Related papers: Classification of Stellar Spectra with LLE
In this article we give an overview of the developments in the field of spectral classification and its continued importance in the fields of stellar and galactic evolution. The extension of MK system to cool stars as well as refined…
With X-ray free-electron lasers (XFELs), it is possible to determine the three-dimensional structure of noncrystalline nanoscale particles using X-ray single-particle imaging (SPI) techniques at room temperature. Classifying SPI scattering…
We proposed a machine learning approach to identify and distinguish dusty stellar sources employing supervised and unsupervised methods and categorizing point sources, mainly evolved stars, using photometric and spectroscopic data collected…
We describe the development and implementation of the SEGUE (Sloan Extension for Galactic Exploration and Understanding) Stellar Parameter Pipeline (SSPP). The SSPP derives, using multiple techniques, radial velocities and the fundamental…
The availability of high-quality spectra for a large number of galaxies in the SDSS survey allows for a more sophisticated extraction of information about their stellar populations than, e.g., the luminosity weighted age. Indeed,…
The determination of chemical composition is crucial for investigating the formation and evolution of star forming galaxies and provides a powerful tool to constrain the effects of galactic winds and accretion from the cosmic web. In this…
Label embedding (LE) is an important family of multi-label classification algorithms that digest the label information jointly for better performance. Different real-world applications evaluate performance by different cost functions of…
The data archives from space and ground-based telescopes present a vast opportunity for the astronomical community. We describe a classification encoding system for stellar spectra designed for archival databases that organizes the spectral…
Deep learning-based nuclei segmentation and classification in pathology images typically rely on large-scale pixel-level manual annotations, which are costly and difficult to obtain across diverse tissues and staining conditions. To address…
The study of galaxy evolution hinges on our ability to interpret multi-wavelength galaxy observations in terms of their physical properties. To do this, we rely on spectral energy distribution (SED) models which allow us to infer physical…
We investigate the application of neural networks to the automation of MK spectral classification. The data set for this project consists of a set of over 5000 optical (3800-5200 AA) spectra obtained from objective prism plates from the…
Dimensionality reduction is a topic of recent interest. In this paper, we present the classification constrained dimensionality reduction (CCDR) algorithm to account for label information. The algorithm can account for multiple classes as…
This paper discusses the spectral collocation method for numerically solving nonlocal problems: one dimensional space fractional advection-diffusion equation; and two dimensional linear/nonlinear space fractional advection-diffusion…
Traditional lost-in-space algorithms, such as those implemented in astrometry.net, solve for spacecraft orientation by matching observed star fields to celestial catalogs using geometric asterisms alone. In this work, we propose a novel…
The ability to characterize the color content of natural imagery is an important application of image processing. The pixel by pixel coloring of images may be viewed naturally as points in color space, and the inherent structure and…
Non-linear dimensionality reduction can be performed by \textit{manifold learning} approaches, such as Stochastic Neighbour Embedding (SNE), Locally Linear Embedding (LLE) and Isometric Feature Mapping (ISOMAP). These methods aim to produce…
Spectral embedding is a procedure which can be used to obtain vector representations of the nodes of a graph. This paper proposes a generalisation of the latent position network model known as the random dot product graph, to allow…
Using data mining techniques applied on emission line characteristics of Be stars spectra we attempted to find new Be stars candidates in SDSS SEGUE survey. The mid-resolution spectra of confirmed Be stars obtained from VO-compatible…
We report high-resolution spectroscopy of 125 field stars previously observed as part of the Sloan Digital Sky Survey and its program for Galactic studies, the Sloan Extension for Galactic Understanding and Exploration (SEGUE). These…
We present a statistical study of the correlations and dimensionality of emission lines carried out on a sample of over 40,000 Sloan Digital Sky Survey (SDSS) galaxies. Using principal component analysis, we found that the equivalent widths…