Related papers: Principal Component Analysis for ACS/WFC Superbias…
Observations utilizing the ultraviolet capabilities of the Cosmic Origin Spectrograph (COS) onboard the Hubble Space Telescope are of unique value to the astronomy community. Spectroscopy down to 900 A with COS has enabled new science…
While time-delay lenses can be an independent probe of $H_0$ the estimates are degenerate with the convergence of the lens near the Einstein radius. Velocity dispersions, $\sigma$, can be used to break the degeneracy, with uncertainties…
We explore the use of principal component analysis (PCA) to characterize high-fidelity simulations and interferometric observations of the millimeter emission that originates near the horizons of accreting black holes. We show…
Eclipsing binaries are perfect laboratories to measure precise, accurate and model-independent stellar radii and stellar masses, so long as both components are spectroscopically resolved. Resolving both components is difficult in…
Principal component analysis (PCA) is a widely used unsupervised dimensionality reduction technique in machine learning, applied across various fields such as bioinformatics, computer vision and finance. However, when the response variables…
ESA's PROBA-V Earth observation satellite enables us to monitor our planet at a large scale, studying the interaction between vegetation and climate and provides guidance for important decisions on our common global future. However, the…
We report the weak lensing measurement of filaments between Sloan Digital Sky Survey (SDSS) III/Baryon Oscillation Spectroscopic Survey (BOSS) CMASS galaxy pairs at $z\sim0.55$, using the Subaru Hyper Suprime-Cam (HSC) first-year galaxy…
We describe the Hubble Space Telescope (HST) Wide Field Camera 3 (WFC3) Early Release Science (ERS) observations in the Great Observatories Origins Deep Survey (GOODS) South field. The new WFC3 ERS data provide calibrated, drizzled mosaics…
Sparse principal component analysis (sPCA) enhances the interpretability of principal components (PCs) by imposing sparsity constraints on loading vectors (LVs). However, when used as a precursor to independent component analysis (ICA) for…
Strong gravitational lens systems with measured time delays between the multiple images provide a method for measuring the "time-delay distance" to the lens, and thus the Hubble constant. We present a Bayesian analysis of the strong…
Principal component analysis has been widely adopted to reduce the dimension of data while preserving the information. The quantum version of PCA (qPCA) can be used to analyze an unknown low-rank density matrix by rapidly revealing the…
Adaptive optics (AO) systems have significantly improved astronomical imaging capabilities over the last decade, and are revolutionizing the kinds of science possible with 4-5m class ground-based telescopes. A thorough understanding of AO…
We present an objective method to remove the stellar continuum emission from narrow-band images to derive emission-line images. The method is based on the skewness of the pixel histogram of the residual images. Specifically, we exploit a…
Time-delay cosmography uses strong gravitational lensing of a time-variable source to infer the Hubble Constant. The measurement is independent from both traditional distance ladder and CMB measurements. An accurate measurement with this…
Current cosmological analyses which use Type Ia supernova (SN Ia) observations combine SN samples to expand the redshift range beyond that of a single sample and increase the overall sample size. The inhomogeneous photometric calibration…
Cepheids in multiple systems provide information on the outcome of the formation of massive stars. They can also lead to exotic end-stage objects. This study concludes our survey of 70 galactic Cepheids using the {\it Hubble Space…
From 2002 to 2025, the Hubble Space Telescope's Advanced Camera for Surveys has suffered in the harsh radiation environment above the protection of the Earth's atmosphere. We track the degradation of its image quality, as Solar protons and…
Functional Principal Components Analysis (FPCA) is a widely used analytic tool for dimension reduction of functional data. Traditional implementations of FPCA estimate the principal components from the data, then treat these estimates as…
Motivation: Although principal component analysis is frequently applied to reduce the dimensionality of matrix data, the method is sensitive to noise and bias and has difficulty with comparability and interpretation. These issues are…
We measure surface brightness fluctuation (SBF) magnitudes in the F814W filter and g-I colors for nine bright early-type Fornax cluster galaxies imaged with the Hubble Space Telescope Advanced Camera for Surveys (ACS). The goal is to…