Telescope Group 2023: Measuring the Age of a Stellar Cluster

Written by Sara Fekri


Hello everyone! We are the Telescope Group 2023: Sof, Stel, Sara, Sarthak, Elvi, Matin, and Malcom. As part of our 3rd year research project, we are looking at different star clusters in London’s dark skies to measure the age of the stars within the clusters and produce a Hertzberg-Russel (HR) diagram for them. 

Here, we will be presenting how the FITS files, an astronomy file format, are handled in Python and the steps taken to get the HR diagram of the star clusters. The objects we have captured are Messier 35, Messier 36, and Messier 37 on the night of 08/February/2023. 

The steps taken for our project each have a detailed blog post and are as follows:

  • The clusters observed 
  • Handling FITS files 
  • Removing hot pixels 
  • Subtracting the background 
  • Identifying stars and producing an HR diagram

HR Diagram

A Hertzberg-Russel (HR) diagram is plot of stars showing the relationship between the stars’ absolute magnitude versus their effective temperatures. The absolute magnitude corresponds to the stars’ luminosities with negative values being brighter. The effective temperature can be shown using the stellar classification, where the stars from O to M correspond to decreasing temperate on the x-axis.1

An important feature in the HR diagram is the main sequence (MS), the region where the primary hydrogen-burning takes place within a star’s lifetime. Looking into detail where the MS ends, the “MS turn-off”, gives astronomers information about the age of the star cluster.

The general layout of the HR diagram can be seen below, where stars of greater luminosity are located at the top of the diagram, and stars with higher surface temperature are towards the left side of the diagram. 

Hertzberg-Russel diagram. (Image taken from here)

Our Cluster Data – M35

The data captured using the telescope at King’s gives us a FITS file, the most common digital file format in astronomy, and were captured with the blue and green filters attached. From that, we were able to open the files and get on with extracting the data. For this post, data from the M35 blue filter will be used. The raw image for M35_blue from Python can be seen:

Raw image of one of the data files for M35 Blue filter.

However, this raw image does not only contain the stars; it includes hot pixels captured by the filters and a background. The histogram below shows the numbers of counts vs the pixels of the image above:

Histogram of the data set above with the number of counts on the x-axis and the number pixels on the y-axis.

The brightest stars would be located on the histogram where the sharp peaks occur at the right of the curve. The highest peak of the histogram on the left side corresponds to the background of the image.

Next, the hot pixels needed to be removed from the image. Below is a picture showing the stars detected on the M35_Blue image after removing hot pixels using a code in Python:

Stars from M35_Blue are detected with a red circle around them.

The following procedure involved finding the background for it to be subtracted from each data set. The detailed methodology of background removal can be accessed here. The same steps were taken for each of the 10 data sets captured for M35_blue and M35_green. Now, the 10 images can be stacked to form:

The step afterwards was to identify the stars in the blue (B) and green (V) stacked images by comparing the two and see which stars overlap. Finally, those stars can be plotted, and this would form the B-V HR-diagram of M35. The final image remains under progress and will be uploaded here once obtained. 


This project aimed to produce an HR diagram of M35 starting from extracting the data in the FITS files to comparing the stars in the stacked images of the blue and green filter. The same methodology was applied to M36 and M37. This post provided a summary of the steps taken to reach the final result where details can be found in the appropriate separate posts.

Hope you enjoyed reading!


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