422 citations · 2k across the 35 of their papers we have counts for
6 papers · 1 filter
Snowmass2021 Computational Frontier White Paper: Cosmological Simulations and Modeling
Marcelo A. Alvarez, Arka Banerjee, Simon Birrer +9
Powerful new observational facilities will come online over the next decade, enabling a number of discovery opportunities in the "Cosmic Frontier", which targets understanding of t…
Self-supervised similarity search for large scientific datasets
George Stein, Peter Harrington, Jacqueline Blaum +2
We present the use of self-supervised learning to explore and exploit large unlabeled datasets. Focusing on 42 million galaxy images from the latest data release of the Dark Energy…
Mining for Strong Gravitational Lenses with Self-supervised Learning
George Stein, Jacqueline Blaum, Peter Harrington +2
We employ self-supervised representation learning to distill information from 76 million galaxy images from the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys' Data Re…
Cosmic Inference: Constraining Parameters With Observations and Highly Limited Number of Simulations
Timur Takhtaganov, Zarija Lukic, Juliane Mueller +1
Cosmological probes pose an inverse problem where the measurement result is obtained through observations, and the objective is to infer values of model parameters which characteri…
CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks
Mustafa Mustafa, Deborah Bard, Wahid Bhimji +3
Inferring model parameters from experimental data is a grand challenge in many sciences, including cosmology. This often relies critically on high fidelity numerical simulations, w…
HACC: Simulating Sky Surveys on State-of-the-Art Supercomputing Architectures
Salman Habib, Adrian Pope, Hal Finkel +11
Current and future surveys of large-scale cosmic structure are associated with a massive and complex datastream to study, characterize, and ultimately understand the physics behind…