Related papers: Identifying and Characterizing Very Low Mass Spect…

A vast majority of mass spectrometry data remains uncharacterized, leaving much of its biological and chemical information untapped. Recent advances in machine learning have begun to address this gap, particularly for tasks such as spectral…

Theoretical models of low-mass stars continue to be discrepant with observations when used to examine the mass-radius relationship and other physical parameters of individual stars. High-resolution spectroscopy that leads to dynamical…

Given a training dataset composed of images and corresponding category labels, deep convolutional neural networks show a strong ability in mining discriminative parts for image classification. However, deep convolutional neural networks…

We present spatially resolved photometric and spectroscopic observations of two wide brown dwarf binaries uncovered by the SIMP near-infrared proper motion survey. The first pair (SIMP J1619275+031350AB) has a separation of 0.691" (15.2 AU)…

The stellar mass-luminosity relation is poorly constrained by observations for high mass stars. We describe our program to find eclipsing massive binaries in the Magellanic Clouds using photometry of regions rich in massive stars, and our…

Deep learning can be used to drastically decrease the processing time of parameter estimation for coalescing binaries of compact objects including black holes and neutron stars detected in gravitational waves (GWs). As a first step, we…

We study close approaches in hierarchical triple systems with comparable masses using full N-body simulations, motivated by a recent model for type Ia supernovae involving direct collisions of white dwarfs (WDs). For stable hierarchical…

A revised near infrared classification scheme for T dwarfs is presented, based on and superseding prior schemes developed by Burgasser et al. and Geballe et al., and defined following the precepts of the MK Process. Drawing from two large…

We present results from a high-angular-resolution survey of 78 very low mass (VLM) binary systems with 6.0 <= V-K colour <= 7.5 and proper motion >= 0.15 arcsec/yr. 21 VLM binaries were detected, 13 of them new discoveries. The new binary…

White dwarfs (WD) with main-sequence (MS) companions are crucial probes of stellar evolution. However, due to the significant difference in their luminosities, the WD is often outshined by the MS star. The aim of this work is to find hidden…

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…

The system of light electroweakinos and heavy squarks gives rise to one of the most challenging signatures to detect at the LHC. It consists of missing transverse energy recoiled against a few hadronic jets originating either from QCD…

Using Monte Carlo simulations and published radial velocity surveys we have constrained the frequency and separation (a) distribution of very low mass star (VLM) and brown dwarf (BD) binary systems. We find that simple Gaussian extensions…

Significant populations of field L and T dwarfs are now known, and we anticipate the discovery of even cooler dwarfs by Spitzer and ground-based infrared surveys. However, as the number of known L and T dwarfs increases so does the range in…

The statistical characteristics of double main-sequence (MS) binaries are essential for investigating star formation, binary evolution, and population synthesis. Our previous study proposed a machine learning-based method to identify MS…

(abridged) We present the discovery of two T dwarf binaries, 2MASS 1225-2739AB and 2MASS 1534-2952AB, identified in a sample of ten T dwarfs imaged with the Hubble Space Telescope Wide Field Planetary Camera 2. The separations of the two…

The machine learning based approaches efficiently solve the goal of searching the best materials candidate for the targeted properties. The search for topological materials using traditional first-principles and symmetry-based methods often…

As in many other scientific domains, we face a fundamental problem when using machine learning to identify proteins from mass spectrometry data: large ground truth datasets mapping inputs to correct outputs are extremely difficult to…

We search for new superhard B-N-O compounds with an iterative machine learning (ML) procedure, where ML models are trained using sample crystal structures from evolutionary algorithm. We first use cohesive energy to evaluate the…

We present the first discoveries of the double-lined double white dwarf (DBL) survey that targets over-luminous sources with respect to the canonical white dwarf cooling sequence according to a set of well-defined criteria. The primary goal…