Related papers: Identifying kinematic structures in simulated gala…
We study the intrinsic 3D shapes of quiescent galaxies over the last half of cosmic history based on their axial ratio distribution. To this end, we construct a sample of unprecedented size, exploiting multi-wavelength $u$-to-$K_s$…
Intrinsic galaxy alignments constitute the major astrophysical systematic of forthcoming weak gravitational lensing surveys but also yield unique insights into galaxy formation and evolution. We build analytic models for the distribution of…
We present a novel unsupervised learning approach to automatically segment and label images in astronomical surveys. Automation of this procedure will be essential as next-generation surveys enter the petabyte scale: data volumes will…
This article presents a novel approach to construct Intrinsic Gaussian Processes for regression on unknown manifolds with probabilistic metrics (GPUM) in point clouds. In many real world applications, one often encounters high dimensional…
We present Classification of Cluster GAlaxy MEmbers (C$^2$-GaMe), a classification algorithm based on a suite of machine learning models that differentiates galaxies into orbiting, infalling, and background (interloper) populations, using…
Distinguishing active galaxies from star-forming galaxies is essential for understanding galaxy evolution. Diagnostic methods like the BPT (Baldwin, Phillips, and Terlevich) diagram use optical emission-line ratios to separate galaxies.…
Classification of galaxies is traditionally associated with their morphologies through visual inspection of images. The amount of data to come renders this task inhuman and Machine Learning (mainly Deep Learning) has been called to the…
We use high resolution simulations to study the formation and distribution of galaxies within a cluster which forms hierarchically. We follow both dark matter and baryonic gas which is subject to thermal pressure, shocks and radiative…
We introduce a novel method for discerning optical telescope images of stars from those of galaxies using Gaussian processes (GPs). Although applications of GPs often struggle in high-dimensional data modalities such as optical image…
We present an image-based method for comparing the structural properties of galaxies produced in hydrodynamical simulations to real galaxies in the Sloan Digital Sky Survey. The key feature of our work is the introduction of extensive…
Understanding the evolution of galaxies provides crucial insights into a broad range of aspects in astrophysics, including structure formation and growth, the nature of dark energy and dark matter, baryonic physics, and more. It is,…
We present an unsupervised machine learning technique that automatically segments and labels galaxies in astronomical imaging surveys using only pixel data. Distinct from previous unsupervised machine learning approaches used in astronomy…
The kinematic characterization of different galaxy populations is a key observational input to distinguish between different galaxy evolutionary scenarios, since it helps to determine the number ratio of rotating disks to mergers at…
Modern cosmological hydrodynamical galaxy simulations provide tens of thousands of reasonably realistic synthetic galaxies across cosmic time. However, quantitatively assessing the level of realism of simulated universes in comparison to…
In this two-paper series, we present a straightforward mathematical model for synthesizing quasar absorption line profiles from sight lines through idealized, spatial-kinematic models of the circumgalactic medium (CGM) and their host…
Large photometric surveys provide a rich source of observations of quiescent galaxies, including a surprisingly large population at z>1. However, identifying large, but clean, samples of quiescent galaxies has proven difficult because of…
Understanding the formation and evolution of galaxy populations requires robust classification and characterisation techniques that jointly account for internal galaxy properties and environment. We analyse $5,306$ galaxies from the Galaxy…
Multi-band images of galaxies reveal a huge amount of information about their morphology and structure. However, inferring properties of the underlying stellar populations such as age, metallicity or kinematics from those images is…
A fundamental prediction of the LambdaCDM cosmology is the hierarchical build-up of structure and therefore the successive merging of galaxies into more massive ones. As one can only observe galaxies at one specific time in cosmic history,…
We investigate the connection between the morphology and internal kinematics of the stellar component of central galaxies with mass $M_\star > {10}^{9.5} {\rm M}_\odot$ in the EAGLE simulations. We compare several kinematic diagnostics…