A new, faster
modeling technique for galaxy formation has been developed
by University of
Illinois student Harshil Kamdar and professor Robert Brunner.
The technique uses
machine learning to cut down computing times from
thousands of
computing
(November 11, 2015) A
new machine-learning simulation system developed at the University of Illinois
promises cosmologists an expanded suite of galaxy models – a necessary first
step to developing more accurate and relevant insights into the formation of
the universe.
The feasibility of this method has been laid out in two
recent papers written by astronomy, physics and statistics professor Robert
Brunner, his undergraduate student Harshil Kamdar and National Center for
Supercomputing Applications research scientist Matthew Turk.
Cosmologists currently use two simulation approaches. The
first is an N-body simulation, which models how dark matter collapses under
gravity. These simulations, when coupled with a semianalytical model, let
astronomers model how galaxies form. The second is a hydrodynamical N-body
simulation, which lets astronomers directly model how normal matter interacts
with dark matter under gravitational collapse to form galaxies; this more
computationally intensive approach directly shows how invisible clumps of dark
matter, referred to as halos, influence star formation.