相关论文: Quasar Factor Analysis -- An Unsupervised and Prob…

Factor models are widely applied to the analysis of multivariate data across disparate fields of research. However, modern scientific data are often incomplete, and estimating a factor model from partially observed data can be very…

Quadruply-imaged strongly lensed quasars (quads) are routinely used for measurements of the expansion rate of the Universe with time delays. It has recently been suggested that any quad lens is subject to a Malmquist-like bias that causes…

For subspace estimation with an unknown colored noise, Factor Analysis (FA) is a good candidate for replacing the popular eigenvalue decomposition (EVD). Finding the unknowns in factor analysis can be done by solving a non-linear least…

The distribution of absorption lines in the spectra of distant quasars, called the Lyman-$\alpha$ (Ly-$\alpha$) forest, is a unique probe of cosmology and the intergalactic medium at high redshifts and small scales. The statistical power of…

We show that the long-established concept of a linewidth-enhancement factor \alpha to describe carrier-induced refractive index changes in semiconductor lasers breaks down in quantum dot (QD) lasers when describing complex dynamic…

We investigate the possibility of constraining primordial non-Gaussianity using the 3D bispectrum of Ly-alpha forest. The strength of the quadratic non-Gaussian correction to an otherwise Gaussian primordial gravitational field is assumed…

This paper introduces a Projected Principal Component Analysis (Projected-PCA), which employs principal component analysis to the projected (smoothed) data matrix onto a given linear space spanned by covariates. When it applies to…

In order to investigate the origin of quasars, we estimate the bias factor for low-luminosity quasars at high redshift for the first time. In this study, we use the two-point angular cross-correlation function (CCF) for both low-luminosity…

In Lyman-alpha forest measurements it is generally assumed that quasars are mere background light sources which are uncorrelated with the forest. Gravitational lensing of the quasars violates this assumption. This effect leads to a…

We study semiparametric factor models in high-dimensional panels where the factor loadings consist of a nonparametric component explained by observed covariates and an idiosyncratic component capturing unobserved heterogeneity. A key…

The matter density field at $z\sim 6$ is very challenging to probe. One of the traditional probes of the low density IGM that works successfully at lower redshifts is the Lyman-alpha forest in quasar spectra. However, at the end of…

Factor analysis provides a canonical framework for imposing lower-dimensional structure such as sparse covariance in high-dimensional data. High-dimensional data on the same set of variables are often collected under different conditions,…

Factor analysis (FA) and principal component analysis (PCA) are popular statistical methods for summarizing and explaining the variability in multivariate datasets. By default, FA and PCA assume the number of components or factors to be…

We measure the large-scale cross-correlation of quasars with the Lyman \alpha\ forest absorption in redshift space, using ~ 60000 quasar spectra from Data Release 9 (DR9) of the Baryon Oscillation Spectroscopic Survey (BOSS). The…

Many real-world time series exhibit strong periodic structures arising from physical laws, human routines, or seasonal cycles. However, modern deep forecasting models often fail to capture these recurring patterns due to spectral bias and a…

In this paper, we propose a novel non-standard Local Fourier Analysis (LFA) variant for accurately predicting the multigrid convergence of problems with random and jumping coefficients. This LFA method is based on a specific basis of the…

Contamination-free assessments of the interstellar medium and star formation in quasar host galaxies, particularly based on the far-infrared, offer insights into the role of supermassive black holes in galaxy evolution. Motivated by…

Quaternionic signal processing provides powerful tools for efficiently managing color signals by preserving the intrinsic correlations among signal dimensions through quaternion algebra. In this paper, we address the quaternionic phase…

Unmeasured confounding can severely bias causal effect estimates from spatiotemporal observational data, especially when the confounders do not vary smoothly in time and space. In this work, we develop a method for addressing unmeasured…

Exploring the possibility that fundamental constants of Nature might vary temporally or spatially constitutes one of the key science drivers for the European Southern Observatory's ESPRESSO spectrograph on the VLT and for the HIRES…