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Found 32 papers, 24 papers with code
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 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.
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.
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.
Predicting the Thermal Sunyaev-Zel'dovich Field using Modular and Equivariant Set-Based Neural Networks
We train neural networks on the IllustrisTNG-300 cosmological simulation to predict the continuous electron pressure field in galaxy clusters from gravity-only simulations.
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.
Constraining reionization with the first measurement of the cross-correlation between the CMB optical-depth fluctuations and the Compton y-map
Since the power spectrum of the electron density fluctuations is constrained by the $\delta\tau$ auto spectrum, the temperature constraints should be only weakly model-dependent on the details of the electron distributions and should be statistically representative of the temperature in ionized bubbles during reionization.
PICO
Cosmology and Nongalactic Astrophysics
Astrophysics of Galaxies
A Bayesian neural network predicts the dissolution of compact planetary systems
Our model, trained directly from short N-body time series of raw orbital elements, is more than two orders of magnitude more accurate at predicting instability times than analytical estimators, while also reducing the bias of existing machine learning algorithms by nearly a factor of three.
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
The Atacama Cosmology Telescope: Delensed Power Spectra and Parameters
Our results show that gravitational lensing of the microwave background is internally consistent within the framework of the standard cosmological model.
Cosmology and Nongalactic Astrophysics High Energy Physics - Phenomenology
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
What is the price of abandoning dark matter? Cosmological constraints on alternative gravity theories
Measurements of the large-scale structure of galaxies at low redshift show much weaker features in the spectrum.
Cosmology and Nongalactic Astrophysics General Relativity and Quantum Cosmology
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
The Atacama Cosmology Telescope: A CMB lensing mass map over 2100 square degrees of sky and its cross-correlation with BOSS-CMASS galaxies
We construct cosmic microwave background lensing mass maps using data from the 2014 and 2015 seasons of observations with the Atacama Cosmology Telescope (ACT).
Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies
The Atacama Cosmology Telescope: Constraints on Cosmic Birefringence
We present new constraints on anisotropic birefringence of the cosmic microwave background polarization using two seasons of data from the Atacama Cosmology Telescope covering $456$ square degrees of sky.
Cosmology and Nongalactic Astrophysics High Energy Physics - Experiment High Energy Physics - Phenomenology
The Atacama Cosmology Telescope: Component-separated maps of CMB temperature and the thermal Sunyaev-Zel'dovich effect
In this paper, we implement a component separation method based on the internal linear combination (ILC) approach which we have designed to optimally account for the anisotropic noise (in the 2D Fourier domain) often found in ground-based CMB experiments.
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
Modeling the Gaia Color-Magnitude Diagram with Bayesian Neural Flows to Constrain Distance Estimates
We demonstrate an algorithm for learning a flexible color-magnitude diagram from noisy parallax and photometry measurements using a normalizing flow, a deep neural network capable of learning an arbitrary multi-dimensional probability distribution.
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
Weak Lensing of Intensity Mapping: the Cosmic Infrared Background
To this end, we generalize the CMB lensing quadratic estimator to any weakly non-Gaussian source field, by deriving the optimal lensing weights.
Cosmology and Nongalactic Astrophysics
Looking through the same lens: shear calibration for LSST, Euclid & WFIRST with stage 4 CMB lensing
We constrain shear calibration biases while simultaneously varying cosmological parameters, galaxy biases and photometric redshift uncertainties.
Cosmology and Nongalactic Astrophysics
Evidence for the kinematic Sunyaev-Zeľdovich effect with ACTPol and velocity reconstruction from BOSS
We use microwave temperature maps from two seasons of data from the Atacama Cosmology Telescope (ACTPol) at 146 GHz, together with the Constant Mass CMASS galaxy sample from the Baryon Oscillation Spectroscopic Survey to measure the kinematic Sunyaev-Ze\v{l}dovich (kSZ) effect over the redshift range z = 0. 4 - 0. 7.
Cosmology and Nongalactic Astrophysics
Joint likelihood function of cluster counts and $n$-point correlation functions: Improving their power through including halo sample variance
This approach enables an approximate derivation of a joint likelihood for the cluster number counts, the weak lensing power spectrum and the bispectrum.
Cosmology and Nongalactic Astrophysics
Efficient Power Spectrum Estimation for High Resolution CMB Maps
Estimation of the angular power spectrum of the Cosmic Microwave Background (CMB) on a small patch of sky is usually plagued by serious spectral leakage, specially when the map has a hard edge.
