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 …

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 …

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 …

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 …

We present the GPry algorithm for fast Bayesian inference of general (non-Gaussian)
posteriors with a moderate number of parameters. GPry does not need any pre-training …

Bayesian inference usually requires running potentially costly inference procedures
separately for every new observation. In contrast, the idea of amortized Bayesian inference …

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

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 …

We look at dark energy from a biology inspired viewpoint by means of the Approximate
Bayesian Computation (ABC) and late time cosmological observations. We find that …

Standard cosmic microwave background (CMB) analyses constrain cosmological and
astrophysical parameters by fitting parametric models to multifrequency power spectra …