(abs , pdf ) Ashton et al., Nested sampling for physical scientists
(abs , pdf ) Robles et al., A deep learning approach to halo merger tree construction
(abs , pdf ) Lusso et al., The Dawn of Black Holes
(abs , pdf ) Feldmann et al., FIREbox: Simulating galaxies at high dynamic range in a cosmological volume
(abs , pdf ) Kokron et al., Accurate predictions from small boxes: variance suppression via the Zel'dovich approximation
(abs , pdf ) Chon et al., Impact of the cosmic background radiation on the initial mass function of metal-poor stars
(abs , pdf ) Collins & Read, Observational constraints on stellar feedback in dwarf galaxies
(abs , pdf ) van Dokkum et al., A trail of dark matter-free galaxies from a bullet dwarf collision
(abs , pdf ) Bordoloi et al., Resolving the HI in Damped Lyman-{\alpha} systems that power star-formation
(abs , pdf ) Bisbas et al., The origin of the [CII]-deficit in a simulated dwarf galaxies starburst
(abs , pdf ) Karpov et al., Physics-Informed Machine Learning for Modeling Turbulence in Supernovae
(abs , pdf ) Chiaki & Wise, Triggered Population III star formation: the effect of H$_2$ self-shielding
(abs , pdf ) Schatz et al., Horizons: Nuclear Astrophysics in the 2020s and Beyond
(abs , pdf ) Piras et al., Fast and realistic large-scale structure from machine-learning-augmented random field simulations
(abs , pdf ) Ricotti et al., Ghostly Stellar Haloes and their Relationship to Ultra-faint Dwarfs
(abs , pdf ) Zier & Springel, Simulating cold shear flows on a moving mesh
(abs , pdf ) Seitova & Pober, The Optical Depth of Foregrounds for the Highest Redshift 21 cm Signals
(abs , pdf ) Woosley & Smith, SN 1961V: A Pulsational Pair-Instability Supernova
(abs , pdf ) García-Jara et al., Improving Astronomical Time-series Classification via Data Augmentation with Generative Adversarial Networks
(abs , pdf ) Sweere et al., Deep Learning-Based Super-Resolution and De-Noising for XMM-Newton Images
(abs , pdf ) Sassano et al., Super-critical accretion of medium-weight seed black holes in gaseous proto-galactic nuclei
(abs , pdf ) Hausen et al., Revealing the Galaxy-Halo Connection Through Machine Learning
(abs , pdf ) Porter et al., Spatially Resolved Gas-phase Metallicity in FIRE-2 Dwarfs: Late-Time Evolution of Metallicity Relations in Simulations with Feedback and Mergers
(abs , pdf ) Jagvaral et al., Galaxies and Halos on Graph Neural Networks: Deep Generative Modeling Scalar and Vector Quantities for Intrinsic Alignment
(abs , pdf ) Herrera-Camus et al., Kiloparsec view of a typical star-forming galaxy when the Universe was $\sim$1 Gyr old II. Regular rotating disk and evidence for baryon dominance on galactic scales
(abs , pdf ) Thomas et al., Determining Research Priorities for Astronomy Using Machine Learning
(abs , pdf ) Macciò et al., Using Artificial Intelligence and real galaxy images to constrain parameters in galaxy formation simulations
(abs , pdf ) Meštrić et al., Exploring the physical properties of lensed star-forming clumps at $2\lesssim z \lesssim6$
(abs , pdf ) Jaura et al., Trapping of HII regions in Population III star formation
(abs , pdf ) Bar et al., Dynamical friction in globular cluster-rich ultra-diffuse galaxies: the case of NGC5846-UDG1
(abs , pdf ) Lanfranchi et al., Parameterizing the Outflow from a Central Black Hole in Dwarf Spheroidal Galaxies: A 3D Hydrodynamic Simulation
(abs , pdf ) Sikder et al., Machine Learning to Decipher the Astrophysical Processes at Cosmic Dawn
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