Predicting Localized Primordial Star Formation with Deep Convolutional Neural Networks

• URL: http://arxiv.org/abs/2011.01358v2
• Date: Tue, 20 Apr 2021 21:47:02 GMT
• Title: Predicting Localized Primordial Star Formation with Deep Convolutional Neural Networks
• Authors: Azton I. Wells and Michael L. Norman
• Abstract summary: We investigate applying 3D deep convolutional neural networks as fast surrogate models of the formation and feedback effects of primordial stars. We present the surrogate model to predict localized primordial star formation; the feedback model will be presented in a subsequent paper. To our knowledge, this is the first model that can predict primordial star forming regions that match highly-resolved cosmological simulations.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We investigate applying 3D deep convolutional neural networks as fast surrogate models of the formation and feedback effects of primordial stars in hydrodynamic cosmological simulations of the first galaxies. Here, we present the surrogate model to predict localized primordial star formation; the feedback model will be presented in a subsequent paper. The star formation prediction model consists of two sub-models: the first is a 3D volume classifier that predicts which (10 comoving kpc)$^3$ volumes will host star formation, followed by a 3D Inception-based U-net voxel segmentation model that predicts which voxels will form primordial stars. We find that the combined model predicts primordial star forming volumes with high skill, with $F_1 >0.995$ and true skill score $>0.994$. The star formation is localized within the volume to $\lesssim5^3$~voxels ($\sim1.6$~comoving kpc$^3$) with $F_1>0.399$ and true skill score $>0.857$. Applied to simulations with low spatial resolution, the model predicts star forming regions in the same locations and at similar redshifts as sites in resolved full-physics simulations that explicitly model primordial star formation and feedback. When applied to simulations with lower mass resolution, we find that the model predicts star forming regions at later redshift due to delayed structure formation resulting from lower mass resolution. Our model predicts primordial star formation without halo finding, so will be useful in spatially under-resolved simulations that cannot resolve primordial star forming halos. To our knowledge, this is the first model that can predict primordial star forming regions that match highly-resolved cosmological simulations.

Related papers

• ASURA-FDPS-ML: Star-by-star Galaxy Simulations Accelerated by Surrogate Modeling for Supernova Feedback [0.7324709841516586]
  We introduce new high-resolution galaxy simulations accelerated by a surrogate model that reduces the computation cost by approximately 75 percent. Massive stars with a Zero Age Main Sequence mass of about 8 solar masses explode as core-collapse supernovae (CCSNe) Our new approach achieves high-resolution fidelity while reducing computational costs, effectively bridging the physical scale gap and enabling multi-scale simulations.
  arXiv  Detail & Related papers  (2024-10-30T18:00:02Z)
• CHARM: Creating Halos with Auto-Regressive Multi-stage networks [1.6987257996124416]
  CHARM is a novel method for creating mock halo catalogs. We show that the mock halo catalogs and painted galaxy catalogs have the same statistical properties as obtained from $N$-body simulations in both real space and redshift space.
  arXiv  Detail & Related papers  (2024-09-13T18:00:06Z)
• Aurora: A Foundation Model of the Atmosphere [56.97266186291677]
  We introduce Aurora, a large-scale foundation model of the atmosphere trained on over a million hours of diverse weather and climate data. In under a minute, Aurora produces 5-day global air pollution predictions and 10-day high-resolution weather forecasts.
  arXiv  Detail & Related papers  (2024-05-20T14:45:18Z)
• Surrogate Modeling for Computationally Expensive Simulations of Supernovae in High-Resolution Galaxy Simulations [0.7927502566022343]
  We develop a method combining machine learning and Gibbs sampling to predict how a supernova affects the surrounding gas. Our method can replace the SN sub-grid models and help properly simulate un-resolved SN feedback in galaxy formation simulations.
  arXiv  Detail & Related papers  (2023-11-14T19:00:03Z)
• SE(3)-Stochastic Flow Matching for Protein Backbone Generation [54.951832422425454]
  We introduce FoldFlow, a series of novel generative models of increasing modeling power based on the flow-matching paradigm over $3mathrmD$ rigid motions. Our family of FoldFlowgenerative models offers several advantages over previous approaches to the generative modeling of proteins.
  arXiv  Detail & Related papers  (2023-10-03T19:24:24Z)
• NeuralClothSim: Neural Deformation Fields Meet the Thin Shell Theory [70.10550467873499]
  We propose NeuralClothSim, a new quasistatic cloth simulator using thin shells. Our memory-efficient solver operates on a new continuous coordinate-based surface representation called neural deformation fields.
  arXiv  Detail & Related papers  (2023-08-24T17:59:54Z)
• 3D-Spatiotemporal Forecasting the Expansion of Supernova Shells Using Deep Learning toward High-Resolution Galaxy Simulations [0.0]
  Short integration timesteps for Supernova (SNe) are serious bottlenecks in high-resolution galaxy simulations. We develop a deep learning model, 3D-MIM, to predict a shell expansion after a SN explosion.
  arXiv  Detail & Related papers  (2023-01-31T19:00:32Z)
• Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator [57.57321628587564]
  We introduce Hybrid Graph Network Simulator (HGNS) for learning reservoir simulations of 3D subsurface fluid flows. HGNS consists of a subsurface graph neural network (SGNN) to model the evolution of fluid flows, and a 3D-U-Net to model the evolution of pressure. Using an industry-standard subsurface flow dataset (SPE-10) with 1.1 million cells, we demonstrate that HGNS is able to reduce the inference time up to 18 times compared to standard subsurface simulators.
  arXiv  Detail & Related papers  (2022-06-15T17:29:57Z)
• Satellite galaxy abundance dependency on cosmology in Magneticum simulations [101.18253437732933]
  We build an emulator of satellite abundance based on cosmological parameters. We find that $A$ and $beta$ depend on cosmological parameters, even if weakly. We also show that satellite abundance cosmology dependency differs between full-physics (FP) simulations, dark-matter only (DMO) and non-radiative simulations.
  arXiv  Detail & Related papers  (2021-10-11T18:00:02Z)
• Autoencoder-driven Spiral Representation Learning for Gravitational Wave Surrogate Modelling [47.081318079190595]
  We investigate the existence of underlying structures in the empirical coefficients using autoencoders. We design a spiral module with learnable parameters, that is used as the first layer in a neural network, which learns to map the input space to the coefficients. The spiral module is evaluated on multiple neural network architectures and consistently achieves better speed-accuracy trade-off than baseline models.
  arXiv  Detail & Related papers  (2021-07-09T09:03:08Z)
• Fast and Accurate Non-Linear Predictions of Universes with Deep Learning [21.218297581239664]
  We build a V-Net based model that transforms fast linear predictions into fully nonlinear predictions from numerical simulations. Our NN model learns to emulate the simulations down to small scales and is both faster and more accurate than the current state-of-the-art approximate methods.
  arXiv  Detail & Related papers  (2020-12-01T03:30:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.