I How common is automation in Astronomy?

AI Thread Summary
Automation is increasingly common in astronomy, particularly for data analysis tasks that can be time-consuming. The discussion highlights a personal experience where a script reduced luminosity calculation time from 7-13 hours to 40 minutes. Examples of automation include scripts for measuring supernova remnants and code developed by Prof. Brian Schmidt for identifying Type Ia supernovae. The trend reflects a broader movement in research towards automating repetitive tasks to enhance efficiency. Overall, automation in astronomy mirrors practices in IT, aiming to streamline workflows and improve productivity.
Phys12
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I am currently an undergraduate researching Supernovae. We are doing some luminosity calculations using CIAO and HEASOFT which take anywhere between 7 to 13 hours for each remnant. To make the process quicker, I wrote a script that reduces that time to about 40 minutes for each remnant. One of my current professor's students (who graduated last year) wrote a script to divide a remnant into small boxes and do some measurements with it. Prof. Brian Schmidt wrote the code which automated the process of finding Type Ia supernovae.

Is this a common trend? Are there any other examples that you can give me? I assume that it would be since a lot of Astronomy is data analysis which happens using computers.
 
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I would think that whatever could be automated will get automated so you can focus on other things, similar to the world of IT. You try to make things as automated and autonomous as possible where possible.
 
Phys12 said:
Is this a common trend? Are there any other examples that you can give me? I assume that it would be since a lot of Astronomy is data analysis which happens using computers.

I assume it's a very common trend anywhere you have data analysis that can benefit from automation. Heck, I just received an e-mail from my engineering college about a weekend course they're offering on automation in data analysis and other areas in research:
Learn the foundational skills necessary to be productive in a small research teams, including:

• Automate repetitive tasks in the Unix shell/command line.
• Program in R to make analyses reproducible.
• Track and manage your work with git.
• Store and share your software with GitHub.

Workshop participation is free.
 
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