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Found 15 papers, 12 papers with code
syren-halofit: A fast, interpretable, high-precision formula for the $Λ$CDM nonlinear matter power spectrum
Our symbolic expressions for $k_\sigma$, $n_{\rm eff}$ and $C$ have root mean squared fractional errors of 0. 8%, 0. 2% and 0. 3%, respectively, for redshifts below 3 and a wide range of cosmologies.
A precise symbolic emulator of the linear matter power spectrum
We obtain an analytic approximation to the linear power spectrum with a root mean squared fractional error of 0. 2% between $k = 9\times10^{-3} - 9 \, h{\rm \, Mpc^{-1}}$ and across a wide range of cosmological parameters, and we provide physical interpretations for various terms in the expression.
Fishnets: Information-Optimal, Scalable Aggregation for Sets and Graphs
Set-based learning is an essential component of modern deep learning and network science.
Evidence Networks: simple losses for fast, amortized, neural Bayesian model comparison
Multiple real-world and synthetic examples illustrate that Evidence Networks are explicitly independent of dimensionality of the parameter space and scale mildly with the complexity of the posterior probability density function.
The Cosmic Graph: Optimal Information Extraction from Large-Scale Structure using Catalogues
We a) demonstrate the high sensitivity of modular graph structure to the underlying cosmology in the noise-free limit, b) show that graph neural network summaries automatically combine mass and clustering information through comparisons to traditional statistics, c) demonstrate that networks can still extract information when catalogues are subject to noisy survey cuts, and d) illustrate how nonlinear IMNN summaries can be used as asymptotically optimal compressed statistics for Bayesian simulation-based inference.
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
Solving high-dimensional parameter inference: marginal posterior densities & Moment Networks
High-dimensional probability density estimation for inference suffers from the "curse of dimensionality".
Perfectly parallel cosmological simulations using spatial comoving Lagrangian acceleration
Existing cosmological simulation methods lack a high degree of parallelism due to the long-range nature of the gravitational force, which limits the size of simulations that can be run at high resolution.
Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics
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
SSSpaNG! Stellar Spectra as Sparse, data-driven, Non-Gaussian processes
With this in mind, we introduce SSSpaNG: a data-driven Gaussian Process model of stellar spectra.
Solar and Stellar Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics
Neural physical engines for inferring the halo mass distribution function
Here we demonstrate a method for determining the halo mass distribution function by learning the tracer bias between density fields and halo catalogues using a neural bias model.
Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics
Automatic physical inference with information maximising neural networks
We anticipate that the automatic physical inference method described in this paper will be essential to obtain both accurate and precise cosmological parameter estimates from complex and large astronomical data sets, including those from LSST and Euclid.
Instrumentation and Methods for Astrophysics
Computing High Accuracy Power Spectra with Pico
This paper presents the second release of Pico (Parameters for the Impatient COsmologist).
Pico: Parameters for the Impatient Cosmologist
In this paper, we demonstrate that using Pico to compute power spectra and likelihoods produces parameter posteriors that are very similar to those using CAMB and the official WMAP3 code, but in only a fraction of the time.
astro-ph
Fast Convolution on the Sphere
We propose fast, exact and efficient algorithms for the convolution of two arbitrary functions on the sphere which speed up computations by a factor \order{\sqrt{N}} compared to present methods where $N$ is the number of pixels.
astro-ph
