Related papers: A Demonstration of a Neural Network as a Bridge Be…
A simple, fully connected neural network with a single hidden layer is used to estimate stellar masses for star-forming galaxies. The model is trained on broad-band photometry - from far-ultraviolet to mid-infrared wavelengths - generated…
Galaxies grow and evolve in dark matter halos. Because dark matter is not visible, galaxies' halo masses ($\rm{M}_{\rm{halo}}$) must be inferred indirectly. We present a graph neural network (GNN) model for predicting $\rm{M}_{\rm{halo}}$…
Maps of cosmic structure produced by galaxy surveys are one of the key tools for answering fundamental questions about the Universe. Accurate theoretical predictions for these quantities are needed to maximize the scientific return of these…
Galaxy groups are essential for studying the distribution of matter on a large scale in redshift surveys and for deciphering the link between galaxy traits and their associated halos. In this work, we propose a widely applicable method for…
Estimating stellar masses for billions of galaxies in upcoming surveys requires methods that are both accurate and computationally efficient. We present a new approach using symbolic regression trained on a simulation to derive simple,…
Recent work has suggested that mid-IR wavelengths are optimal for estimating the mass-to-light ratios of stellar populations and hence the stellar masses of galaxies. We compare stellar masses deduced from spectral energy distribution (SED)…
Conventional galaxy mass estimation methods suffer from model assumptions and degeneracies. Machine learning, which reduces the reliance on such assumptions, can be used to determine how well present-day observations can yield predictions…
The merging history of galaxies can be traced with studies of dynamically close pairs. These consist of a massive primary galaxy and a less massive secondary (or satellite) galaxy. The study of the stellar populations of secondary (lower…
Aims. We aim to perform consistent comparisons between observations and simulations on the mass dependence of the galaxy major merger fraction at low redshift over an unprecedentedly wide range of stellar masses (10^9 to 10^12 solar…
Quantifying the contribution of mergers to the stellar mass of galaxies is key for constraining the mechanisms of galaxy assembly across cosmic time. However, the mapping between observable galaxy properties and merger histories is not…
Mergers are an important aspect of galaxy formation and evolution. We aim to test whether deep learning techniques can be used to reproduce visual classification of observations, physical classification of simulations and highlight any…
We train deep learning models on thousands of galaxy catalogues from the state-of-the-art hydrodynamic simulations of the CAMELS project to perform regression and inference. We employ Graph Neural Networks (GNNs), architectures designed to…
We use sparse regression methods (SRM) to build accurate and explainable models that predict the stellar mass of central and satellite galaxies as a function of properties of their host dark matter halos. SRM are machine learning algorithms…
This paper describes the first catalogue of photometrically-derived stellar mass estimates for intermediate-redshift (z < 0.65) galaxies in the Galaxy And Mass Assembly (GAMA) spectroscopic redshift survey. These masses, as well as the full…
We present a simulation-based inference framework using a convolutional neural network to infer dynamical masses of galaxy clusters from their observed 3D projected phase-space distribution, which consists of the projected galaxy positions…
Galactic rotation curves are crucial for understanding the distribution of mass in galaxies. Despite advances in precision observations, there are discrepancies between the inferred mass from luminosity and the observed rotational…
Cosmological galaxy formation simulations are still limited by their spatial/mass resolution and cannot model from first principles some of the processes, like star formation, that are key in driving galaxy evolution. As a consequence they…
From a volume limited sample of 45,542 galaxies and 6,000 groups with $z \leq 0.213$ we use an adapted minimal spanning tree algorithm to identify and classify large scale structures within the Galaxy and Mass Assembly (GAMA) survey. Using…
We apply four statistical learning methods to a sample of $7941$ galaxies ($z<0.06$) from the Galaxy and Mass Assembly (GAMA) survey to test the feasibility of using automated algorithms to classify galaxies. Using $10$ features measured…
We present predictions for the galaxy-galaxy lensing profile from the EAGLE hydrodynamical cosmological simulation at redshift z=0.18, in the spatial range 0.02 < R/(Mpc/h) < 2, and for five logarithmically equi-spaced stellar mass bins in…