Related papers: Attention-based Neural Network Emulators for Multi…
Machine learning can accelerate cosmological inferences that involve many sequential evaluations of computationally expensive data vectors. Previous works in this series have examined how machine learning architectures impact emulator…
The next generation of cosmological surveys is expected to generate unprecedented high-quality data, consequently increasing the already substantial computational costs of Bayesian statistical methods. This will pose a significant challenge…
In this work, we develop a simulation-based model to predict the density split (DSS) and second-order shear and clustering statistics. A simulation-based model has the potential to model highly non-linear scales where current DSS models…
The Lyman-$\alpha$ forest offers a unique avenue for studying the distribution of matter in the high redshift universe and extracting precise constraints on the nature of dark matter, neutrino masses, and other $\Lambda$CDM extensions.…
Galaxy clustering is an important probe in the upcoming China Space Station Telescope (CSST) survey to understand the structure growth and reveal the nature of the dark sector. However, it is a long-term challenge to model this biased…
We analyze Dark Energy Survey (DES) data to constrain a cosmological model where a subset of parameters -- focusing on $\Omega_m$ -- are split into versions associated with structure growth (e.g. $\Omega_m^{\rm grow}$) and expansion history…
We investigate whether neural networks (NNs) can accurately differentiate between growth-rate data of the large-scale structure (LSS) of the Universe simulated via two models: a cosmological constant and $\Lambda$ cold dark matter (CDM)…
Ground and space-based sky surveys enable powerful cosmological probes based on measurements of galaxy properties and the distribution of galaxies in the Universe. These probes include weak lensing, baryon acoustic oscillations, abundance…
We implement a model for the two-point statistics of biased tracers that combines dark matter dynamics from $N$-body simulations with an analytic Lagrangian bias expansion. Using Aemulus, a suite of $N$-body simulations built for emulation…
We construct accurate emulators for the projected and redshift space galaxy correlation functions and excess surface density as measured by galaxy-galaxy lensing, based on Halo Occupation Distribution (HOD) modeling. Using the complete…
We perform a general test of the $\Lambda{\rm CDM}$ and $w {\rm CDM}$ cosmological models by comparing constraints on the geometry of the expansion history to those on the growth of structure. Specifically, we split the total matter energy…
We explore the capability of deep learning to classify cosmic structures. In cosmological simulations, cosmic volumes are segmented into voids, sheets, filaments and knots, according to the distribution and kinematics of dark matter (DM),…
We test the smooth dark energy paradigm using Dark Energy Survey (DES) Year 1 and Year 3 weak lensing and galaxy clustering data. Within the $\Lambda$CDM and $w$CDM model we separate the expansion and structure growth history by splitting…
We present a Neural Network based emulator for the galaxy redshift-space power spectrum that enables several orders of magnitude acceleration in the galaxy clustering parameter inference, while preserving 3$\sigma$ accuracy better than…
Traditional estimators of the galaxy power spectrum and bispectrum are sensitive to the survey geometry. They yield spectra that differ from the true underlying signal since they are convolved with the window function of the survey. For the…
We apply and test a field-level emulator for non-linear cosmic structure formation in a volume matching next-generation surveys. Inferring the cosmological parameters and initial conditions from which the particular galaxy distribution of…
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. We employ Graph Neural Networks (GNNs), architectures designed to…
We reconstruct shear maps and angular power spectra from simulated weakly lensed total intensity (TT) and polarised (EB) maps of the Cosmic Microwave Background (CMB) anisotropies, obtained using Born approximated ray-tracing through the…
Testing a subset of viable cosmological models beyond General Relativity (GR), with implications for cosmic acceleration and the Dark Energy associated with it, is within the reach of Rubin Observatory Legacy Survey of Space and Time (LSST)…
Cosmological parameter constraints from recent galaxy imaging surveys are reaching $2-3\%$-level accuracy. The upcoming Legacy Survey of Space and Time (LSST) of the Vera C. Rubin Observatory will produce sub-percent level measurements of…