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1. AI-assisted superresolution cosmological simulations

   https://doi.org/10.1073/pnas.2022038118  

   Li, Yin; Ni, Yueying; Croft, Rupert A.; Di Matteo, Tiziana; Bird, Simeon; Feng, Yu (May 2021, Proceedings of the National Academy of Sciences)

   null (Ed.)

   Cosmological simulations of galaxy formation are limited by finite computational resources. We draw from the ongoing rapid advances in artificial intelligence (AI; specifically deep learning) to address this problem. Neural networks have been developed to learn from high-resolution (HR) image data and then make accurate superresolution (SR) versions of different low-resolution (LR) images. We apply such techniques to LR cosmological N-body simulations, generating SR versions. Specifically, we are able to enhance the simulation resolution by generating 512 times more particles and predicting their displacements from the initial positions. Therefore, our results can be viewed as simulation realizations themselves, rather than projections, e.g., to their density fields. Furthermore, the generation process is stochastic, enabling us to sample the small-scale modes conditioning on the large-scale environment. Our model learns from only 16 pairs of small-volume LR-HR simulations and is then able to generate SR simulations that successfully reproduce the HR matter power spectrum to percent level up to 16   h − 1 Mpc and the HR halo mass function to within 10 % down to 1 0 11   M ⊙ . We successfully deploy the model in a box 1,000 times larger than the training simulation box, showing that high-resolution mock surveys can be generated rapidly. We conclude that AI assistance has the potential to revolutionize modeling of small-scale galaxy-formation physics in large cosmological volumes. 

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

   https://doi.org/10.1093/mnras/stab2113  

   Ni, Yueying; Li, Yin; Lachance, Patrick; Croft, Rupert A; Di Matteo, Tiziana; Bird, Simeon; Feng, Yu (August 2021, Monthly Notices of the Royal Astronomical Society)

   null (Ed.)

   ABSTRACT In this work, we expand and test the capabilities of our recently developed superresolution (SR) model to generate high-resolution (HR) realizations of the full phase-space matter distribution, including both displacement and velocity, from computationally cheap low-resolution (LR) cosmological N-body simulations. The SR model enhances the simulation resolution by generating 512 times more tracer particles, extending into the deeply nonlinear regime where complex structure formation processes take place. We validate the SR model by deploying the model in 10 test simulations of box size 100 h−1 Mpc, and examine the matter power spectra, bispectra, and two-dimensional power spectra in redshift space. We find the generated SR field matches the true HR result at per cent level down to scales of k ∼ 10 h  Mpc−1. We also identify and inspect dark matter haloes and their substructures. Our SR model generates visually authentic small-scale structures that cannot be resolved by the LR input, and are in good statistical agreement with the real HR results. The SR model performs satisfactorily on the halo occupation distribution, halo correlations in both real and redshift space, and the pairwise velocity distribution, matching the HR results with comparable scatter, thus demonstrating its potential in making mock halo catalogues. The SR technique can be a powerful and promising tool for modelling small-scale galaxy formation physics in large cosmological volumes. 

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3. Multi-Fidelity Emulation for the Matter Power Spectrum using Gaussian Processes

   https://doi.org/10.1093/mnras/stab3114  

   Ho, Ming-Feng; Bird, Simeon; Shelton, Christian R (October 2021, Monthly Notices of the Royal Astronomical Society)

   Abstract We present methods for emulating the matter power spectrum by combining information from cosmological N-body simulations at different resolutions. An emulator allows estimation of simulation output by interpolating across the parameter space of a limited number of simulations. We present the first implementation in cosmology of multi-fidelity emulation, where many low-resolution simulations are combined with a few high-resolution simulations to achieve an increased emulation accuracy. The power spectrum’s dependence on cosmology is learned from the low-resolution simulations, which are in turn calibrated using high-resolution simulations. We show that our multi-fidelity emulator predicts high-fidelity counterparts to percent-level relative accuracy when using only 3 high-fidelity simulations and outperforms a single-fidelity emulator that uses 11 simulations, although we do not attempt to produce a converged emulator with high absolute accuracy. With a fixed number of high-fidelity training simulations, we show that our multi-fidelity emulator is ≃ 100 times better than a single-fidelity emulator at k ≤ 2 hMpc−1, and ≃ 20 times better at 3 ≤ k < 6.4 hMpc−1. Multi-fidelity emulation is fast to train, using only a simple modification to standard Gaussian processes. Our proposed emulator shows a new way to predict non-linear scales by fusing simulations from different fidelities. 

