We present the first simulation-based inference (SBI) of cosmological parameters from field-
level analysis of galaxy clustering. Standard galaxy clustering analyses rely on analyzing …

We present a simulation-based inference (SBI) cosmological analysis of cosmic shear two-
point statistics from the fourth weak gravitational lensing data release of the ESO Kilo …

Traditional probabilistic fatigue life test requires long time, a large number of samples and
high cost due to dispersion, randomness and complexity. A new data-driven probabilistic …

Cosmological inferences typically rely on explicit expressions for the likelihood and
covariance of the data vector, which normally consists of a set of summary statistics …

Persistent homology naturally addresses the multi-scale topological characteristics of the
large-scale structure as a distribution of clusters, loops, and voids. We apply this tool to the …

Abstract Simulation-Based Inference of Galaxies (SimBIG) is a forward modeling framework
for analyzing galaxy clustering using simulation-based inference. In this work, we present …

The standard Λ CDM cosmological model predicts the presence of cold dark matter, with the
current accelerated expansion of the Universe driven by dark energy. This model has …

In recent years, there has been a remarkable development of simulation-based inference
(SBI) algorithms, and they have now been applied across a wide range of astrophysical and …

Convolutional neural networks (CNNs) have recently been applied to cosmological fields—
weak lensing mass maps and Galaxy maps. However, cosmological maps differ in several …

Many recent works in simulation-based inference (SBI) rely on deep generative models to
approximate complex, high-dimensional posterior distributions. However, evaluating …