Related papers: pysersic: A Python package for determining galaxy …
Gaussian process is a theoretically appealing model for nonparametric analysis, but its computational cumbersomeness hinders its use in large scale and the existing reduced-rank solutions are usually heuristic. In this work, we propose a…
In this paper, we introduce Pysimfrac, a open-source python library for generating 3-D synthetic fracture realizations, integrating with fluid simulators, and performing analysis. Pysimfrac allows the user to specify one of three fracture…
Polytropes have gained renewed interest because they account for several seemingly-disconnected observational properties of galaxies. Here we study if polytropes are also able to explain the stellar mass distribution within galaxies. We…
We present PyXtal, a new package based on the Python programming language, used to generate structures with specific symmetry and chemical compositions for both atomic and molecular systems. This soft ware provides support for various…
Bayesian Inference is a powerful approach to data analysis that is based almost entirely on probability theory. In this approach, probabilities model {\it uncertainty} rather than randomness or variability. This thesis is composed of a…
Approximate Bayesian Computation (ABC) enables parameter inference for complex physical systems in cases where the true likelihood function is unknown, unavailable, or computationally too expensive. It relies on the forward simulation of…
It is time-consuming and error-prone to implement inference procedures for each new probabilistic model. Probabilistic programming addresses this problem by allowing a user to specify the model and having a compiler automatically generate…
Taking advantage of HST CANDELS data, we analyze the lowest redshift (z<0.5) massive galaxies in order to disentangle their structural constituents and study possible faint non-axis-symmetric features. Due to the excellent HST spatial…
This paper introduces the first release of Pytearcat, a Python package developed to compute tensor algebra operations in the context of theoretical physics, for instance, in general relativity. Given that working with tensors can become a…
We present GalSyn (Galaxy Synthesizer), a modular and flexible Python package for generating synthetic spectrophotometric observations from hydrodynamical galaxy simulations. GalSyn employs a particle-by-particle spectral modeling approach…
Bayesian models of cognition have gained considerable traction in computational neuroscience and psychiatry. Their scopes are now expected to expand rapidly to artificial intelligence, providing general inference frameworks to support…
We demonstrate the potential of an emulator-based approach to analyzing galaxy formation models in the domain where constraining data is limited. We have applied the open-source Python package PRISM to the galaxy formation model Meraxes.…
Stellar populations serve as a fossil record of galaxy formation and evolution, providing crucial information about the history of star formation and galaxy evolution. The color-magnitude diagram (CMD) stands out as the most accurate tool…
We present a unified framework to derive fundamental stellar parameters by combining all available observational and theoretical information for a star. The algorithm relies on the method of Bayesian inference, which for the first time…
Understanding astrophysical and cosmological processes can be challenging due to their complexity and lack of intuitive analogies. To address this, we present \texttt{AstronomyCalc}, a Python package specifically designed to aid…
We introduce PHI, a fully Bayesian Markov-chain Monte Carlo algorithm designed for the structural decomposition of galaxy images. PHI uses a triple layer approach to effectively and efficiently explore the complex parameter space. Combining…
We present single-S\'ersic two-dimensional model fits to 167,600 galaxies modelled independently in the ugrizYJHK bandpasses using reprocessed Sloan Digital Sky Survey Data Release Seven (SDSS DR7) and UKIRT Infrared Deep Sky Survey Large…
We present a unified algorithmic framework for the numerical solution, constrained optimization, and physics-informed learning of PDEs with a variational structure. Our framework is based on a Galerkin discretization of the underlying…
Structural parameters are normally extracted from observed galaxies by fitting analytic light profiles to the observations. Obtaining accurate fits to high-resolution images is a computationally expensive task, requiring many model…
We present the first public release of our Bayesian inference tool, Bayes-X, for the analysis of X-ray observations of galaxy clusters. We illustrate the use of Bayes-X by analysing a set of four simulated clusters at z=0.2-0.9 as they…