Related papers: galsbi: A Python package for the GalSBI galaxy pop…
Characterization of the morphology of strongly lensed galaxies is challenging because images of such galaxies are typically highly distorted. Lens modeling and source plane reconstruction is one approach that can provide reasonably…
Models of Stellar Population Synthesis (SPS) provide a predictive framework for the spectral energy distribution (SED) of a galaxy. SPS predictions can be computationally intensive, creating a bottleneck for attempts to infer the physical…
The standard approach to inference from cosmic large-scale structure data employs summary statistics that are compared to analytic models in a Gaussian likelihood with pre-computed covariance. To overcome the idealising assumptions about…
We present an efficient Bayesian method for estimating individual photometric redshifts and galaxy properties under a pre-trained population model (pop-cosmos) that was calibrated using purely photometric data. This model specifies a prior…
We introduce galmoss, a python-based, torch-powered tool for two-dimensional fitting of galaxy profiles. By seamlessly enabling GPU parallelization, galmoss meets the high computational demands of large-scale galaxy surveys, placing galaxy…
Datasets with tens of millions of galaxies present new challenges for the analysis of spatial clustering. We have built a framework that integrates a database of object catalogs, tools for creating masks of bad regions, and a fast (NlogN)…
Stellar population synthesis is a crucial methodology in astrophysics, enabling the interpretation of the integrated light of galaxies and stellar clusters. By combining empirical and/or theoretical libraries of the spectral energy…
We introduce GalaxyGenius, a Python package designed to produce synthetic galaxy images tailored to different telescopes based on hydrodynamical simulations. Its implementation will support and advance research on galaxies in the era of…
Understanding $\textit{galaxy bias}$ -- that is the statistical relation between matter and galaxies -- is of key importance for extracting cosmological information from galaxy surveys. While the bias function $f$ -- that is the probability…
I present StarEstate, an open-source Python package for producing rapid, statistically robust galactic population synthesis models. By utilizing optimized pre-calculated inverse-cumulative distribution function samplers, the tool generates…
In many areas of extragalactic astrophysics, we need to convert the luminosity of a galaxy into its stellar mass. In this work, we aim to find a simple and effective formula to estimate the stellar mass from the images of galaxies delivered…
Now that large databases of resolved galaxy images are provided by modern imaging surveys, advanced morphological studies can be envisioned, urging for well defined calibration samples. We present the EFIGI catalogue, a multiwavelength…
Gauss-Hermite and Gauss-Laguerre ("shapelet") decompositions of images have become important tools in galaxy modeling, particularly for the purpose of extracting ellipticity and morphological information from astronomical data. However, the…
We present a python package "ScalPy" for studying the late time scalar field cosmology for a wide variety of scalar field models, namely the quintessence, tachyon and Galileon model. The package solves the autonomous system of equations for…
Modern cosmological simulations model the universe with increasing sophistication and at higher spatial and temporal resolutions. These enhancements permit detailed comparisons between the simulation outputs and real observational data.…
For the next generation of spectroscopic galaxy surveys, it is important to forecast their performances and to accurately interpret their large data sets. For this purpose, it is necessary to consistently simulate different populations of…
We present EzGal, a flexible python program designed to easily generate observable parameters (magnitudes, colors, mass-to-light ratios) for any stellar population synthesis (SPS) model. As has been demonstrated by various authors, the…
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…
Context. Measuring how the physical properties of galaxies change across cosmic times is essential to understand galaxy formation and evolution. With the advent of numerous ground-based and space-borne instruments launched over the past few…
In this work, we present a scalable approach for inferring the dark energy equation-of-state parameter ($w$) from a population of strong gravitational lens images using Simulation-Based Inference (SBI). Strong gravitational lensing offers…