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  1. One Latent to Fit Them All: A Unified Representation of Baryonic Feedback on Matter Distribution

    Publication
    Accurate and parsimonious quantification of baryonic feedback on matter distribution is of crucial importance for understanding both cosmology and galaxy formation from observational data. This is, however, challenging …

  2. Field-level emulation of cosmic structure formation with cosmology and redshift dependence

    Publication
    We present a field-level emulator for large-scale structure, capturing the cosmology dependence and the time evolution of cosmic structure formation. The emulator maps linear displacement fields …

  3. Differentiable Cosmological Simulation with the Adjoint Method

    Publication
    Rapid advances in deep learning have brought not only myriad powerful neural networks, but also breakthroughs that benefit established scientific research. In particular, automatic differentiation (AD) tools and …

  4. Particle Clustering in Turbulence: Prediction of Spatial and Statistical Properties with Deep Learning

    Publication
    We investigate the utility of deep learning for modeling the clustering of particles that are aerodynamically coupled to turbulent fluids. Using a Lagrangian particle module within the Athena …

  5. pmwd: A Differentiable Cosmological Particle-Mesh N-body Library

    Publication
    The formation of the large-scale structure, the evolution and distribution of galaxies, quasars, and dark matter on cosmological scales, requires numerical simulations. Differentiable simulations provide gradients of …

  6. Differentiable Cosmological Simulation with Adjoint Method

    Publication
    Rapid advances in deep learning have brought not only myriad powerful neural networks, but also breakthroughs that benefit established scientific research. In particular, automatic differentiation (AD) tools and …

  7. Simple lessons from complex learning: what a neural network model learns about cosmic structure formation

    Publication
    We train a neural network model to predict the full phase space evolution of cosmological N-body simulations. Its success implies that the neural network model is accurately …

  8. Field Level Neural Network Emulator for Cosmological N-body Simulations

    Publication
    We build a field level emulator for cosmic structure formation that is accurate in the nonlinear regime. Our emulator consists of two convolutional neural networks trained to output …

  9. AI-assisted superresolution cosmological simulations – II. Halo substructures, velocities, and higher order statistics

    Publication
    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 …

  10. AI-assisted super-resolution cosmological simulations

    Publication
    Cosmological simulations of galaxy formation are limited by finite computational resources. We draw from the ongoing rapid advances in Artificial Intelligence (specifically Deep Learning) to address this problem …

  11. AI-assisted superresolution cosmological simulations

    Publication
    Cosmological simulations of galaxy formation are limited by finite computational resources. We draw from the ongoing rapid advances in Artificial Intelligence (specifically Deep Learning) to address this problem …

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

    Publication

  13. Learning the Evolution of the Universe in N-body Simulations

    Publication
    Understanding the physics of large cosmological surveys down to small (nonlinear) scales will significantly improve our knowledge of the Universe. Large N-body simulations have been built to …

  14. Fast and Accurate Non-Linear Predictions of Universes with Deep Learning

    Publication
    Cosmologists aim to model the evolution of initially low amplitude Gaussian density fluctuations into the highly non-linear "cosmic web" of galaxies and clusters. They aim to compare …

  15. Constraining the Halo Mass of Damped Lyα Absorption Systems (DLAs) at z=2-3.5 using the Quasar-CMB Lensing Cross-correlation

    Publication
    We study the cross correlation of damped Ly\(alpha\) systems (DLAs) and their background quasars, using the most updated DLA catalog and the Planck 2018 CMB lensing convergence …

  16. The CAMELS project: Cosmology and Astrophysics with MachinE Learning Simulations

    Publication
    We present the Cosmology and Astrophysics with MachinE Learning Simulations --CAMELS-- project. CAMELS is a suite of 4,233 cosmological simulations of …

  17. The Quijote Simulations

    Publication
    The Quijote simulations are a set of 44,100 full N-body simulations spanning more than 7000 cosmological models in the …

  18. Measurement of Void Bias Using Separate Universe Simulations

    Publication
    Cosmic voids are biased tracers of the large-scale structure of the universe. Separate universe simulations (SUS) enable accurate measurements of this biasing relation by implementing the peak …

  19. Cosmological simulation in tides: power spectrum and halo shape responses, and shape assembly bias

    Publication
    The well-developed separate universe technique enables accurate calibration of the response of any observable to an isotropic long-wavelength density fluctuation. The large-scale environment also hosts …

  20. Rotation method for accelerating multiple-spherical Bessel function integrals against a numerical source function

    Publication
    A common problem in cosmology is to integrate the product of two or more spherical Bessel functions (sBFs) with different configuration-space arguments against the power spectrum or …