Related papers: Principal component analysis-based inversion of ef…
We use a modified directed path algorithm to study the finite temperature chiral singularities of two color lattice QCD with staggered fermions at strong coupling. Our lattice calculations are done at a fixed finite temperature in the…
Thermodynamical properties of nuclear matter are studied in the framework of an effective many-body field theory at finite temperature, considering the Sommerfeld approximation. We perform the calculations by using the nonlinear Boguta and…
We introduce an updated version of our deep learning tool that predicts stellar parameters from the optical spectra of young low-mass stars with intermediate spectral resolution. We adopt a conditional invertible neural network (cINN)…
The temperature dependent effective potential (TDEP) method is generalized beyond pair interactions. The second and third order force constants are determined consistently from ab initio molecular dynamics simulations at finite temperature.…
Brillouin microscopy has recently emerged as a new bio-imaging modality that provides information on the micromechanical properties of biological materials, cells and tissues. The data collected in a typical Brillouin microscopy experiment…
Principal Component Analysis (PCA) is being extensively used in Astronomy but not yet exhaustively exploited for variability search. The aim of this work is to investigate the effectiveness of using the PCA as a method to search for…
Spectral energy distributions for models of arbitrarily rotating stars are computed using two dimensional rotating stellar models, NLTE plane parallel model atmospheres, and a code to integrate the appropriately weighted intensities over…
Context. Scattering and absorption processes in stellar atmosphere affect the center-to-limb variations of the intensity (CLVI) and the linear polarization (CLVP) of stellar radiation. Aims. There are several theoretical and observational…
The Discrete Absorption Components (DACs) commonly observed in the ultraviolet lines of hot stars have previously been modelled by dynamical simulations of Corotating Interaction Regions (CIRs) in their line-driven stellar winds. Here we…
Grid-based modelling is widely used for estimating stellar parameters. However, stellar model grid is sparse because of the computational cost. This paper demonstrates an application of a machine-learning algorithm using the Gaussian…
We present a new straightforward principal component analysis (PCA) method based on the diagonalization of the weighted variance-covariance matrix through two spectral decomposition methods: power iteration and Rayleigh quotient iteration.…
We analyse medium-resolution spectra (R\sim 18000) of 19 late type dwarfs in order to determine vsini-s using synthetic rather than observational template spectra. For this purpose observational data around 2.2 $\mu$m of stars with spectral…
This paper examines several applications of principal component analysis (PCA) to physical systems. The first of these demonstrates that the principal components in a basis of appropriate system variables can be employed to identify…
Insights on stellar surface large-scale magnetic field topologies are usually drawn by applying Zeeman-Doppler-Imaging (ZDI) to the observed spectropolarimetric time series. However, ZDI requires experience for reliable results to be…
We present a systematical method for obtaining analytical long-range embedded-atom potentials based on the lattice-inversion method. The potentials converge faster (exponentially) than Sutton and Chen's power-law potentials (Philos. Mag.…
The investigation of the pulsation properties of pre-main-sequence intermediate-mass stars is a promising tool to evaluate the intrinsic properties of these stars and to constrain current evolutionary models. Many new candidates of this…
We derive the stellar parameters of a sample of Galactic early-O type stars by analysing their UV and Far-UV spectra from FUSE (905-1187A), IUE, HST-STIS and ORFEUS (1200-2000A). The data have been modeled with spherical, hydrodynamic,…
We consider diagonal matrix elements of local operators between multi-soliton states in finite volume in the sine-Gordon model, and formulate a conjecture regarding their finite size dependence which is valid up to corrections exponential…
Principal component analysis (PCA) is a widely used method for dimension reduction. In high dimensional data, the "signal" eigenvalues corresponding to weak principal components (PCs) do not necessarily separate from the bulk of the "noise"…
One develops a fast computational methodology for principal component analysis on manifolds. Instead of estimating intrinsic principal components on an object space with a Riemannian structure, one embeds the object space in a numerical…