Related papers: Probabilistic Forward Modeling of Galaxy Catalogs …
The cosmological redshift of a galaxy's light is inferable from its observable properties in images. Because imaging is much easier to acquire than spectroscopic observations that would allow the identification of distinct line features,…
Galaxy distances and derived radial peculiar velocity catalogs constitute valuable datasets to study the dynamics of the Local Universe. However, such catalogs suffer from biases whose effects increase with the distance. Malmquist biases…
We introduce a Variational Autoencoder (VAE)--Normalizing Flow (NF) framework for rapid probabilistic inference of galaxy properties and emission line fluxes at $z \leq 0.3$ from SDSS \textit{gri} imaging and photometry. Our model…
The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will produce several billion photometric redshifts (photo-$z$'s), enabling cosmological analyses to select a subset of galaxies with the most accurate photo-$z$. We…
We present a method for accurately and precisely inferring photometric dust extinction towards stars at mid-to-high Galactic latitudes using probabilistic machine learning to model the colour-magnitude distribution of zero-extinction stars…
Key cosmological applications require the three-dimensional galaxy distribution on the entire celestial sphere. These include measuring the gravitational pull on the Local Group, estimating the large-scale bulk flow and testing the…
We present a method, PhotoWeb, for estimating photometric redshifts of individual galaxies, and their equivalent distance, with megaparsec and even sub-megaparsec accuracy using the Cosmic Web as a constraint over photo-z estimates.…
Ptychography, as an essential tool for high-resolution and nondestructive material characterization, presents a challenging large-scale nonlinear and non-convex inverse problem; however, its intrinsic photon statistics create clear…
Although many deep-learning-based super-resolution approaches have been proposed in recent years, because no ground truth is available in the inference stage, few can quantify the errors and uncertainties of the super-resolved results. For…
Around $10^5$ strongly lensed galaxies are expected to be discovered with Euclid and the LSST. Utilising these large samples to study the inner structure of lens galaxies requires source redshifts, to turn lens models into mass…
We develop a new method which measures the projected density distribution w_p(r_p)n of photometric galaxies surrounding a set of spectroscopically-identified galaxies, and simultaneously the projected correlation function w_p(r_p) between…
Modern galaxy surveys demand extensive survey volumes and resolutions surpassing current dark matter-only simulations' capabilities. To address this, many methods employ effective bias models on the dark matter field to approximate object…
It has recently been demonstrated that one can accurately derive galaxy morphology from particular primary and secondary isophotal shape estimates in the Sloan Digital Sky Survey imaging catalog. This was accomplished by applying Machine…
Cataloging is challenging in crowded fields because sources are extremely covariant with their neighbors and blending makes even the number of sources ambiguous. We present the first optical probabilistic catalog, cataloging a crowded (~0.1…
Star-forming galaxies with strong nebular and collisional emission lines are privileged target galaxies in forthcoming cosmological large galaxy redshift surveys. We use the COSMOS2015 photometric catalog to model galaxy spectral energy…
Our view of the low-redshift Cosmic Web has been revolutionized by galaxy redshift surveys such as 6dFGS, SDSS and 2MRS. However, the trade-off between depth and angular coverage limits a systematic three-dimensional account of the entire…
We use the deepest and most complete redshift catalog currently available (the Hubble Deep Field (HDF) North supplemented by new HDF South redshift data) to minimize residuals between photometric and spectroscopic redshift estimates. The…
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…
In addition to the maximum likelihood approach, there are two other methods which are commonly used to reconstruct the true redshift distribution from photometric redshift datasets: one uses a deconvolution method, and the other a…
We describe a new method for measuring the true redshift distribution of any set of objects studied only photometrically. The angular cross-correlation between objects in a photometric sample with objects in some spectroscopic sample as a…