Tag Archives: ML

20 Feb 2020

  • (abs, pdf) Sanchez, Let us bury the prehistoric $h$: arguments against using $h^{-1}{\rm Mpc}$ units in observational cosmology
  • (abs, pdf) Regos et al., Detecting Pair-Instability Supernovae at z<5 with the James Webb Space Telescope
  • (abs, pdf) List & Lewis, A unified framework for 21cm tomography sample generation and parameter inference with Progressively Growing GANs
  • (abs, pdf) Toyouchi et al., Gaseous dynamical friction under radiative feedback: do intermediate-mass black holes speed up or down?
  • (abs, pdf) Cielo et al., Honing and proofing Astrophysical codes on the road to Exascale. Experiences from code modernization on many-core systems
  • (abs, pdf) Ding et al., Testing the fidelity of simulations of black hole — galaxy co-evolution at z ~ 1.5 with observations
  • (abs, pdf) Lehner et al., Project AMIGA: The Circumgalactic Medium of Andromeda
  • (abs, pdf) Zick et al., Towards Studying Hierarchical Assembly in Real Time: A Milky Way Progenitor Galaxy at z = 2.36 under the Microscope

17 Jan 2020

  • (abs, pdf) Wolcott-Green et al., Suppression of H2-cooling in protogalaxies aided by trapped Ly{\alpha} cooling radiation
  • (abs, pdf) Schutz, The Subhalo Mass Function and Ultralight Bosonic Dark Matter
  • (abs, pdf) Yoo et al., On the origin of low escape fractions of ionizing radiation from massive star-forming galaxies at high redshift
  • (abs, pdf) Ramanah et al., Super-resolution emulator of cosmological simulations using deep physical models
  • (abs, pdf) Rodríguez-Montoya et al., Constraints on the velocity dispersion of Dark Matter from Cosmology and new bounds on scattering from the Cosmic Dawn

07 Jan 2020

  • (abs, pdf) Tilvi et al., Onset of Cosmic Reionization: Evidence of An Ionized Bubble Merely 680 Myrs after the Big Bang
  • (abs, pdf) Emsenhuber et al., Realistic On-the-fly Outcomes of Planetary Collisions II: Bringing Machine Learning to N-body Simulations
  • (abs, pdf) Komiya et al., Are Faint Supernovae Responsible for Carbon-Enhanced Metal-Poor Stars?
  • (abs, pdf) Negrello et al., Understanding galaxy formation and evolution through an all-sky submillimetre spectroscopic survey

11 Dec 2019

  • (abs, pdf) Bernardini et al., Predicting dark matter halo formation in N-body simulations with deep regression networks
  • (abs, pdf) Blumenthal et al., Galaxy interactions in IllustrisTNG-100, I: The power and limitations of visual identification
  • (abs, pdf) Tang et al., Dependence of Gravitational Wave Transient Rates on Cosmic Star Formation and Metallicity Evolution History
  • (abs, pdf) Cipolletta et al., Spritz: a new fully general-relativistic magnetohydrodynamic code

30 Oct 2019

  • (abs, pdf) Breen et al., Newton vs the machine: solving the chaotic three-body problem using deep neural networks
  • (abs, pdf) Carlesi et al., On the Mass Assembly History of the Local Group
  • (abs, pdf) Martin-Navarro et al., Black hole feedback and the evolution of massive early-type galaxies
  • (abs, pdf) Farmer et al., Mind the gap: The location of the lower edge of the pair instability supernovae black hole mass gap
  • (abs, pdf) Collins et al., A detailed study of Andromeda XIX, an extreme local analogue of ultra diffuse galaxies
  • (abs, pdf) Decarli et al., Testing the paradigm: First spectroscopic evidence of a quasar-galaxy Mpc-scale association at cosmic dawn
  • (abs, pdf) Kim et al., High-redshift Galaxy Formation with Self-consistently Modeled Stars and Massive Black Holes: Stellar Feedback and Quasar Growth

18 Oct 2019

  • (abs, pdf) Yuan et al., Dynamical Relics of the Ancient Galactic Halo
  • (abs, pdf) Hassan et al., Testing Galaxy Formation Simulations with Damped Lyman-${\alpha}$ Abundance and Metallicity Evolution
  • (abs, pdf) Luo et al., Direct collapse to supermassive black hole seeds: the critical conditions for suppression of $\rm H_2$ cooling
  • (abs, pdf) Yip et al., From Dark Matter to Galaxies with Convolutional Neural Networks
  • (abs, pdf) Cielo et al., Speeding simulation analysis up with yt and Intel Distribution for Python
  • (abs, pdf) Tsizh et al., Large-scale structures in the $\Lambda$CDM Universe: network analysis and machine learning
  • (abs, pdf) Cielo et al., Visualizing the world's largest turbulence simulation