Applied Computer Science and “Real” Physics

In Computer Science, there is a divide between general methods research (such as: algorithms, data structures, security research) and applications (find a solution for a company or societal problem). When studying at University, I was surrounded by the opinion that the more general method is of higher value, and the other is “just application” and messy. I held this opinion for a while, too.

In physics, there is a divide between software method research (such as: new data analysis methods) and finding out something about a physical process (application of a method to a specific case and interpreting the result). The latter is much higher regarded. I’ll give some examples I have experienced:

1. Physicists would say “But what’s the new physics here?” and consistently display a dismissive attitude to automating, robustifying or generalising analyses.
2. Astronomy journals reject papers that present general methods research without application.

There is a cognitive dissonance there too, because methods papers are very highly cited. Physicists find them extremely useful. For example, the 2014 paper of PyMultiNest/BXA and the 2021 paper of UltraNest have 1500 and 500 citations, respectively.

Pure data (catalog) papers are somewhere in the middle. They receive a substantial number of citations and moderate regard. But physicists would recommend not to publish a pure catalog paper with no direct scientific content.

Part of the above can be explained by people liking similar people. If you are a computer scientist at a University, likely you are doing general research and hold that in high regard. If you are a physicist famous for some subfield, you will value progress in that subfield, not general-purpose methods that could help all fields.

The tide is changing a bit recently. There are a few professorships for astrostatistics and machine learning in astronomy. And of course, the above is a simplification: there have been well-regarded experts in methods before, for example specific simulation techniques, observation techniques (direct imaging), going at least back to Joseph Fraunhofer developing the best optics.