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4. From EMBER to FIRE: predicting high resolution baryon fields from dark matter simulations with deep learning

   https://doi.org/10.1093/mnras/stab3088  

   Bernardini, M; Feldmann, R; Anglés-Alcázar, D; Boylan-Kolchin, M; Bullock, J; Mayer, L; Stadel, J (November 2021, Monthly Notices of the Royal Astronomical Society)

   ABSTRACT Hydrodynamic simulations provide a powerful, but computationally expensive, approach to study the interplay of dark matter and baryons in cosmological structure formation. Here, we introduce the EMulating Baryonic EnRichment (EMBER) Deep Learning framework to predict baryon fields based on dark matter-only simulations thereby reducing computational cost. EMBER comprises two network architectures, U-Net and Wasserstein Generative Adversarial Networks (WGANs), to predict 2D gas and H i densities from dark matter fields. We design the conditional WGANs as stochastic emulators, such that multiple target fields can be sampled from the same dark matter input. For training we combine cosmological volume and zoom-in hydrodynamical simulations from the Feedback in Realistic Environments (FIRE) project to represent a large range of scales. Our fiducial WGAN model reproduces the gas and H i power spectra within 10 per cent accuracy down to ∼10 kpc scales. Furthermore, we investigate the capability of EMBER to predict high resolution baryon fields from low resolution dark matter inputs through upsampling techniques. As a practical application, we use this methodology to emulate high-resolution H i maps for a dark matter simulation of a $$L=100\, \text{Mpc}\, h^{ -1}$$ comoving cosmological box. The gas content of dark matter haloes and the H i column density distributions predicted by EMBER agree well with results of large volume cosmological simulations and abundance matching models. Our method provides a computationally efficient, stochastic emulator for augmenting dark matter only simulations with physically consistent maps of baryon fields. 

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5. Dynamical friction modelling of massive black holes in cosmological simulations and effects on merger rate predictions

   https://doi.org/10.1093/mnras/stab3411  

   Chen, Nianyi; Ni, Yueying; Tremmel, Michael; Di Matteo, Tiziana; Bird, Simeon; DeGraf, Colin; Feng, Yu (December 2021, Monthly Notices of the Royal Astronomical Society)

   ABSTRACT In this work, we establish and test methods for implementing dynamical friction (DF) for massive black hole pairs that form in large volume cosmological hydrodynamical simulations that include galaxy formation and black hole growth. We verify our models and parameters both for individual black hole dynamics and for the black hole population in cosmological volumes. Using our model of DF from collisionless particles, black holes can effectively sink close to the galaxy centre, provided that the black hole’s dynamical mass is at least twice that of the lowest mass resolution particles in the simulation. Gas drag also plays a role in assisting the black holes’ orbital decay, but it is typically less effective than that from collisionless particles, especially after the first billion years of the black hole’s evolution. DF from gas becomes less than $$1{{\ \rm per\ cent}}$$ of DF from collisionless particles for BH masses >107 M⊙. Using our best DF model, we calculate the merger rate down to z = 1.1 using an Lbox = 35 Mpc h−1 simulation box. We predict ∼2 mergers per year for z > 1.1 peaking at z ∼ 2. These merger rates are within the range obtained in previous work using similar resolution hydrodynamical simulations. We show that the rate is enhanced by factor of ∼2 when DF is taken into account in the simulations compared to the no-DF run. This is due to $${\gt}40{{\ \rm per\ cent}}$$ more black holes reaching the centre of their host halo when DF is added. 

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