Related papers: Identifying and Characterizing Very Low Mass Spect…

We surveyed thirteen very low mass (VLM; M < 0.2 M_sun) objects in the Taurus star-forming region using near-infrared diffraction-limited imaging techniques on the W.M. Keck I 10 m telescope. Of these thirteen, five were found to be binary,…

We present near-infrared spectra for a sample of T dwarfs, including eleven new discoveries made using the Two Micron All Sky Survey. These objects are distinguished from warmer (L-type) brown dwarfs by the presence of methane absorption…

Identification of specific stellar populations using photometry for spectroscopic follow-up is a first step to confirm and better understand their nature. In this context, we present an unsupervised machine learning approach to identify…

We have used Monte Carlo simulation techniques and published radial velocity surveys to constrain the frequency of very low-mass star (VLMS) and brown dwarf (BD) binary systems and their separation (a) distribution. Gaussian models for the…

Machine Learning (ML) plays an increasingly important role in the discovery and design of new materials. In this paper, we demonstrate the potential of ML for materials research using hard-magnetic phases as an illustrative case. We build…

We present the completion of a program to cross-correlate the SDSS Data Release 1 and 2MASS Point Source Catalog in search for extremely red L and T dwarfs. The program was initiated by Metchev and collaborators, who presented the findings…

We report the identification of the M9 dwarf SDSS J000649.16-085246.3 as a spectral binary and radial velocity variable with components straddling the hydrogen burning mass limit. Low-resolution near-infrared spectroscopy reveals spectral…

We present high resolution Laser Guide Star Adaptive Optics imaging of 43 late-M, L and T dwarf systems with Keck/NIRC2. These include 17 spectral binary candidates, systems whose spectra suggest the presence of a T dwarf secondary. We…

The frequency of multiple systems and their properties are key constraints of stellar formation and evolution. Formation mechanisms of very low-mass (VLM) objects are still under considerable debate and an accurate assessment of their…

The multiplicity properties of brown dwarfs are critical empirical constraints for formation theories, while multiples themselves provide unique opportunities to test evolutionary and atmospheric models and examine empirical trends. Studies…

We aim to provide a detailed characterization of near-infrared spectra for young LT brown dwarfs, including robust spectral typing, calibrating spectral indices, identifying possible binaries, and selecting suitable spectral standards. We…

As part of our search for young M dwarfs within 25 pc, we acquired high-resolution spectra of 185 low-mass stars compiled by the NStars project that have strong X-ray emission. By cross-correlating these spectra with radial velocity…

The statistical properties of double main sequence (MS) binaries are very important for binary evolution and binary population synthesis. To obtain these properties, we need to identify these MS binaries. In this paper, we have developed a…

High-resolution imaging has revealed an unusually high binary fraction amongst objects spanning the transition between the L dwarf and T dwarf spectral classes. In an attempt to reproduce and unravel the origins of this apparent binary…

This study presents a multi-task machine learning framework for simultaneous morphology classification and physical parameter estimation of eclipsing binaries using photometric light curves. We train Random Forest and XGBoost ensemble…

The study of machine learning (ML) techniques for the autonomous classification of astrophysical sources is of great interest, and we explore its applications in the context of a multifrequency data-frame. We test the use of supervised ML…

We identify the L dwarf 2MASS J20261584-2943124 as an unresolved spectral binary, based on low-resolution, near-infrared spectroscopy from IRTF/SpeX. The data reveal a peculiar absorption feature at 1.6 microns, previously noted in the…

Unsupervised mixture learning (UML) aims at identifying linearly or nonlinearly mixed latent components in a blind manner. UML is known to be challenging: Even learning linear mixtures requires highly nontrivial analytical tools, e.g.,…

Constraining L dwarf properties from their spectra is challenging. Near-infrared spectra probe a limited range of pressures, while many species condense within their photospheres. Condensation creates two complexities: gas-phase species…

We consider machine learning techniques associated with the application of a Boosted Decision Tree (BDT) to searches at the Large Hadron Collider (LHC) for pair-produced lepton partners which decay to leptons and invisible particles. This…