The Role of Machine Learning in the Next Decade of Cosmology

no code implementations • 26 Feb 2019 • Michelle Ntampaka, Camille Avestruz, Steven Boada, Joao Caldeira, Jessi Cisewski-Kehe, Rosanne Di Stefano, Cora Dvorkin, August E. Evrard, Arya Farahi, Doug Finkbeiner, Shy Genel, Alyssa Goodman, Andy Goulding, Shirley Ho, Arthur Kosowsky, Paul La Plante, Francois Lanusse, Michelle Lochner, Rachel Mandelbaum, Daisuke Nagai, Jeffrey A. Newman, Brian Nord, J. E. G. Peek, Austin Peel, Barnabas Poczos, Markus Michael Rau, Aneta Siemiginowska, Dougal J. Sutherland, Hy Trac, Benjamin Wandelt

In recent years, machine learning (ML) methods have remarkably improved how cosmologists can interpret data.

Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics

On the Error of Random Fourier Features

no code implementations • 9 Jun 2015 • Dougal J. Sutherland, Jeff Schneider

Kernel methods give powerful, flexible, and theoretically grounded approaches to solving many problems in machine learning.

BIG-bench Machine Learning

A Machine Learning Approach for Dynamical Mass Measurements of Galaxy Clusters

no code implementations • 2 Oct 2014 • Michelle Ntampaka, Hy Trac, Dougal J. Sutherland, Nicholas Battaglia, Barnabas Poczos, Jeff Schneider

In the conventional method, we use a standard M(sigma_v) power law scaling relation to infer cluster mass, M, from line-of-sight (LOS) galaxy velocity dispersion, sigma_v.

Cosmology and Nongalactic Astrophysics