Related papers: Cloud model inversions of strong chromospheric abs…
Unsupervised learning makes manifest the underlying structure of data without curated training and specific problem definitions. However, the inference of relationships between data points is frustrated by the `curse of dimensionality' in…
Searches for small exoplanets around solar-type stars are limited by stellar physical variability. While chromospheric variability is well studied, observing, modeling. and understanding the much smaller fluctuations in photospheric…
Using Principal Component Analysis (PCA), the nodal injection and line flow patterns in a network model of a future highly renewable European electricity system are investigated. It is shown that the number of principal components needed to…
Many recent models consider the structure of individual interstellar medium (ISM) clouds as a way to explain observations of large parts of galaxies. To compare such models to observations, one must understand how to translate between…
A variety of interstellar complex organic molecules (COMs) have been detected in various physical conditions. However, in the protostellar and protoplanetary environments, their complex kinematics make line profiles blend each other and the…
Efficient processing and feature extraction of largescale point clouds are important in related computer vision and cyber-physical systems. This work investigates point cloud resampling based on hypergraph signal processing (HGSP) to better…
The asteroseismic analysis of stellar power density spectra is often computationally expensive. The models used in the analysis may use several dozen parameters to accurately describe features in the spectra caused by oscillation modes and…
Determination of cosmological parameters is a major goal in cosmology at present. The availability of improved data sets necessitates the development of novel statistical tools to interpret the inference from a cosmological model. In this…
Determining accurate velocity measurements from observations of the Sun is of vital importance to solar physicists who are studying the wave dynamics in the solar atmosphere. Weak chromospheric absorption lines, due to dynamic events in the…
We present an automated procedure that derives simultaneously the effective temperature $T_{eff}$, the surface gravity logg, the metallicity [Fe/H], and the equatorial projected rotational velocity vsini for "normal" A and Am stars. The…
This paper addresses the challenge of spectral-spatial feature extraction for hyperspectral image classification by introducing a novel tensor-based framework. The proposed approach incorporates circular convolution into a tensor structure…
Principal component analysis (PCA), the most popular dimension-reduction technique, has been used to analyze high-dimensional data in many areas. It discovers the homogeneity within the data and creates a reduced feature space to capture as…
High-resolution observations of the solar chromosphere at millimeter wavelengths are now possible with the Atacama Large Millimeter Array (ALMA), promising to tackle many open problems in solar physics. Observations from other ground and…
The convectively driven, weakly magnetized regions of the solar photosphere dominate the Sun's surface at any given time, but the temporal variations of these quiet regions of the photosphere throughout the solar cycle are still not well…
We use synchronous movies from the Dutch Open Telescope sampling the G band, Ca II and Halpha with five-wavelength profile sampling to study the response of the chromosphere to acoustic events in the underlying photosphere. We first compare…
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…
Principal Component Analysis (PCA) is the most widely used tool for linear dimensionality reduction and clustering. Still it is highly sensitive to outliers and does not scale well with respect to the number of data samples. Robust PCA…
Principal component analysis (PCA) is a popular tool for linear dimensionality reduction and feature extraction. Kernel PCA is the nonlinear form of PCA, which better exploits the complicated spatial structure of high-dimensional features.…
We present high-resolution optical spectra (at ~0.6--1.8 km s-1) of interstellar CN, CH, CH^+, \ion{Ca}{1}, \ion{K}{1}, and \ion{Ca}{2} absorption toward 29 lines of sight in three star-forming regions, \rho Oph, Cep OB2, and Cep OB3. The…
We study non-LTE inversions of the Ca II infrared triplet lines as a tool for inferring physical properties of the quiet Sun. The inversion code is successful in recovering the temperature, velocity and longitudinal magnetic flux density in…