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

Symbiotic stars (SySts) are interacting binaries composed of a red giant transferring material to a hot compact star, typically a white dwarf. Although only about 300 systems are confirmed, the Galactic population is estimated at 1.2 x 10^3…

We have obtained high resolution (R = 10,000) K-band spectra of candidate young massive stars deeply embedded in high-mass star-forming regions. These objects were selected from a near-infrared survey of 44 regions of high-mass…

The inner ~200 pc region of the Milky Way contains a nuclear stellar disc and a nuclear star cluster that are embedded in the larger Galactic bar. These stellar systems overlap spatially, which makes it challenging to separate stars that…

Supervised classification of temporal sequences of astronomical images into meaningful transient astrophysical phenomena has been considered a hard problem because it requires the intervention of human experts. The classifier uses the…

Context. Convolutional neural networks (CNNs) have been established as the go-to method for fast object detection and classification on natural images. This opens the door for astrophysical parameter inference on the exponentially…

In the central few degrees of the bulge of the Milky Way there is a flattened structure of gas, dust and stars (the central molecular zone) similar to nuclear disks in other galaxies. Due to extreme foreground extinction we possess only…

Analyses of stellar spectra often begin with the determination of a number of parameters that define a model atmosphere. This work presents a prototype for an automated spectral classification system that uses a 15 nm-wide region around…

Context. Machine-Learning (ML) solves problems by learning patterns from data, with limited or no human guidance. In Astronomy, it is mainly applied to large observational datasets, e.g. for morphological galaxy classification. Aims. We…

Low Mass X-ray binaries (LMXBs) are binary systems where one of the components is either a black hole or a neutron star and the other is a less massive star. It is challenging to unambiguously determine whether a LMXB hosts a black hole or…

High dimension, low sample size (HDLSS) problems are numerous among real-world applications of machine learning. From medical images to text processing, traditional machine learning algorithms are usually unsuccessful in learning the best…

We present 3D kinematic observations of stars within the central 0.5 pc of the Milky Way nuclear star cluster using adaptive optics imaging and spectroscopy from the Keck telescopes. Recent observations have shown that the cluster has a…

We demonstrate that highly accurate joint redshift-stellar mass probability distribution functions (PDFs) can be obtained using the Random Forest (RF) machine learning (ML) algorithm, even with few photometric bands available. As an…

A new 2-parameter family of central structures in trees, called central forests, is introduced. Minieka's $m$-center problem and McMorris's and Reid's central-$k$-tree can be seen as special cases of central forests in trees. A central…

Gravitational microlensing of gamma-ray bursts (GRBs) provides a unique opportunity to probe compact dark matter and small-scale structures in the Universe. However, identifying such microlensed GRBs within large data sets is a significant…

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…

In this study, we conduct a pilot program aimed at the red supergiant population of the Magellanic Clouds. We intend to extend the current known sample to the unexplored low end of the brightness distribution of these stars, building a more…

We present a new tool for the photometric estimate of stellar masses in distant galaxies. The observed SEDs are fitted by combining single stellar populations, with different SFRs and amounts of dust extinction. This approach gives us the…

A simple, fully connected neural network with a single hidden layer is used to estimate stellar masses for star-forming galaxies. The model is trained on broad-band photometry - from far-ultraviolet to mid-infrared wavelengths - generated…

Identifying merging galaxies is an important - but difficult - step in galaxy evolution studies. We present random forest classifications of galaxy mergers from simulated JWST images based on various standard morphological parameters. We…

The K20 survey is a near infrared-selected, deep (Ks < 20) redshift survey targeting galaxies in two independent regions of the sky, the CDFS and the q0055-2659 field. The total Ks-selected sample includes 545 objects. Optical spectra for…