Related papers: SIMBA: A Python-based single-point astrochemical s…
PySDM is an open-source Python package for simulating the dynamics of particles undergoing condensational and collisional growth, interacting with a fluid flow and subject to chemical composition changes. It is intended to serve as a…
The study of the chemical composition of the interstellar medium (ISM) requires a strong synergy between laboratory astrophysics, modeling, and observations. In particular, astrochemical models have been developed for decades now and…
The Simulation Environment for Atomistic and Molecular Modeling (SEAMM) is an open-source software package written in Python that provides a graphical interface for setting up, executing, and analyzing molecular and materials simulations.…
The emission lines from ionized nebulae allow us to determine their physical and chemical properties, along with the interstellar extinction. "pyEQUIB" is a pure Python open-source package including several application programming interface…
For modeling the spectra of exoplanets one must know their atmospheric composition. This is necessary because the abundance of molecules, atoms, ions and condensates is needed to construct the total cross-section for the interaction between…
In this paper, we present a new gas-grain chemical code for interstellar clouds written in pure Python (GGCHEMPY). By combining with the high-performance Python compiler Numba, GGCHEMPY is as efficient as the Fortran-based version. With the…
The abundance of dust within galaxies directly influences their evolution. Contemporary models attempt to match this abundance by simulating the processes of dust creation, growth, and destruction. While these models are accurate, they…
This paper presents the first results from a model for chemical evolution that can be applied to N-body cosmological simulations and quantitatively compared to measured stellar abundances from large astronomical surveys. This model…
We develop a Python-based open-source package to analyze the results stemming from ab initio molecular-dynamics simulations of fluids. The package is best suited for applications on natural systems, like silicate and oxide melts,…
Hybrid modelling enhances the accuracy and predictive capability of dynamic models by integrating first principles with data-driven methods, effectively mitigating epistemic uncertainties inherent in mechanistic approaches. However, hybrid…
We present some preliminary results obtained with a new galactic chemodynamical tool under development. In the framework of non-instantaneous recycling approach, we follow the interactions due to star formation and feedback processes. One…
Multiplexed imaging data are revolutionizing our understanding of the composition and organization of tissues and tumors. A critical aspect of such tissue profiling is quantifying the spatial relationship relationships among cells at…
Characterizing the temporal variability of astrophysical sources is key to understanding the underlying physical processes driving their emissions. This work introduces a gammapy_SyLC, a Python package that offers tools to simulate and fit…
Reconstructing the chemical evolution of the Milky Way is crucial for understanding the formation of stars, planets, and galaxies throughout cosmic time. Different studies associated with element production in the early universe and how…
The distribution of chemical abundances and their variation in time are important tools to understand the chemical evolution of galaxies: in particular, the study of chemical evolution models can improve our understanding of the basic…
In recent years, numerical simulations have become indispensable for addressing complex astrophysical problems. The MagnetoHydroDynamics (MHD) framework represents a key tool for investigating the dynamical evolution of astrophysical…
We present a toolkit, CosmoDS, designed to study cosmological models at the background level using dynamical system analysis within the Cobaya framework. Dynamical system analysis is a powerful mathematical approach for studying nonlinear…
Chemistry plays a key role in many astrophysical situations regulating the cooling and the thermal properties of the gas, which are relevant during gravitational collapse, the evolution of disks and the fragmentation process. In order to…
Background and Objective: Deep learning enables tremendous progress in medical image analysis. One driving force of this progress are open-source frameworks like TensorFlow and PyTorch. However, these frameworks rarely address issues…
We substantially update the capabilities of the open-source software instrument Modules for Experiments in Stellar Astrophysics (MESA). MESA can now simultaneously evolve an interacting pair of differentially rotating stars undergoing…