Computational Astrophysics (Studierendentage SS 2018)

Sommersemester, 2018

C.P. Dullemond

Goal

The goal of this crash course in computational astrophysics is to learn about several numerical methods to compute and/or model physical systems and solve a variety of problems. We apply these methods to problems from the field of astrophysics, because that is a particular fruitful area with many exciting examples. But many of the methods of this course will also be applicable to other areas of physics and science in general.

Topics

A tentative list of topics:

• Modeling orbits of planets in a multi-planetary system using Runge-Kutta integration. Applied to: Exoplanetary systems such as TRAPPIST-1.
• Fitting a model to observed data using Markov Chain Monte Carlo modeling. Applied to: Exoplanetary observational data.
• Computing a spectrum of an accretion disk. Application: Either a black hole or a protoplanetary disk (your choice).
• 1-D spherical gas dynamics model (numerical hydrodynamics) of a collapsing molecular cloud. Applied to: The formation of a star.

Requirements

• Basic knowledge of Python programming, including handling numpy arrays and plotting with matplotlib
• Bring your own laptop with Python 2.7 or Python 3 installed, including the following libraries installed:
• numpy
• scipy
• matplotlib

Course material

• Intro
• Chapter 1: N-body model for exoplanetary systems
• snippet_ode_euler_1.py
• snippet_ode_scipy_1.py
• snippet_kepler_1.py
• snippet_nbody_1.py
• snippet_anim.py
• snippet_nbody_trappist1.py
• snippet_orbitangle.py
• Chapter 2: Automatic fitting of exoplanetary RV data
• snippet_lsq.py
• snippet_ml.py
• snippet_mcmc.py
• kepler.py
• 51 Pegasi radial velocity data
• Chapter 3: Numerical hydrodynamics of astrophysical gas clouds
• snippet_naive_advection.py
• snippet_donor_cell_template.py

Organisation

Anmeldung erfolgt auf dem Übungsgruppensystem:

StudierendenTage SS 2018 in der Gruppe E.

Monday April 9 to Friday April 13 (5 days) from 9:15 to 12:00

Location: INF 227, SR 2.402