COMPUTATIONAL ASTROPHYSICS

Students will learn the fundamentals of computational astrophysics, including gravitational evolution of fluids with and without pressure, and radiative transfer.  Examples will be shown from active research fields.  The class will pair a theoretical foundation with hands-on applications using popular simulators.  Students will analyze simulated data and learn how to compare with observations in a structured statistical framework.

Basics of classical physics, general relativity, radiative processes, Fourier analysis, and a basic familiarity with coding in Python/C.

-       Fluid equations, including continuity, Euler, Poisson, both in a classical and cosmological context

-       Linear evolution, including Eulerian and Lagrangian perturbation theory

-       Non-linear evolution, both analytic and numerical (N-body, hydrodynamic, radiative transfer simulations)

-       Analysis of simulation data

-       Comparison with observations, including Bayesian inference

-       Monte Carlo techniques

-       Machine learning applications to simulations

All lecture notes and other materials will be provided in class.

Each student will be assigned an exercise or a small research project  and the results will be the starting point for the oral exam discussion. Its aim is to probe the level of comprehension of the central concepts, their applications, and the link to observations.