Search Results for author: Rupert A. C. Croft

Found 6 papers, 3 papers with code

AI-assisted super-resolution cosmological simulations II: Halo substructures, velocities and higher order statistics

no code implementations3 May 2021 Yueying Ni, Yin Li, Patrick Lachance, Rupert A. C. Croft, Tiziana Di Matteo, Simeon Bird, Yu Feng

In this work, we expand and test the capabilities of our recently developed super-resolution (SR) model to generate high-resolution (HR) realizations of the full phase-space matter distribution, including both displacement and velocity, from computationally cheap low-resolution (LR) cosmological N-body simulations.

Super-Resolution

AI-assisted super-resolution cosmological simulations

no code implementations13 Oct 2020 Yin Li, Yueying Ni, Rupert A. C. Croft, Tiziana Di Matteo, Simeon Bird, Yu Feng

Cosmological simulations of galaxy formation are limited by finite computational resources.

Super-Resolution

Trend Filtering -- II. Denoising Astronomical Signals with Varying Degrees of Smoothness

4 code implementations10 Jan 2020 Collin A. Politsch, Jessi Cisewski-Kehe, Rupert A. C. Croft, Larry Wasserman

The remaining studies share broad themes of: (1) estimating observable parameters of light curves and spectra; and (2) constructing observational spectral/light-curve templates.

Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Earth and Planetary Astrophysics Solar and Stellar Astrophysics Applications

Trend Filtering: A Modern Statistical Tool for Time-Domain Astronomy and Astronomical Spectroscopy

2 code implementations20 Aug 2019 Collin A. Politsch, Jessi Cisewski-Kehe, Rupert A. C. Croft, Larry Wasserman

The problem of denoising a one-dimensional signal possessing varying degrees of smoothness is ubiquitous in time-domain astronomy and astronomical spectroscopy.

Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Applications

Towards Machine-assisted Meta-Studies: The Hubble Constant

no code implementations31 Jan 2019 Tom Crossland, Pontus Stenetorp, Sebastian Riedel, Daisuke Kawata, Thomas D. Kitching, Rupert A. C. Croft

We present an approach for automatic extraction of measured values from the astrophysical literature, using the Hubble constant for our pilot study.

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