123 citations · 359 across the 16 of their papers we have counts for
5 papers · 1 filter
Physics-informed neural networks in the recreation of hydrodynamic simulations from dark matter
Zhenyu Dai, Ben Moews, Ricardo Vilalta +1
Physics-informed neural networks have emerged as a coherent framework for building predictive models that combine statistical patterns with domain knowledge. The underlying notion…
Ridges in the Dark Energy Survey for cosmic trough identification
Ben Moews, Morgan A. Schmitz, Andrew J. Lawler +6
Cosmic voids and their corresponding redshift-projected mass densities, known as troughs, play an important role in our attempt to model the large-scale structure of the Universe.…
Dark Matter Science in the Era of LSST
Keith Bechtol, Alex Drlica-Wagner, Kevork N. Abazajian +177
Astrophysical observations currently provide the only robust, empirical measurements of dark matter. In the coming decade, astrophysical observations will guide other experimental…
Probing the Fundamental Nature of Dark Matter with the Large Synoptic Survey Telescope
Alex Drlica-Wagner, Yao-Yuan Mao, Susmita Adhikari +97
Astrophysical and cosmological observations currently provide the only robust, empirical measurements of dark matter. Future observations with Large Synoptic Survey Telescope (LSST…
Stress testing the dark energy equation of state imprint on supernova data
Ben Moews, Rafael S. de Souza, Emille E. O. Ishida +4
This work determines the degree to which a standard Lambda-CDM analysis based on type Ia supernovae can identify deviations from a cosmological constant in the form of a redshift-d…