相关论文: Quasar Factor Analysis -- An Unsupervised and Prob…
Predictable Feature Analysis (PFA) (Richthofer, Wiskott, ICMLA 2015) is an algorithm that performs dimensionality reduction on high dimensional input signal. It extracts those subsignals that are most predictable according to a certain…
Latent variable models can be used to probabilistically "fill-in" missing data entries. The variational autoencoder architecture (Kingma and Welling, 2014; Rezende et al., 2014) includes a "recognition" or "encoder" network that infers the…
An experimental search for variation in the fundamental coupling constants is strongly motivated by modern high-energy physics theories. Comparison of quasar absorption line spectra with laboratory spectra provides a sensitive probe for…
The Fast Folding Algorithm (FFA) is a phase-coherent search technique for periodic signals. It has rarely been used in radio pulsar searches, having been historically supplanted by the less computationally expensive Fast Fourier Transform…
Quasar samples remain severely incomplete at low Galactic latitudes because of strong extinction and source confusion. We conduct a systematic search for quasars behind the Galactic plane using X-ray sources from the Chandra Source Catalog…
We present a theoretical framework for linking quasar properties, such as quasar age, to the surrounding Ly$\alpha$ emission intensity. In particular, we focus on a method for mapping the large-scale structure of Ly$\alpha$ emission…
We exploit a set of high signal-to-noise (~70), low-resolution (R~800) quasar spectra to search for the signature of the so-called proximity effect in the HI Ly alpha forest. Our sample consists of 17 bright quasars in the redshift range…
We present a novel factor analysis method that can be applied to the discovery of common factors shared among trajectories in multivariate time series data. These factors satisfy a precedence-ordering property: certain factors are recruited…
We introduce an intrinsic Ly\alpha\ emission line profile reconstruction method for high-$z$ quasars (QSOs). This approach utilises a covariance matrix of emission line properties obtained from a large, moderate-$z$ ($2 \leq z \leq 2.5$),…
The average flux decrement shortward the Ly$_{\alpha}$ emission, due to the well-known ``forest'' of absorptions, has been measured in the spectra of 8 quasars. Quasi-simultaneous optical and IUE observations of the two low redshift quasars…
Quantum principal component analysis (qPCA) is commonly formulated as the extraction of eigenvalues and eigenvectors of a covariance-encoded density operator. Yet in many qPCA settings the practical goal is simpler: projection onto the…
Latent or unobserved phenomena pose a significant difficulty in data analysis as they induce complicated and confounding dependencies among a collection of observed variables. Factor analysis is a prominent multivariate statistical modeling…
This article establishes a new and comprehensive estimation and inference theory for principal component analysis (PCA) under the weak factor model that allow for cross-sectional dependent idiosyncratic components under the nearly minimal…
Recent studies have derived quasar luminosity functions (QLFs) at various redshifts. However, the faint side of the QLF at high redshifts is still too uncertain. An accurate estimate of the survey completeness is essential to derive an…
Quasars are variable and their variability can both constrain their physical properties and help to identify them. We look for ways to efficiently identify quasars exhibiting consistent variability over multi-year time-scales, based on a…
We consider implications of our new model of quasar lifetimes and light curves for the quasar luminosity function (LF) at different frequencies and redshifts. In our picture, quasars evolve rapidly and the lifetime depends on both their…
The apparent angular positions of quasars are deflected on the sky by the gravitational field sourced by foreground matter. This weak lensing effect is measurable through the distortions it introduces in the lensed quasar spectra.…
Factor-based forecasting using Principal Component Analysis (PCA) is an effective machine learning tool for dimension reduction with many applications in statistics, economics, and finance. This paper introduces a Supervised Screening and…
Meta-forecasting is a newly emerging field which combines meta-learning and time series forecasting. The goal of meta-forecasting is to train over a collection of source time series and generalize to new time series one-at-a-time. Previous…
Confirmatory Factor Analysis (CFA) is a particular form of factor analysis, most commonly used in social research. In confirmatory factor analysis, the researcher first develops a hypothesis about what factors they believe are underlying…