1 code implementation • 23 Jun 2021 • Peter Harrington, Mustafa Mustafa, Max Dornfest, Benjamin Horowitz, Zarija Lukić
Full-physics cosmological simulations are powerful tools for studying the formation and evolution of structure in the universe but require extreme computational resources.
no code implementations • 12 Jan 2021 • Md Abul Hayat, Peter Harrington, George Stein, Zarija Lukić, Mustafa Mustafa
We use a contrastive self-supervised learning framework to estimate distances to galaxies from their photometric images.
1 code implementation • 24 Dec 2020 • Md Abul Hayat, George Stein, Peter Harrington, Zarija Lukić, Mustafa Mustafa
We show that, without the need for labels, self-supervised learning recovers representations of sky survey images that are semantically useful for a variety of scientific tasks.
1 code implementation • 27 Sep 2017 • Yao-Yuan Mao, Eve Kovacs, Katrin Heitmann, Thomas D. Uram, Andrew J. Benson, Duncan Campbell, Sofía A. Cora, Joseph DeRose, Tiziana Di Matteo, Salman Habib, Andrew P. Hearin, J. Bryce Kalmbach, K. Simon Krughoff, François Lanusse, Zarija Lukić, Rachel Mandelbaum, Jeffrey A. Newman, Nelson Padilla, Enrique Paillas, Adrian Pope, Paul M. Ricker, Andrés N. Ruiz, Ananth Tenneti, Cristian Vega-Martínez, Risa H. Wechsler, Rongpu Zhou, Ying Zu, for the LSST Dark Energy Science Collaboration
The use of high-quality simulated sky catalogs is essential for the success of cosmological surveys.
Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics
2 code implementations • 7 Jun 2017 • Mustafa Mustafa, Deborah Bard, Wahid Bhimji, Zarija Lukić, Rami Al-Rfou, Jan M. Kratochvil
Inferring model parameters from experimental data is a grand challenge in many sciences, including cosmology.