1.9k citations · 19k across the 159 of their papers we have counts for
68 papers · 1 filter
Estimating Cosmological Constraints from Galaxy Cluster Abundance using Simulation-Based Inference
Moonzarin Reza, Yuanyuan Zhang, Brian Nord +3
Inferring the values and uncertainties of cosmological parameters in a cosmology model is of paramount importance for modern cosmic observations. In this paper, we use the simulati…
DeepZipper II: Searching for Lensed Supernovae in Dark Energy Survey Data with Deep Learning
Robert Morgan, B. Nord, K. Bechtol +64
Gravitationally lensed supernovae (LSNe) are important probes of cosmic expansion, but they remain rare and difficult to find. Current cosmic surveys likely contain and 5-10 LSNe i…
Data Preservation for Cosmology
Marcelo Alvarez, Stephen Bailey, Deborah Bard +9
We describe the needs and opportunities for preserving cosmology datasets and simulations, and facilitating their joint analysis beyond the lifetime of individual projects. We reco…
Snowmass 2021 CMB-S4 White Paper
Kevork Abazajian, Arwa Abdulghafour, Graeme E. Addison +353
This Snowmass 2021 White Paper describes the Cosmic Microwave Background Stage 4 project CMB-S4, which is designed to cross critical thresholds in our understanding of the origin a…
Discovering the building blocks of dark matter halo density profiles with neural networks
Luisa Lucie-Smith, Hiranya V. Peiris, Andrew Pontzen +3
The density profiles of dark matter halos are typically modeled using empirical formulae fitted to the density profiles of relaxed halo populations. We present a neural network mod…
DeepZipper: A Novel Deep Learning Architecture for Lensed Supernovae Identification
Robert Morgan, B. Nord, K. Bechtol +48
Large-scale astronomical surveys have the potential to capture data on large numbers of strongly gravitationally lensed supernovae (LSNe). To facilitate timely analysis and spectro…