Related papers: GalSBI: Phenomenological galaxy population model f…
We investigate the evolution of the galaxy stellar mass function at high-redshift ($z\ge 5$) using a pair of large cosmological hydrodynamical simulations: {\em MassiveBlack} and {\em MassiveBlack-II}. By combining these simulations we can…
The availability of large, public, multi-modal astronomical datasets presents an opportunity to execute novel research that straddles the line between science of AI and science of astronomy. Photometric redshift estimation is a…
Weak gravitational lensing is a powerful probe of the dark sector, once measurement systematic errors can be controlled. In Refregier & Amara (2014), a calibration method based on forward modeling, called MCCL, was proposed. This relies on…
We introduce a new galaxy image decomposition tool, GALPHAT (GALaxy PHotometric ATtributes), to provide full posterior probability distributions and reliable confidence intervals for all model parameters. GALPHAT is designed to yield a high…
We use the combined photometric SDSS + GALEX database to look for populations of luminous blue star-forming galaxies. These were initially identified from such a sample at redshifts near 0.4, using SDSS spectra. We make use of the colour…
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 present halo model predictions for the expected angular clustering and associated errors from the completed Sloan Digital Sky Survey (SDSS) photometric galaxy sample. These results are used to constrain halo model parameters under the…
In this paper, we assemble a well-defined sample of early-type gravitational lenses extracted from a large collection of 158 systems, and use the redshift distribution of galactic-scale lenses to test the standard cosmological model…
In the absence of numerous gravitational-wave detections with confirmed electromagnetic counterparts, the "dark siren" method has emerged as a leading technique of gravitational-wave cosmology. The method allows redshift information of such…
Simulation based inference has seen increasing interest in the past few years as a promising approach to model the non linear scales of galaxy clustering. The common approach using Gaussian process is to train an emulator over the…
Data-driven approaches using deep learning are emerging as powerful techniques to extract non-Gaussian information from cosmological large-scale structure. This work presents the first simulation-based inference (SBI) pipeline that combines…
Understanding the epochs of cosmic dawn and reionisation requires us to leverage multi-wavelength and multi-tracer observations, with each dataset providing a complimentary piece of the puzzle. To interpret such data, we update the public…
The data on spectroscopic galaxy clustering collected by the Dark Energy Spectroscopic Instrument (DESI) will allow the significant detection of subtle features in the galaxy two-point correlation in redshift space, beyond the "standard"…
We present a maximum-likelihood analysis of galaxy-galaxy lensing effects in galaxy clusters and in the field. The aim is to determine the accuracy and robustness of constraints that can be obtained on galaxy halo properties in both…
Many statistical models in cosmology can be simulated forwards but have intractable likelihood functions. Likelihood-free inference methods allow us to perform Bayesian inference from these models using only forward simulations, free from…
Semi-analytic models are a widely used approach to simulate galaxy properties within a cosmological framework, relying on simplified yet physically motivated prescriptions. They have also proven to be an efficient alternative for generating…
We present a proof-of-concept analysis of photometric redshifts with Bayesian priors on physical properties of galaxies. This concept is particularly suited for upcoming/on-going large imaging surveys, in which only several broad-band…
We present results exploring the role that probabilistic deep learning models can play in cosmology from large scale astronomical surveys through estimating the distances to galaxies (redshifts) from photometry. Due to the massive scale of…
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…
In recent years, constraints on the Hubble parameter using multiple dark sirens have been made,relying on a galaxy catalogue, correlations between the mass and redshift distributions, or both. Those studies have typically used only…