Related papers: Bayesian Posteriors with Stellar Population Synthe…
Models of stellar population synthesis (SPS) are the fundamental tool that relates the physical properties of a galaxy to its spectral energy distribution (SED). In this paper, we present DSPS: a python package for stellar population…
We present an efficient Bayesian SED-fitting framework tailored to multiwavelength pixel photometry from the JWST Advanced Deep Extragalactic Survey (JADES). Our method employs simulation-based inference to enable rapid posterior sampling…
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 present a Bayesian hierarchical framework to analyze photometric galaxy survey data with stellar population synthesis (SPS) models. Our method couples robust modeling of spectral energy distributions with a population model and a noise…
We present SPECULATOR - a fast, accurate, and flexible framework for emulating stellar population synthesis (SPS) models for predicting galaxy spectra and photometry. For emulating spectra, we use principal component analysis to construct a…
The Probabilistic Value-Added Bright Galaxy Survey (PROVABGS) catalog will provide the posterior distributions of physical properties of $>10$ million DESI Bright Galaxy Survey (BGS) galaxies. Each posterior distribution will be inferred…
Galaxy formation and evolution is one of the most active areas of research in astronomy. In recent times there have been several developments on the observational fronts particularly with the discovery of several relations between galaxy…
Next generation photometric and spectroscopic surveys will enable unprecedented tests of the concordance cosmological model and of galaxy formation and evolution. Fully exploiting their potential requires a precise understanding of the…
Principled Bayesian inference of galaxy properties has not previously been performed for wide-area weak lensing surveys with millions of sources. We address this gap by applying the pop-cosmos generative model to perform spectral energy…
Bayesian spectral deconvolution provides a data-driven framework for mathematical model selection and parameter estimation from spectral data. Although highly versatile, it becomes computationally expensive as the number of model…
Accurate estimates of fundamental physical properties of galaxies, such as star formation rates (SFRs) or stellar masses, are essential for testing and constraining models of galaxy formation and evolution. Spectral energy distribution…
We present PopSED, a framework for the population-level inference of galaxy properties from photometric data. Unlike the traditional approach of first analyzing individual galaxies and then combining the results to determine the physical…
The forthcoming CSST wide-field multiband imaging survey will produce seven-band photometric spectral energy distributions (SEDs) for billions of galaxies. The effective extraction of astronomical information from these massive datasets of…
Stellar population synthesis techniques for predicting the observable light emitted by a stellar population have extensive applications in numerous areas of astronomy. However, accurate predictions for small populations of young stars, such…
We demonstrate a GPU-accelerated nested sampling framework for efficient high-dimensional Bayesian inference in cosmology. Using JAX-based neural emulators and likelihoods for cosmic microwave background and cosmic shear analyses, our…
Aims. We present here a new theoretical approach to population synthesis. The aim is to predict colour magnitude diagrams (CMDs) for huge numbers of stars. With this method we generate synthetic CMDs for N-body simulations of galaxies.…
The spectral energy distributions (SEDs) of galaxies are shaped by nearly every physical property of the system, including the star formation history, metal content, abundance pattern, dust mass, grain size distribution, star-dust geometry,…
With the advent of billion-galaxy surveys with complex data, the need of the hour is to efficiently model galaxy spectral energy distributions (SEDs) with robust uncertainty quantification. The combination of Simulation-Based inference…
The simple stellar population models produced by stellar population and spectral synthesis (SPS) codes are used as spectral templates in a variety of astrophysical contexts. In this paper, we test the predictions of four commonly used…
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…