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Found 38 papers, 24 papers with code
Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets
Deep learning models have been shown to outperform methods that rely on summary statistics, like the power spectrum, in extracting information from complex cosmological data sets.
Field-level simulation-based inference with galaxy catalogs: the impact of systematic effects
In particular, de Santi et al. (2023) developed models that could accurately infer the value of $\Omega_{\rm m}$ from catalogs that only contain the positions and radial velocities of galaxies that are robust to uncertainties in astrophysics and subgrid models.
The CAMELS project: Expanding the galaxy formation model space with new ASTRID and 28-parameter TNG and SIMBA suites
We present CAMELS-ASTRID, the third suite of hydrodynamical simulations in the Cosmology and Astrophysics with MachinE Learning (CAMELS) project, along with new simulation sets that extend the model parameter space based on the previous frameworks of CAMELS-TNG and CAMELS-SIMBA, to provide broader training sets and testing grounds for machine-learning algorithms designed for cosmological studies.
Robust Field-level Likelihood-free Inference with Galaxies
We train graph neural networks to perform field-level likelihood-free inference using galaxy catalogs from state-of-the-art hydrodynamic simulations of the CAMELS project.
Robust field-level inference with dark matter halos
More importantly, we find that our models are very robust: they can infer the value of $\Omega_{\rm m}$ and $\sigma_8$ when tested using halo catalogues from thousands of N-body simulations run with five different N-body codes: Abacus, CUBEP$^3$M, Enzo, PKDGrav3, and Ramses.
The SZ flux-mass ($Y$-$M$) relation at low halo masses: improvements with symbolic regression and strong constraints on baryonic feedback
Our results can be useful for using upcoming SZ surveys (e. g., SO, CMB-S4) and galaxy surveys (e. g., DESI and Rubin) to constrain the nature of baryonic feedback.
Field Level Neural Network Emulator for Cosmological N-body Simulations
We build a field level emulator for cosmic structure formation that is accurate in the nonlinear regime.
Simple lessons from complex learning: what a neural network model learns about cosmic structure formation
We find our model generalizes well to these well understood scenarios, demonstrating that the networks have inferred general physical principles and learned the nonlinear mode couplings from the complex, random Gaussian training data.
Fast and realistic large-scale structure from machine-learning-augmented random field simulations
Producing thousands of simulations of the dark matter distribution in the Universe with increasing precision is a challenging but critical task to facilitate the exploitation of current and forthcoming cosmological surveys.
Learning cosmology and clustering with cosmic graphs
We train deep learning models on thousands of galaxy catalogues from the state-of-the-art hydrodynamic simulations of the CAMELS project to perform regression and inference.
Machine Learning and Cosmology
Methods based on machine learning have recently made substantial inroads in many corners of cosmology.
The CAMELS project: public data release
The Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project was developed to combine cosmology with astrophysics through thousands of cosmological hydrodynamic simulations and machine learning.
Augmenting astrophysical scaling relations with machine learning: application to reducing the Sunyaev-Zeldovich flux-mass scatter
Using SR on the data from the IllustrisTNG hydrodynamical simulation, we find a new proxy for cluster mass which combines $Y_\mathrm{SZ}$ and concentration of ionized gas ($c_\mathrm{gas}$): $M \propto Y_\mathrm{conc}^{3/5} \equiv Y_\mathrm{SZ}^{3/5} (1-A\, c_\mathrm{gas})$.
Weighing the Milky Way and Andromeda with Artificial Intelligence
We present new constraints on the masses of the halos hosting the Milky Way and Andromeda galaxies derived using graph neural networks.
Inferring halo masses with Graph Neural Networks
Furthermore, a GNN trained on a suite of simulations is able to preserve part of its accuracy when tested on simulations run with a different code that utilizes a distinct subgrid physics model, showing the robustness of our method.
Ranked #1 on
Graph Learning
on CAMELS
The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligence
We present the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) Multifield Dataset, CMD, a collection of hundreds of thousands of 2D maps and 3D grids containing many different properties of cosmic gas, dark matter, and stars from 2, 000 distinct simulated universes at several cosmic times.
Robust marginalization of baryonic effects for cosmological inference at the field level
We train neural networks to perform likelihood-free inference from $(25\, h^{-1}{\rm Mpc})^2$ 2D maps containing the total mass surface density from thousands of hydrodynamic simulations of the CAMELS project.
Multifield Cosmology with Artificial Intelligence
Although our maps only cover a small area of $(25~h^{-1}{\rm Mpc})^2$, and the different fields are contaminated by astrophysical effects in very different ways, our networks can infer the values of $\Omega_{\rm m}$ and $\sigma_8$ with a few percent level precision for most of the fields.
Learning the Evolution of the Universe in N-body Simulations
Understanding the physics of large cosmological surveys down to small (nonlinear) scales will significantly improve our knowledge of the Universe.
