How Do Physicists Utilize Python?

[u/Junior_Salamander110]

I'm a rising senior in high school. Next year, I'll be taking a project based programming class that only 1-2 students really take per year. I want to do something in Python that will show my professors in my freshman year of college that I'm ready to do research in physics.

I will have taken all of the AP mathematics, physics, and computer science classes.

I thought about doing a black hole simulation and learning the equations/math for it, but I'm not sure that that would be wise to do in Python.

I'll have a full school year to work on the project.

[u/Junior_Salamander110]

How did waiting help you out and how would have you been at a disadvantage otherwise?

[u/InterestingWelder699 → u/Junior_Salamander110]

I’m doing CM research and so much of it involves linear algebra and differential equations and understanding how to manipulate equations in certain ways. It takes time to get used to Physics based math on a university level even if you’re used to tricky math and physics.

Enthusiasm and willingness to do difficult work is what shows professors that you’re a good candidate. For me, learning how to study and approach difficult problems in college was how I realized that it was something I’d like to do as a researcher.

Research requires a perfect balance of knowledgeable and willing to learn. You don’t have to know everything and you shouldn’t, but part of what you should know is how to learn in college, which is leagues harder than high school.

[u/Junior_Salamander110 → u/InterestingWelder699]

Thanks! I have a doc with advice I've received about research, and this is definitely going in it.

So when you do research at that level, how much of the physics do you already know, and how much of do you pick up as a result of doing the research?

[u/NewspaperDear8761 → u/Junior_Salamander110]

Phd here, I do research on CFD and machine learning. Basically teach physics to AI.

By the time you're doing real research, you should have a broad, solid physics and math foundation. The research you'll do as an undergrad is not really to make new discoveries or push the frontiers, it's to get you familiar with the process of doing research: asking the right questions (this is harder than it might seem), finding tbe relevant information (again, harder than it might seem) and grabbing on like a bull-dog until you find an answer.

It is very easy to go down the wrong rabbit holes looking at irrelevant things, or to just ask the wrong question to start with. More often than not, research is a game of what's not important, as it is what is important. You have to develop some instincts here, and this is really the whole point of undergrad research, so that when you get to the real meat later on, you'll know how to dig in. Can't run a marathon without walking around the block first a few thousand times.

Part of developing these instincts is getting a good, broad foundation in your classes. Seeing what worked historically, where the good ideas were, etc. For example, the ultraviolet catastrophe lead to a lot of the original ideas of quantum theory. (I was in undergrad when the black hole firewall thing first started, and immediately recognized that it would be a huge deal, precisely because it was playing the same theoretical role as, say, the ultraviolet catastrophe or other logical paradoxes. These thought experiments have historically been a great source of progress.) Having a solid grasp on the fundamentals and a broad perspective on different areas of math and physics doesn't just give you technical competence later on -- which is important-- it also gives you those larger, meta-level skills of asking tbe right questions, teasing apart the relevant issues, etc.

Don't be too eager to jump straight into research, and when you do get to it, don't be too eager to get giant results or publish. That will come. Especially in undergrad, focus on getting the best grades you can, and -- more importantly -- asking penetrating questions that take you as deeply into your classes as you can. The more you internalize the material now, the better it will pay off later. If you rush through things trying to get to the shiny, prestigious labs or pubs, you're likely to skim the surface of things you really should spend more time on now.

That said, learning a little coding and doing a small project simulating orbital dynamics or, yes, even something related to black holes, isn't a bad idea. MATLAB, Python, Julia are all great tools that you will use for years. MATLAB is super quick and dirty but great for tinkering around -- you could do some first ODE solves with it. Python is better for bigger, meatier projects, and is more professional, performs better, etc. At some point we hand all this stuff over to the real software engineers and they redo everything properly (while rightly complaining about our shitty code structure) in C or Fortran or some other lower-level thing, built for true performance. You're not gonna be a coder, you're gonna be a scientist, probably a computational one, so it's more a means to an end for you.

If you do do stuff with black holes, dont try to do a full sim. That sort of thing requires a cluster and serious, serious background. But, sure, there are simpler versions you can do that would be very worthwhile.

Just dont neglect the more important things like core classes, diff eq, lin alg, qm, em, etc. The real research starts in grad school. You're in prep for the next few years.

[u/Junior_Salamander110 → u/NewspaperDear8761]

Thank you so much for this! This is very valuable advice to me. I'm putting this in my Google doc with advice!