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

Distinguishing standard and modified gravity cosmologies with machine learning

no code implementations • 25 Oct 2018 • Austin Peel, Florian Lalande, Jean-Luc Starck, Valeria Pettorino, Julian Merten, Carlo Giocoli, Massimo Meneghetti, Marco Baldi

We present a convolutional neural network to identify distinct cosmological scenarios based on the weak-lensing maps they produce.

Cosmology and Nongalactic Astrophysics

On the dissection of degenerate cosmologies with machine learning

no code implementations • 25 Oct 2018 • Julian Merten, Carlo Giocoli, Marco Baldi, Massimo Meneghetti, Austin Peel, Florian Lalande, Jean-Luc Starck, Valeria Pettorino

Based on the DUSTGRAIN-pathfinder suite of simulations, we investigate observational degeneracies between nine models of modified gravity and massive neutrinos.

BIG-bench Machine Learning General Classification +2