What maths for Astrophysics Major?

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SUMMARY

For an Astrophysics major, mastering statistics is essential, particularly in the interpretation and use of power spectra. Courses in practical Fourier analysis and Bayesian statistics are highly recommended for effective data analysis. While differential geometry is often deemed irrelevant for most astrophysics applications, it may have niche uses in general relativity, primarily among cosmologists. Students should prioritize statistics and computational mathematics to prepare for advanced studies and research in astrophysics.

PREREQUISITES
  • Statistics, particularly power spectra interpretation
  • Fourier analysis techniques
  • Bayesian statistics fundamentals
  • Computational mathematics concepts
NEXT STEPS
  • Research advanced statistics for astrophysics applications
  • Learn practical Fourier analysis techniques
  • Study Bayesian statistics and its applications in data analysis
  • Explore computational mathematics for modeling astrophysical phenomena
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Astrophysics students, researchers in particle astrophysics, and anyone pursuing a PhD in astrophysics will benefit from this discussion.

ttyu6
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I'm currently in my second year and I plan on completing a major in Astrophysics and math. I've just completed Vector calculus and next semester I'll be taking differential calculus. Next year I'll be taking computational mathematics, fluid dynamics and PDEs. Are there any additional maths subjects that would be helpful, given that I intend to do a PhD in Astrophysics?
 
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Differential Geometry
 
As an experimentalist, I personally wish that I had taken a statistics course. That would have been the most useful thing to someone like me, who does particle astophysics. But I guess it depends on what field you're in. Maybe Fourier analysis? I know a lot of people who study stellar pulsations and stuff do FFTs all thetime.
 
tenparsecs said:
Differential Geometry

For an astrophysics major? In my opinion, differential geometry is completely irrelevant to the vast majority of work in astrophysics. Not only does it require a huge investment of time in order to learn it properly, but it has little to no application in the sort of areas a working astrophysicist will encounter.

As to the OP, by far the most useful thing you could learn is statistics, and lots of it. In particular, you'll end up needing to know an awful lot about the interpretation and use of power spectra, so statistics and practical Fourier analysis is an absolute must. In addition, once you start trying to do serious analysis of data, you'll find that a knowledge of Bayesian statistics is worth its weight in gold.
 
shoehorn said:
For an astrophysics major? In my opinion, differential geometry is completely irrelevant to the vast majority of work in astrophysics. Not only does it require a huge investment of time in order to learn it properly, but it has little to no application in the sort of areas a working astrophysicist will encounter.

Well, I guess someone doing GR might use it. But usually that sort of stuff is done by cosmologists. And they call themselves physicists, not astrophysicists (granted, so do particle astrophysicists). But yeah, I don't know any astrophysicists in my department who do any GR. I took differential geometry in undergrad because I was also a math major. Never used it in physics, not even when I took GR. I think the physics way of doing tensor calculus is totally different than the math way, or something like that.

shoehorn said:
As to the OP, by far the most useful thing you could learn is statistics, and lots of it. In particular, you'll end up needing to know an awful lot about the interpretation and use of power spectra, so statistics and practical Fourier analysis is an absolute must. In addition, once you start trying to do serious analysis of data, you'll find that a knowledge of Bayesian statistics is worth its weight in gold.

Totally agreed. I had no stats coming into grad school. Huge mistake.
 
If you don't know differential geometry, you'll never be able to make your wife happy.
 

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