As physics/astronomy become increasingly computational, training and infrastructure have struggled to keep pace. In today’s bite we discuss these issues and a new company, Camber, working to streamline research and democratize high performance computing!
Pulsar timing arrays could localise individual sources of gravitational waves to host galaxies. The problem is, it’s so computationally difficult! This paper shows us a faster way.
Ever imagined what computers go through while analyzing gravitational wave data? Today’s post sheds light on this computing nightmare.
Constraining physical parameters in a cosmological survey is often computationally expensive, especially when considering more than one survey at a time. The authors of this paper offer a simple method to reconstruct parameter distributions in a fraction of the time needed for most high-performance computers.
Finding craters is a pesky problem – so let’s outsource it to machine learning!
The number of known moons of Pluto has now reached five. What are they like, and how did they get there? Kenyon and Bromley use numerical simulations to answer these questions and determine what else New Horizons may find in 2015.