Related papers: Deep learning for intensity mapping observations: …
The statistical power of weak lensing measurements is principally driven by the number of high redshift galaxies whose shapes are resolved. Conventional wisdom and physical intuition suggest this is optimised by deep imaging at long (red or…
Optical emission-line ratios in star-forming galaxies at $z \sim 3$-8, such as [OIII]/H$\beta$ and [OIII]/[OII], are strongly offset from those at $z \sim 0$-2, pointing to more extreme ionization and ISM conditions in the early Universe.…
We present a new approach based on Supervised Machine Learning (SML) algorithms to infer key physical properties of galaxies (density, metallicity, column density and ionization parameter) from their emission line spectra. We introduce a…
We present a sample of $\sim 1000$ emission line galaxies at $z=0.4-4.7$ from the $\sim0.7$deg$^2$ High-$z$ Emission Line Survey (HiZELS) in the Bo\"otes field identified with a suite of six narrow-band filters at $\approx 0.4-2.1$ $\mu$m.…
Upcoming large astronomical surveys are expected to capture an unprecedented number of strong gravitational lensing systems. Deep learning is emerging as a promising practical tool for the detection and quantification of these galaxy-scale…
Studies of the distribution and evolution of galaxies are of fundamental importance to modern cosmology; these studies, however, are hampered by the complexity of the competing effects of spectral and density evolution. Constructing a…
Line-intensity mapping, being an imperfect observation of the line-intensity field in a cosmological volume, will be subject to various anisotropies introduced in observation. Existing literature in the context of CO and [C II]…
We propose a new approach for measuring the mass profile and shape of groups and clusters of galaxies, which uses lensing magnification of distant background galaxies. The main advantage of lensing magnification is that, unlike lensing…
Constraints on primordial non-Gaussianity (PNG) will shed light on the origin of primordial fluctuations and the physics of the early universe. The intensity mapping technique is a promising probe of structure formation on large scales; at…
Cosmic microwave background (CMB) experiments that constrain the tensor-to-scalar ratio $r$ are now approaching the sensitivity at which delensing---removing the $B$ modes induced by the gravitational lensing of large-scale structure---is…
Line Intensity Mapping (LIM) has garnered attention as a powerful cosmological probe, with next-generation instruments such as SPHEREx preparing to map the evolution of large-scale structure during the Epoch of Reionization (EoR).…
We invoke a Gaussian mixture model (GMM) to jointly analyse two traditional emission-line classification schemes of galaxy ionization sources: the Baldwin-Phillips-Terlevich (BPT) and $\rm W_{H\alpha}$ vs. [NII]/H$\alpha$ (WHAN) diagrams,…
Deep learning has emerged as a technique of choice for rapid feature extraction across imaging disciplines, allowing rapid conversion of the data streams to spatial or spatiotemporal arrays of features of interest. However, applications of…
Optical emission line diagnostics, which are a common tool to constrain the properties of the interstellar medium (ISM) of galaxies, become progressively inaccessible at higher redshifts for ground-based facilities. Far-infrared (FIR)…
One of the main goals of modern observational cosmology is to map the large scale structure of the Universe. A potentially powerful approach for doing this would be to exploit three-dimensional spectral maps, i.e. the specific intensity of…
Eighteen candidates for emission line galaxies were discovered in a narrow-band infrared survey that targeted the redshifts of damped Lyman-alpha or metal lines in the spectra of quasars. The presence of emission lines is inferred from the…
We use numerical simulations to study the effects of the patchiness of a partly reionized intergalactic medium (IGM) on the observability of Ly-alpha emitters (LAEs) at high redshifts (z ~ 6). We present a new model that divides the…
Cosmology inference of galaxy clustering at the field level with the EFT likelihood in principle allows for extracting all non-Gaussian information from quasi-linear scales, while robustly marginalizing over any astrophysical uncertainties.…
We explore the capability of deep learning to classify cosmic structures. In cosmological simulations, cosmic volumes are segmented into voids, sheets, filaments and knots, according to the distribution and kinematics of dark matter (DM),…
Intensity mapping -- the large-scale mapping of selected spectral lines without resolving individual sources -- is quickly emerging as an efficient way to conduct large cosmological surveys. Multiple surveys covering a variety of lines…