Related papers: Identifying and Characterizing Very Low Mass Spect…

The use of Environmental Microorganisms (EMs) offers a highly efficient, low cost and harmless remedy to environmental pollution, by monitoring and decomposing of pollutants. This relies on how the EMs are correctly segmented and…

Language models (LMs) significantly improve the recognition accuracy of end-to-end (E2E) models on words rarely seen during training, when used in either the shallow fusion or the rescoring setups. In this work, we introduce LMs in the…

Knowledge of late K and M dwarf metallicities can be used to guide planet searches and constrain planet formation models. However, the determination of metallicities of late-type stars is difficult because visible wavelength spectra of…

Self-consistent model grids of brown dwarfs involve complex physics and chemistry, and are often computed using proprietary computer codes, making it challenging to identify the reasons for discrepancies between model and data as well as…

[abbreviated] We report resolved near-infrared spectroscopic monitoring of the nearby L dwarf/T dwarf binary WISE J104915.57-531906.1AB (Luhman 16AB), as part of a broader campaign to characterize the spectral energy distribution and…

GJ 229B, the first type-T brown dwarf to be discovered, has presented a tension between comparisons with evolutionary models and the larger-than-expected mass and radius values derived from spectroscopic and astrometric observations. We…

We study the problem of feature selection in general machine learning (ML) context, which is one of the most critical subjects in the field. Although, there exist many feature selection methods, however, these methods face challenges such…

Machine learning potentials (MLPs) are becoming powerful tools for performing accurate atomistic simulations and crystal structure optimizations. An approach to developing MLPs employs a systematic set of polynomial invariants including…

In deep, ground-based imaging, about 15%-30% of object detections are expected to correspond to two or more true objects - these are called ``unrecognized blends''. We use Machine Learning algorithms to detect unrecognized blends in deep…

We develop a data-driven spectral model for identifying and characterizing spatially unresolved multiple-star systems and apply it to APOGEE DR13 spectra of main-sequence stars. Binaries and triples are identified as targets whose spectra…

The Einstein Telescope (ET), a future third-generation gravitational wave detector will have detection sensitivity for gravitational wave signals down to 1 Hz. This improved low-frequency sensitivity of the ET will allow the observation of…

We present a machine learning (ML) framework for the detection of wide binary star systems using Gaia DR3 data. By training supervised ML models on established wide binary catalogues, we efficiently classify wide binaries and employ…

Machine learning (ML) entered the field of computational micromagnetics only recently. The main objective of these new approaches is the automatization of solutions of parameter-dependent problems in micromagnetism such as fast response…

We introduce Mixture-based Feature Space Learning (MixtFSL) for obtaining a rich and robust feature representation in the context of few-shot image classification. Previous works have proposed to model each base class either with a single…

The relativley large spread in the derived metallicities ([Fe/H]) of M dwarfs shows that various approaches have not yet converged to consistency. The presence of strong molecular features, and incomplete line lists for the corresponding…

We developed a novel direct algorithm to derive the mass-ratio distribution (MRD) of short-period binaries from an observed sample of single-lined spectroscopic binaries (SB1). The algorithm considers a class of parameterized MRDs and finds…

Context. White dwarf-main sequence (WDMS) binaries are used to study several different important open problems in modern astrophysics. Aims. The Sloan Digital Sky Survey (SDSS) identified the largest catalogue of WDMS binaries currently…

We use machine learning to classify examples of braids (or flat braids) as trivial or non-trivial. Our ML takes form of supervised learning using neural networks (multilayer perceptrons). When they achieve good results in classification, we…

We consider a semiparametric mixture of two univariate density functions where one of them is known while the weight and the other function are unknown. Such mixtures have a history of application to the problem of detecting differentially…

The atmospheres of low-temperature stars, brown dwarfs, and exoplanets are challenging to model due to strong molecular features and complex gas and condensate chemistry. Self-consistent atmosphere models are commonly used for spectral…