Related papers: White dwarf Random Forest classification through G…
Thanks to Gaia and large-scale spectroscopic follow-up surveys (4MOST, DESI, WEAVE, SDSS-V), it is now possible to build representative and minimally biased samples of the local white dwarf population. Here we analyse several volume-limited…
In large-scale regression problems, random Fourier features (RFFs) have significantly enhanced the computational scalability and flexibility of Gaussian processes (GPs) by defining kernels through their spectral density, from which a finite…
Decision Trees (DTs) and Random Forests (RFs) are powerful discriminative learners and tools of central importance to the everyday machine learning practitioner and data scientist. Due to their discriminative nature, however, they lack…
Random Forest (Breiman, 2001) is a successful and widely used regression and classification algorithm. Part of its appeal and reason for its versatility is its (implicit) construction of a kernel-type weighting function on training data,…
As a result of competing physical mechanisms, the atmospheric composition of white dwarfs changes throughout their evolution, a process known as spectral evolution. Because of the ambiguity of their atmospheric compositions and the…
We present photometric and spectroscopic studies of the white dwarf (WD) populations in the intermediate-age open clusters NGC 6633 and NGC 7063 as part of the ongoing Lick-Arizona White Dwarf Survey (LAWDS). Using wide-field CCD imaging,…
Data analysis and machine learning have become an integrative part of the modern scientific methodology, providing automated techniques to predict further information based on observations. One of these classification and regression…
We present the preliminary results of a survey aimed at significantly increasing the range and completeness of the local census of spectroscopically confirmed white dwarfs. The current census of nearby white dwarfs is reasonably complete…
Discovery and characterisation of black holes (BHs), neutron stars (NSs), and white dwarfs (WDs) with detached luminous companions (LCs) in wide orbits are exciting because they are important test beds for dark remnant (DR) formation…
Hot DA white dwarfs have fully radiative pure hydrogen atmospheres that are the least complicated to model. Pulsationally stable, they are fully characterized by their effective temperature Teff, and surface gravity log g, which can be…
We present the first grid of mean three-dimensional (3D) spectra for pure-hydrogen (DA) white dwarfs based on 3D model atmospheres. We use CO5BOLD radiation-hydrodynamics 3D simulations instead of the mixing-length theory for the treatment…
Random forests is a state-of-the-art supervised machine learning method which behaves well in high-dimensional settings although some limitations may happen when $p$, the number of predictors, is much larger than the number of observations…
The ESO Supernova Ia Progenitor Survey (SPY) took high-resolution spectra of more than 1000 white dwarfs and pre-white dwarfs. About two thirds of the stars observed are hydrogen-dominated DA white dwarfs. Here we present a catalog and…
In this paper we present a catalogue of 11,745 brown dwarfs with spectral types ranging from L0 to T9, photometrically classified using data from the Dark Energy Survey (DES) year 3 release matched to the Vista Hemisphere Survey (VHS) DR3…
Context: Open clusters (OCs) provide homogeneous samples of white dwarfs (WDs) with known distances, extinctions, and total ages. The unprecedented astrometric precision of \textit{\textit{Gaia}} allows us to identify many novel OC--WD…
We present a catalogue of 62 polluted white dwarfs observed by the 9th Low-Resolution Data Release of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST LRS DR9v1; R$\approx$1,800) and the Early Data Release 3 (EDR3) of…
Context. The study of the oldest and most metal-poor stars in our Galaxy promotes our understanding of the Galactic chemical evolution and the beginning of Galaxy and star formation. However, they are notoriously difficult to find, with…
Machine learning (ML) algorithms become increasingly important in the analysis of astronomical data. However, since most ML algorithms are not designed to take data uncertainties into account, ML based studies are mostly restricted to data…
We develop and demonstrate a probabilistic method for classifying rare objects in surveys with the particular goal of building very pure samples. It works by modifying the output probabilities from a classifier so as to accommodate our…
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…