Tag Archives: ML

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

08 Aug 2019

  • (abs, pdf) Santos-Santos et al., An analysis of satellite planar configurations around the MW and M31: singling out new high quality planes
  • (abs, pdf) Ishida, Machine Learning and the future of Supernova Cosmology
  • (abs, pdf) Wang et al., A dominant population of optically invisible massive galaxies in the early Universe
  • (abs, pdf) Kendall & Easther, The Core-Cusp Problem Revisited: ULDM vs. CDM
  • (abs, pdf) Smole et al., Recoiling supermassive black holes in analytical and numerical galaxy potential
  • (abs, pdf) Ng et al., Searching for ultralight bosons within spin measurements of a population of binary black hole mergers

17 Jul 2019

  • (abs, pdf) Wise, An Introductory Review on Cosmic Reionization
  • (abs, pdf) Ginolfi et al., Scaling relations and baryonic cycling in local star-forming galaxies
  • (abs, pdf) Rudolph et al., Astro2020: Promoting Diversity and Inclusion in Astronomy Graduate Education: an Astro2020 APC White Paper by the AAS Taskforce on Diversity and Inclusion in Astronomy Graduate Education
  • (abs, pdf) Inoue & Yoshida, Clumpy galaxies in cosmological simulations: The effect of ISM model
  • (abs, pdf) Álvarez-Márquez et al., Investigating the physical properties of galaxies in the Epoch of Reionization with MIRI/JWST spectroscopy
  • (abs, pdf) Network et al., Astro2020 APC White Paper: Pursuing diversity, equity, and inclusion in multimessenger astronomy collaborations over the coming decade
  • (abs, pdf) Smith et al., Astro2020 APC White Paper: Elevating the Role of Software as a Product of the Research Enterprise
  • (abs, pdf) Ostdiek et al., Cataloging Accreted Stars within Gaia DR2 using Deep Learning

10 Jul 2019

  • (abs, pdf) Goulding et al., Discovery of a close-separation binary quasar at the heart of a z~0.2 merging galaxy and its implications for low-frequency gravitational waves
  • (abs, pdf) Monelli & Trujillo, The TRGB distance to the second galaxy "missing dark matter". Evidence for two groups of galaxies at 13.5 and 19 Mpc in the line of sight of NGC1052
  • (abs, pdf) Hwang et al., Evolution of star formation rate-density relation over cosmic time in a simulated universe: the observed reversal reproduced
  • (abs, pdf) Aragon-Calvo, Self-supervised Learning with Physics-aware Neural Networks I: Galaxy Model Fitting
  • (abs, pdf) Dubois et al., Shock-accelerated cosmic rays and streaming instability in the adaptive mesh refinement code Ramses: methods and tests