Lecture

Collisionless Systems Equilibrium

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In course

PHYS-401: Astrophysics IV : stellar and galactic dynamics

The aim of this course is to acquire the basic knowledge on specific dynamical phenomena related to the origin, equilibrium, and evolution of star clusters, galaxies, and galaxy clusters. ...

Description

This lecture covers the equilibrium of collisionless systems, focusing on models defined from distribution functions (DFs) such as the isothermal sphere and polytropes. It delves into anisotropic DFs in spherical systems, the Jeans Equations, and equilibria of collisionless systems. The lecture also discusses the structure of isothermal self-gravitating spheres, velocity distribution functions, and solutions of the Poisson equation. Additionally, it explores models with finite potential and density, numerical solutions of non-singular isothermal spheres, and King models with constant anisotropy. The presentation concludes with the Jeans Equations for cylindrical and axisymmetric systems, interpreting the moments and rotation curves for Miyamoto-Nagai disks.

Instructor

Yves Revaz

Yves Revaz is a scientist at LASTRO/EPFL. After successful studies in physics at EPFL, he accomplished his PhD entitled: "Dynamics of external regions of spiral galaxies and constraints on the dark matter", at the Geneva Observatory in the galactic dynamics group of Prof. D. Pfenniger. He then moved to the Paris Observatory to work at the LERMA (Laboratory for Studies of Radiation and Matter in Astrophysics) under the supervision of Prof. F. Combes on the understanding of cooling flows in galaxy clusters. He joined LASTRO at EPFL in 2007, where, in collaboration with Pascale Jablonka, he developed a new TreePM/SPH chemo-dynamical code called GEAR, designed to study the chemical evolution of galaxies. His current main research focus on the evolution of dwarf spheroidal galaxies and their link with the cosmology. Yves Revaz is also the author of pNbody, a parallelized python toolbox designed to manipulate large N-body systems.

Official source

Related concepts (32)

Numerical analysis

Numerical analysis is the study of algorithms that use numerical approximation (as opposed to symbolic manipulations) for the problems of mathematical analysis (as distinguished from discrete mathematics). It is the study of numerical methods that attempt at finding approximate solutions of problems rather than the exact ones. Numerical analysis finds application in all fields of engineering and the physical sciences, and in the 21st century also the life and social sciences, medicine, business and even the arts.

Cumulative distribution function

In probability theory and statistics, the cumulative distribution function (CDF) of a real-valued random variable , or just distribution function of , evaluated at , is the probability that will take a value less than or equal to . Every probability distribution supported on the real numbers, discrete or "mixed" as well as continuous, is uniquely identified by a right-continuous monotone increasing function (a càdlàg function) satisfying and .

Numerical stability

In the mathematical subfield of numerical analysis, numerical stability is a generally desirable property of numerical algorithms. The precise definition of stability depends on the context. One is numerical linear algebra and the other is algorithms for solving ordinary and partial differential equations by discrete approximation. In numerical linear algebra, the principal concern is instabilities caused by proximity to singularities of various kinds, such as very small or nearly colliding eigenvalues.

Numerical methods for ordinary differential equations

Numerical methods for ordinary differential equations are methods used to find numerical approximations to the solutions of ordinary differential equations (ODEs). Their use is also known as "numerical integration", although this term can also refer to the computation of integrals. Many differential equations cannot be solved exactly. For practical purposes, however – such as in engineering – a numeric approximation to the solution is often sufficient. The algorithms studied here can be used to compute such an approximation.

Normal distribution

In statistics, a normal distribution or Gaussian distribution is a type of continuous probability distribution for a real-valued random variable. The general form of its probability density function is The parameter is the mean or expectation of the distribution (and also its median and mode), while the parameter is its standard deviation. The variance of the distribution is . A random variable with a Gaussian distribution is said to be normally distributed, and is called a normal deviate.

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