all respects but one, then overlaying them onto the HR diagram of your cluster to see which one most closely matches. You already know the metallicity of your cluster, since you used it previously to generate a single isochrone. Now you have to return to the isochrone generator you used before, and this time collect several isochrones at various ages:
• Is your cluster likely to be millions of years old, or billions?
• How large of an age range should you plot? How far apart should the age of each isochrone be?

## Accessing and Using Data

##### Once you’ve isolated your isochrones, you can now start plotting them to see which ones best overlap with your cluster’s SDSS data. Begin with your HR diagram from the previous part of this activity, and layer your isochrones on top of it until you find a good match. Make sure your SDSS data and isochrones are plotted on the same axes, using the same filters. Once again, programming will come in handy, since with it you can efficiently plot an entire set of isochrones. If you don’t have the knowledge or time to write such a program, you can also get around this by plotting a few isochrones at a time, starting with the most extreme ages and narrowing in on your cluster incrementally. Keep the following questions in mind while plotting:
• How closely must an isochrone fit your data for it to be considered a good match?
• If you’ve found a match, does the age of the corresponding isochrone make sense for your cluster?

## Final Analysis

##### Congratulations! You’ve now taken a cluster from the SDSS database and applied various astronomical solutions to learn more about it. For many clusters, you can actually check your estimated distance and age with a quick online search. Knowing how well your results match with other astronomers’ might make it easier to reflect on the experiment and analyze the most significant sources of error. You can then go back to those sources of error and alter your methods to try improving the results. These questions can help guide you through that process:
• How close were your estimated distance and age to the “accepted” values for your cluster?
• Are you satisfied with how the experiment went? What was successful? What didn’t work so well?
• What could you do to improve your results? Are there places where you might have made mistakes?
• Are there any parts of the procedure used here that couldn’t be improved? If there are, what makes them that way?
• Which had more significant impact on the quality of your results: the initial data or the methods applied to it? Is one more important than the other? Which is more easily improved?