Related papers: Stellar Spectra Classification and Feature evaluat…
The proper scientific analysis of a large amount of stellar spectra requires certain capabilities of the analysing tool (e.g. precise semi-automatic fitting for normalisation of the continuum or line-list assisted measurement of spectral…
The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will produce unprecedentedly deep and wide photometric catalogs, enabling transformative studies of faint stellar systems such as the research of ultra-faint dwarf…
A new method for solving the relativistic inverse stellar structure problem is presented. This method determines a spectral representation of the unknown high density portion of the stellar equation of state from a knowledge of the total…
Random Forest (RF) is a powerful supervised learner and has been popularly used in many applications such as bioinformatics. In this work we propose the guided random forest (GRF) for feature selection. Similar to a feature selection method…
We study the classification and composite spectra of galaxy in the fourth data release (DR4) of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). We select 40,182 spectra of galaxies from LAMOST DR4, which have…
The spectral energy distribution (SED) of observed stars in wide-field images is crucial for chromatic point spread function (PSF) modelling methods, which use unresolved stars as integrated spectral samples of the PSF across the field of…
Spectral-spatial processing has been increasingly explored in remote sensing hyperspectral image classification. While extensive studies have focused on developing methods to improve the classification accuracy, experimental setting and…
Given multiband photometric data from the SDSS DR6, we estimate galaxy redshifts. We employ a Random Forest trained on color features and spectroscopic redshifts from 80,000 randomly chosen primary galaxies yielding a mapping from color to…
Libraries of stellar spectra are fundamental tools in the study of stellar populations and in automatic determination of atmospheric parameters for large samples of observed stars. In the context of the present volume, here I give an…
CONTEXT: Many massive stars have nearby companions whose presence hamper their characterization through spectroscopy. AIMS: We want to obtain spatially resolved spectroscopy of close massive visual binaries to derive their spectral types.…
The study of stellar populations is a discipline that is highly dependent on both imaging and spectroscopy. I discuss techniques in different regimes of resolving power: broadband imaging (R~4), intermediate band imaging (R~16, 64),…
In this paper, we present a deep learning system approach to estimating luminosity, effective temperature, and surface gravity of O-type stars using the optical region of the stellar spectra. In previous work, we compare a set of machine…
Photometric redshift estimation is an indispensable tool of precision cosmology. One problem that plagues the use of this tool in the era of large-scale sky surveys is that the bright galaxies that are selected for spectroscopic observation…
Image segmentation plays a crucial role in extracting objects of interest and identifying their boundaries within an image. However, accurate segmentation becomes challenging when dealing with occlusions, obscurities, or noise in corrupted…
This project outlines the complete development of a variable star classification algorithm methodology. With the advent of Big-Data in astronomy, professional astronomers are left with the problem of how to manage large amounts of data, and…
Super-resolution (SR) advances astronomical imaging by enabling cost-effective high-resolution capture, crucial for detecting faraway celestial objects and precise structural analysis. However, existing datasets for astronomical SR (ASR)…
The determination of photospheric abundances in late-type stars from spectroscopic observations is a well-established field, built on solid theoretical foundations. Improving those foundations to refine the accuracy of the inferred…
With the availability of multiwavelength, multiscale and multiepoch astronomical catalogues, the number of features to describe astronomical objects has increases. The better features we select to classify objects, the higher the…
We investigate the use of dimensionality reduction techniques for the classification of stellar spectra selected from the SDSS. Using local linear embedding (LLE), a technique that preserves the local (and possibly non-linear) structure…
Innovation in the ground and space-based instruments has taken us into a new age of spectroscopy, in which a large amount of stellar content is becoming available. So, automatic classification of stellar spectra became subjective in recent…