Neural networks as optimal estimators to marginalize over baryonic effects
For this data, we show that neural networks can 1) extract the maximum available cosmological information, 2) marginalize over baryonic effects, and 3) extract cosmological information that is buried in the regime dominated by baryonic physics.
Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics
deep21: a Deep Learning Method for 21cm Foreground Removal
We seek to remove foreground contaminants from 21cm intensity mapping observations.
The CAMELS project: Cosmology and Astrophysics with MachinE Learning Simulations
We present the Cosmology and Astrophysics with MachinE Learning Simulations --CAMELS-- project.
Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics
HInet: Generating neutral hydrogen from dark matter with neural networks
Upcoming 21cm surveys will map the spatial distribution of cosmic neutral hydrogen (HI) over very large cosmological volumes.
Cosmology and Nongalactic Astrophysics
Teaching neural networks to generate Fast Sunyaev Zel'dovich Maps
The thermal Sunyaev-Zel'dovich (tSZ) and the kinematic Sunyaev-Zel'dovich (kSZ) effects trace the distribution of electron pressure and momentum in the hot Universe.
Cosmology and Nongalactic Astrophysics
Removing Astrophysics in 21 cm maps with Neural Networks
From these simulations we produce tens of thousands of 21 cm maps at redshifts $10\leq z\leq 20$.
Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies
The Effective Halo Model: Creating a Physical and Accurate Model of the Matter Power Spectrum and Cluster Counts
We introduce a physically-motivated model of the matter power spectrum, based on the halo model and perturbation theory.
Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics High Energy Physics - Phenomenology High Energy Physics - Theory
Super-resolution emulator of cosmological simulations using deep physical models
We present an extension of our recently developed Wasserstein optimized model to emulate accurate high-resolution features from computationally cheaper low-resolution cosmological simulations.
Cosmology and Nongalactic Astrophysics
From Dark Matter to Galaxies with Convolutional Neural Networks
Cosmological simulations play an important role in the interpretation of astronomical data, in particular in comparing observed data to our theoretical expectations.
Learning neutrino effects in Cosmology with Convolutional Neural Networks
In this work, we propose a new method, based on a deep learning network, to quickly generate simulations with massive neutrinos from standard $\Lambda$CDM simulations without neutrinos.
Baryonic effects on the matter bispectrum
The large-scale clustering of matter is impacted by baryonic physics, particularly AGN feedback.
Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies
The Quijote simulations
The Quijote simulations are a set of 44, 100 full N-body simulations spanning more than 7, 000 cosmological models in the $\{\Omega_{\rm m}, \Omega_{\rm b}, h, n_s, \sigma_8, M_\nu, w \}$ hyperplane.
Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics
HIGAN: Cosmic Neutral Hydrogen with Generative Adversarial Networks
One of the most promising ways to observe the Universe is by detecting the 21cm emission from cosmic neutral hydrogen (HI) through radio-telescopes.
From Dark Matter to Galaxies with Convolutional Networks
In combination with current and upcoming data from cosmological observations, our method has the potential to answer fundamental questions about our Universe with the highest accuracy.
Inflation and Early Dark Energy with a Stage II Hydrogen Intensity Mapping Experiment
This white paper envisions a revolutionary post-DESI, post-LSST dark energy program based on intensity mapping of the redshifted 21cm emission line from neutral hydrogen at radio frequencies.
Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics High Energy Physics - Experiment
Statistical properties of paired fixed fields
We quantify the statistical improvement brought by these simulations, over standard ones, on different power spectra such as matter, halos, CDM, gas, stars, black-holes and magnetic fields, finding that they can reduce their variance by factors as large as $10^6$.
Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics
Simulating cosmologies beyond $Λ$CDM with PINOCCHIO
We demonstrate that the abundance of halos in cosmologies with massless and massive neutrinos from PINOCCHIO matches very well the outcome of simulations, and point out that PINOCCHIO can reproduce the $\Omega_\nu-\sigma_8$ degeneracy that affects the halo mass function.
Cosmology and Nongalactic Astrophysics
Cosmology with massive neutrinos III: the halo mass function and an application to galaxy clusters
We find that, in a massive neutrino cosmology, our correction to the halo mass function produces a shift in the $\sigma_8(\Omega_{\rm m}/0. 27)^\gamma$ relation which can be quantified as $\Delta \gamma \sim 0. 05$ and $\Delta \gamma \sim 0. 14$ assuming one ($N_\nu=1$) or three ($N_\nu=3$) degenerate massive neutrino, respectively.
Cosmology and Nongalactic Astrophysics High Energy Physics - Phenomenology
Cosmology with massive neutrinos I: towards a realistic modeling of the relation between matter, haloes and galaxies
We compute the bias between the spatial distribution of dark matter haloes and the overall matter and cold dark matter distributions using statistical tools such as the power spectrum and the two-point correlation function.
Cosmology and Nongalactic Astrophysics High Energy Physics - Phenomenology
