Related papers: Principal component analysis-based inversion of ef…
We used the Project 1640 near-infrared coronagraph and integral field spectrograph to observe 19 young solar type stars. Five of these stars are known binary stars and we detected the late-type secondaries and were able to measure their JH…
Principal component analysis is a widely-used method for the dimensionality reduction of a given data set in a high-dimensional Euclidean space. Here we define and analyze two analogues of principal component analysis in the setting of…
Generalized principal component analysis (GLM-PCA) facilitates dimension reduction of non-normally distributed data. We provide a detailed derivation of GLM-PCA with a focus on optimization. We also demonstrate how to incorporate…
We present a high-resolution spectroscopic analysis of two samples of evolved stars selected in the field and in the intermediate-age open cluster IC 4651, for which detailed measurements of chemical composition were made in the last few…
An inversion technique to recover LTE one-dimensional model photospheres for late-type stars, which was previously applied to the Sun (Allende Prieto et al. 1998), is now employed to reconstruct, semi-empirically, the photospheres of cooler…
We study the effect of temperature on secular, compositional $g$-modes in the core of inviscid neutron stars. Using a chiral $SU(2)_f$ sigma model, we construct isentropic temperature profiles for hot and dense matter and find that the…
The detection of stellar variability often relies on the measurement of selected activity indicators such as coronal emission lines and non-thermal emissions. On the flip side, the effective stellar temperature is normally seen as one of…
Functional data typically contains amplitude and phase variation. In many data situations, phase variation is treated as a nuisance effect and is removed during preprocessing, although it may contain valuable information. In this note, we…
Aims. We introduce a new deep learning tool that estimates stellar parameters (such as effective temperature, surface gravity, and extinction) of young low-mass stars by coupling the Phoenix stellar atmosphere model with a conditional…
The determination of effective temperature for chemically peculiar (CP) stars by means of photometry is a sophisticated task due to their abnormal colours. Standard calibrations for normal stars lead to erroneous results and, in most cases…
Principal Component Analysis (PCA) is a highly useful topic within an introductory Linear Algebra course, especially since it can be used to incorporate a number of applied projects. This method represents an essential application and…
We present globally inverted pressure-temperature (P-T) phase diagrams up to 5,000 GPa for four fundamental planetary materials, Fe, MgO, SiO2, and MgSiO3, derived from logistic regression and supervised learning, together with an…
Very high precision measurements of the electron Lande g-factor in GaAs are presented using spin-quantum beat spectroscopy at low excitation densities and temperatures ranging from 2.6 to 300 K. In colligation with available data for the…
We apply a generalization of the time-dependent DMRG to study finite temperature properties of several quantum spin chains, including the frustrated $J_1-J_2$ model. We discuss several practical issues with the method, including use of…
In this paper, the results of numerical experiments verifying a novel setup for laser beam profiling are presented. The experimental setup is based on infrared thermography and includes laser beam illuminating a thin metal plate. The method…
We extend the principal component analysis (PCA) to second-order stationary vector time series in the sense that we seek for a contemporaneous linear transformation for a $p$-variate time series such that the transformed series is segmented…
This paper presents a method to determine effective temperatures, angular semi-diameters and bolometric corrections for population I and II FGK type stars based on V and 2MASS IR photometry. Accurate calibration is accomplished by using a…
A fast inverse heat conduction model (IHCM) is developed for estimating unknown properties of multi-layer composites considering internal heat generation. This work builds on the validated analytical forward models presented in Part I.…
I develop a feasible weighted projected principal component (FPPC) analysis for factor models in which observable characteristics partially explain the latent factors. This novel method provides more efficient and accurate estimators than…
We present a method using principal component analysis (PCA) to process x-ray pulses with severe shape variation where traditional optimal filter methods fail. We demonstrate that PCA is able to noise-filter and extract energy information…