Related papers: Principal Component Analysis for ACS/WFC Superbias…
Principal component analysis (PCA) is a widely used dimension reduction tool in the analysis of many kind of high-dimensional data. It is used in signal processing, mechanical engineering, psychometrics, and other fields under different…
In ground-based astronomy, Adaptive Optics (AO) is a pivotal technique, engineered to correct wavefront phase distortions and thereby enhance the quality of the observed images. Integral to an AO system is the wavefront sensor (WFS), which…
We present the Advanced Camera for Surveys General Catalog (ACS-GC), a photometric and morphological database using publicly available data obtained with the Advanced Camera for Surveys (ACS) instrument on the Hubble Space Telescope. The…
We present the results from a weak gravitational lensing study of the merging cluster A520 based on the analysis of Hubble Space Telescope/Advanced Camera for Surveys (ACS) data. The excellent data quality allows us to reach a mean number…
We present new observations of two Galactic globular clusters, PAL4 and PAL14, using the Wide-Field Channel of the Advanced Camera for Surveys (ACS) on board the Hubble Space Telescope (HST), and reanalyze archival data from a third,…
We present observations of the first two supernovae discovered with the recently installed Advanced Camera for Surveys (ACS) on the Hubble Space Telescope. The supernovae were found in Wide Field Camera images of the Hubble Deep Field North…
Surround-view system (SVS) is widely used in the Advanced Driver Assistance System (ADAS). SVS uses four fisheye lenses to monitor real-time scenes around the vehicle. However, accurate intrinsic and extrinsic parameter estimation is…
The Cosmology Large Angular Scale Surveyor (CLASS) is a four-telescope array observing the largest angular scales ($2 \lesssim \ell \lesssim 200$) of the cosmic microwave background (CMB) polarization. These scales encode information about…
Principal Component Analysis (PCA) is an important tool of dimension reduction especially when the dimension (or the number of variables) is very high. Asymptotic studies where the sample size is fixed, and the dimension grows [i.e., High…
This paper introduces a robust approach to functional principal component analysis (FPCA) for relative data, particularly density functions. While recent papers have studied density data within the Bayes space framework, there has been…
Hyperspectral optical imaging provides rich spectral information for estimating continuous environmental and material parameters; however, its high dimensionality and strong feature correlation pose significant challenges for machine…
The goal of the ASACUSA (Atomic Spectroscopy And Collisions Using Slow Antiprotons) CUSP experiment at CERN's Antiproton Decelerator is to measure the ground state hyperfine splitting of antihydrogen in order to test whether CPT invariance…
The high resolution spectrograph ESPRESSO on the VLT allows measurements of fundamental constants at unprecedented precision and hence enables tests for spacetime variations predicted by some theories. In a series of recent papers, we…
Video background subtraction is one of the fundamental problems in computer vision that aims to segment all moving objects. Robust principal component analysis has been identified as a promising unsupervised paradigm for background…
Principal Component Analysis (PCA) is a method for estimating a subspace given noisy samples. It is useful in a variety of problems ranging from dimensionality reduction to anomaly detection and the visualization of high dimensional data.…
We report observations of 240 Cepheid variables obtained with the Near Infrared Camera (NICMOS) through the F160W filter on the Hubble Space Telescope (HST). The Cepheids are distributed across six recent hosts of Type Ia supernovae (SNe…
The Advanced Camera for Surveys (ACS) Virgo Cluster Survey is a large program to image 100 early-type Virgo galaxies using the F475W and F850LP bandpasses of the Wide Field Channel of the ACS instrument on the Hubble Space Telescope (HST).…
This paper introduces the Class-wise Principal Component Analysis, a supervised feature extraction method for hyperspectral data. Hyperspectral Imaging (HSI) has appeared in various fields in recent years, including Remote Sensing.…
Speckle Noise is the dominant source of error in high contrast imaging with adaptive optics system. We discuss the potential for wavefront sensing telemetry to calibrate speckle noise with sufficient precision and accuracy so that it can be…
Due to their transient nature, clouds represent anomalies relative to the underlying landscape of interest. Hence, the challenge of cloud identification can be considered a specific case in the more general problem of anomaly detection. The…