相关论文: Random Forest Classification of Stars in the Galac…

We explore whether global observed properties, specifically half-light radii, mean surface brightness, and integrated stellar kinematics, suffice to unambiguously differentiate galaxies from star clusters, which presumably formed…

In this second paper in a series of papers based on the most-up-to-date catalogue of symbiotic stars (SySts), we present a new approach for identifying and distinguishing SySts from other Halpha emitters in photometric surveys using machine…

We study the stellar and wind properties of massive stars in the central cluster of the Galaxy. We use non-LTE atmosphere models including winds and line-blanketing to fit their H and K band spectra obtained with the 3D spectrograph SINFONI…

In many real-world classification or recognition tasks, it is often difficult to collect training examples that exhaust all possible classes due to, for example, incomplete knowledge during training or ever changing regimes. Therefore,…

Random Forest is a machine learning method that offers many advantages, including the ability to easily measure variable importance. Class balancing technique is a well-known solution to deal with class imbalance problem. However, it has…

Following the line of classification-based two-sample testing, tests based on the Random Forest classifier are proposed. The developed tests are easy to use, require almost no tuning, and are applicable for any distribution on…

Observations of the stellar content of the Milky Way's bulge helps to understand the stellar content and evolution of distant galaxies. In this brief overview I will first highlight some recent work directed towards measuring the history of…

The central region of the Milky Way provides a unique laboratory for a systematic, spatially-resolved population study of evolved massive stars of various types in a relatively high metallicity environment. We have conducted a…

Strong line metallicity calibrations are widely used to determine the gas phase metallicities of individual HII regions and entire galaxies. Over a decade ago, based on the Sloan Digital Sky Survey Data Release 4 (SDSS DR4), Kewley \&…

The efficient use of near-IR data in studies of external stellar populations depends on our ability to recognize the nature of the predominant sources of light, and to interprete these findings in terms of age and metallicity. Here we focus…

While the Milky Way Nuclear star cluster has been studied extensively, how it formed is uncertain. Studies have shown it contains a solar and supersolar metallicity population that may have formed in-situ, along with a subsolar metallicity…

Measuring distances of cosmological sources such as galaxies, stars and quasars plays an increasingly critical role in modern cosmology. Obtaining the optical spectrum and consequently calculating the redshift as a distance indicator could…

Globular clusters (GCs) have been at the heart of many longstanding questions in many sub-fields of astronomy and, as such, systematic identification of GCs in external galaxies has immense impacts. In this study, we take advantage of M87's…

Random forests are a widely used machine learning algorithm, but their computational efficiency is undermined when applied to large-scale datasets with numerous instances and useless features. Herein, we propose a nonparametric feature…

The relations between star formation and properties of molecular clouds are studied based on a sample of star forming regions in the Galactic Plane. Sources were selected by having radio recombination lines to provide identification of…

We have developed a method for fast and accurate stellar population parameters determination in order to apply it to high resolution galaxy spectra. The method is based on an optimization technique that combines active learning with an…

We present surface photometry for the central regions of a sample of 33 early-type (E, S0, and S0/a) galaxies observed at 1.6 microns (H band) using the Hubble Space Telescope (HST). We employ a new technique of two-dimensional fitting to…

Few-shot learning remains a challenging problem, with unsatisfactory 1-shot accuracies for most real-world data. Here, we present a different perspective for data distributions in the feature space of a deep network and show how to exploit…

This paper proposes a multi-spectral random forest classifier with suitable feature selection and masking for tree cover estimation in urban areas. The key feature of the proposed classifier is filtering out the built-up region using…

Random Forest has become one of the most popular tools for feature selection. Its ability to deal with high-dimensional data makes this algorithm especially useful for studies in neuroimaging and bioinformatics. Despite its popularity and…