Tag Archives: ML

24 Nov 2020

  • (abs, pdf) Lucie-Smith et al., Deep learning insights into cosmological structure formation
  • (abs, pdf) Talbot et al., Blandford-Znajek jets in galaxy formation simulations: method and implementation
  • (abs, pdf) Simcoe et al., Interstellar and Circumgalactic Properties of an Unseen $z=6.84$ Galaxy: Abundances, Ionization, and Heating in the Earliest Known Quasar Absorber
  • (abs, pdf) Dutton et al., NIHAO — XXV. Convergence in the cusp-core transformation of cold dark matter haloes at high star formation thresholds
  • (abs, pdf) Li et al., Oscillations and Random Walk of the Soliton Core in a Fuzzy Dark Matter Halo
  • (abs, pdf) Garcia et al., Magnetization of the intergalactic medium in the IllustrisTNG simulations: the importance of extended, outflow-driven bubbles

08 Oct 2020

  • (abs, pdf) Turner et al., The Onset of Gravothermal Core Collapse in Velocity Dependent Self-Interacting Dark Matter Subhaloes
  • (abs, pdf) Dai & Seljak, Learning effective physical laws for generating cosmological hydrodynamics with Lagrangian Deep Learning
  • (abs, pdf) Marsden & Shankar, Using Unreal Engine to Visualize a Cosmological Volume
  • (abs, pdf) Hennebelle et al., What is the role of stellar radiative feedback in setting the stellar mass spectrum?

05 Oct 2020

  • (abs, pdf) Barrow et al., The Lyman Continuum Escape Survey: Connecting Time-Dependent [OIII] and [OII] Line Emission with Lyman Continuum Escape Fraction in Simulations of Galaxy Formation
  • (abs, pdf) Villaescusa-Navarro et al., The CAMELS project: Cosmology and Astrophysics with MachinE Learning Simulations
  • (abs, pdf) Smith et al., Pix2Prof: fast extraction of sequential information from galaxy imagery via deep learning
  • (abs, pdf) Dvorkin et al., The impact of turbulent mixing on the galactic r-process enrichment by binary neutron star mergers across the entire metallicity range
  • (abs, pdf) Shahmoradi et al., Fast fully-reproducible serial/parallel Monte Carlo and MCMC simulations and visualizations via ParaMonte::Python library