相关论文: White dwarf Random Forest classification through G…
The spectral energy distributions for pure-hydrogen (DA) hot white dwarfs can be accurately predicted by model atmospheres. This makes it possible to define spectrophotometric calibrators by scaling the theoretical spectral shapes with…
The wealth of data being gathered about humans and their surroundings drives new machine learning applications in various fields. Consequently, more and more often, classifiers are trained using not only numerical data but also complex data…
Random Forest (RF) is a widely used ensemble learning technique known for its robust classification performance across diverse domains. However, it often relies on hundreds of trees and all input features, leading to high inference cost and…
With the imminent data releases from next-generation spectroscopic surveys, hundreds of thousands of white dwarf spectra are expected to become available within the next few years, increasing the data volume by an order of magnitude. This…
We present an homogeneous analysis of all DA stars labeled as magnetic in the Montreal White Dwarf Database (MWDD). Our sample is restricted to almost all known magnetic white dwarf showing clear sign of splitting ($B \gtrsim$ 1-2 MG) that…
We increase the spectroscopic completeness of the 100 pc white dwarf sample in the SDSS footprint with 840 additional spectra. Our spectroscopy is 86% complete for white dwarfs hotter than $T_{\rm eff}= 5000$ K, where H$\alpha$ remains…
We present a spectroscopic analysis of white dwarfs found in the Kiso survey. Spectroscopic observations at high signal-to-noise ratio have been obtained for all DA and DB stars in the Kiso Schmidt ultraviolet excess survey (KUV stars).…
Context. The observed scarcity of brown dwarfs in close orbits (within 10 au) around solar-type stars poses significant questions about the origins of these substellar companions. These questions impact our broader understanding of…
Spectrophotometric observations at high signal-to-noise ratio were obtained of a complete sample of 347 DA white dwarfs from the Palomar Green (PG) Survey. Fits of observed Balmer lines to synthetic spectra calculated from pure-hydrogen…
We aimed to assess the accuracy of the Gaia teff and logg estimates as derived with current models and observations. We assessed the validity of several inference techniques for deriving the physical parameters of ultra-cool dwarf stars. We…
M dwarfs are the dominating type of stars in the solar neighbourhood. They serve as excellent tracers for the study of the distribution and properties of the nearby interstellar dust. In this work, we aim to obtain high accuracy reddening…
Identification of white dwarfs (WD) with main-sequence (MS) companions and characterization of their properties can put important constraints on our understanding of binary stellar evolution and guide the theoretical predictions for a wide…
With the state-of-the-art Gaia astrometry, the number of confirmed white dwarfs has reached a few hundred thousand. We have reached the era where small features in the white dwarf luminosity function (WDLF) of the solar neighbourhood can be…
We used the revised New Luyten Two-Tenths (rNLTT) catalog to select high proper motion white dwarf candidates. We studied the spectra of 70 hydrogen-rich (DA) white dwarfs, which were obtained at the Cerro Tololo Inter-American Observatory…
We present the full spectroscopic white dwarf and hot subdwarf sample from the SDSS first data release, DR1. We find 2551 white dwarf stars of various types, 240 hot subdwarf stars, and an additional 144 objects we have identified as…
It is possible to reliably identify white dwarfs (WDs) without recourse to spectra, instead using photometric and astrometric measurements to distinguish them from Main Sequence stars and quasars. WDs' colours can also be used to infer…
Machine learning has become a popular tool to help us make better decisions and predictions, based on experiences, observations and analysing patterns within a given data set without explicitly functions. In this paper, we describe an…
Statistical pattern recognition methods have provided competitive solutions for variable star classification at a relatively low computational cost. In order to perform supervised classification, a set of features is proposed and used to…
In this paper, we explore the feasibility of using machine learning regression as a method of extracting basic stellar parameters and line-of-sight extinctions from spectro-photometric data. We built a stable gradient-boosted random-forest…
We have exploited the very precise parallaxes, proper motions and photometry of \textit{Gaia}\, Data Release~2 to study white dwarf members of the Hyades star cluster. Gaia photometry and parallaxes for the eight DA white dwarfs confirmed…