Related papers: Propagating data-driven galaxy redshift distributi…
Latent factor models are widely used to discover and adjust for hidden variation in modern applications. However, most methods do not fully account for uncertainty in the latent factors, which can lead to miscalibrated inferences such as…
Intrinsic alignments (IAs) of galaxies are an important contaminant for cosmic shear studies, but the modelling is complicated by the dependence of the signal on the source galaxy sample. In this paper, we use the halo model formalism to…
The 2dF Galaxy Redshift Survey is the first to observe more than 100,000 redshifts, making possible precise measurements of many aspects of galaxy clustering. The spatial distribution of galaxies can be studied as a function of galaxy…
We present a simple method for evaluating the nonlinear biasing function of galaxies from a redshift survey. The nonlinear biasing is characterized by the conditional mean of the galaxy density fluctuation given the underlying mass density…
The nature of galaxy spin is still not fully known. Iye et al (2021) applied a 3D analysis to a dataset of bright SDSS galaxies that was used in the past for photometric analysis. They showed that the distribution of spin directions of…
We present a study aimed at understanding the physical phenomena underlying the formation and evolution of galaxies following a data-driven analysis of spectroscopic data based on the variance in a carefully selected sample. We apply…
Rapid progress in representation learning has led to a proliferation of embedding models, and to associated challenges of model selection and practical application. It is non-trivial to assess a model's generalizability to new, candidate…
Accurate photometric redshifts are a lynchpin for many future experiments to pin down the cosmological model and for studies of galaxy evolution. In this study, a novel sparse regression framework for photometric redshift estimation is…
The stochastic nature of star formation and photon propagation in high-redshift galaxies can result in sizable galaxy-to-galaxy scatter in their properties. Ignoring this scatter by assuming mean quantities can bias estimates of their…
Several inflationary models predict the possibility that the primordial perturbations of the density field may contain a degree of non-Gaussianity which would influence the subsequent evolution of cosmic structures at large scales. In order…
Several statistical problems, such as multiple heterogeneous graph analysis, distributed PCA, integrative data analysis, and simultaneous dimension reduction of images, can involve a collection of $m$ matrices whose leading subspaces…
We present a forward modeling framework for estimating galaxy redshift distributions from photometric surveys. Our forward model is composed of: a detailed population model describing the intrinsic distribution of physical characteristics…
The greatest challenge in the interpretation of galaxy clustering data from any surveys is galaxy bias. Using a simple Fisher matrix analysis, we show that the bispectrum provides an excellent determination of linear and non-linear bias…
The multivariate tool of Principal Component Analysis (PCA) is applied to 23 fields in the FCRAO CO Survey of the Outer Galaxy. PCA enables the identification of line profile differences which are assumed to be generated from fluctuations…
We apply machine learning in the form of a nearest neighbor instance-based algorithm (NN) to generate full photometric redshift probability density functions (PDFs) for objects in the Fifth Data Release of the Sloan Digital Sky Survey (SDSS…
We propose a method to determine the cosmic mass density Omega from redshift-space distortions induced by large-scale flows in the presence of nonlinear clustering. Nonlinear structures in redshift space such as fingers of God can…
Photometric redshifts (photo-$z$'s) are crucial for the cosmology, galaxy evolution, and transient science drivers of next-generation imaging facilities like the Euclid Mission, the Rubin Observatory, and the Nancy Grace Roman Space…
The size-mass galaxy distribution is a key diagnostic for galaxy evolution. Massive compact galaxies are potential surviving relics of a high-redshift phase of star formation. Some of these could be nearly unresolved in SDSS imaging and…
Probabilistic graphical models are widely used to model complex systems under uncertainty. Traditionally, Gaussian directed graphical models are applied for analysis of large networks with continuous variables as they can provide…
Evaluation of per-sample uncertainty quantification from neural networks is essential for decision-making involving high-risk applications. A common approach is to use the predictive distribution from Bayesian or approximation models and…