Related papers: Classifying Cool Dwarfs: Comprehensive Spectral Ty…

Feature selection of high-dimensional labeled data with limited observations is critical for making powerful predictive modeling accessible, scalable, and interpretable for domain experts. Spectroscopy data, which records the interaction…

We present optical (6500-9200A) spectroscopy of eight cool dwarfs detected in a 231 square degree ``Mini-survey'' of the Deep NEar Infrared Survey (DENIS) data. We are able to confirm that the spectral types derived from the Mini-survey…

Observational facilities allow now the detection of optical and IR spectra of young M- and L-dwarfs. This enables empirical comparisons with old M- and L- dwarfs, and detailed studies in comparison with synthetic spectra. While classical…

We describe a technique for deriving effective temperatures, surface gravities, rotation velocities, and radial velocities from high resolution near-IR spectra. The technique matches the observed near-IR spectra to spectra synthesized from…

Countless low-surface brightness objects - including spiral galaxies, dwarf galaxies, and noise patterns - have been detected in recent large surveys. Classically, astronomers visually inspect those detections to distinguish between real…

In this article we give an overview of the developments in the field of spectral classification and its continued importance in the fields of stellar and galactic evolution. The extension of MK system to cool stars as well as refined…

We report the discovery of twenty-one hitherto unknown bright southern ultracool dwarfs with spectral types in the range M7 to L5.5, together with new observations of a further three late M dwarfs previously confirmed. Three more objects…

Trigonometric parallax determinations are presented for 28 late type dwarfs and brown dwarfs, including eight M dwarfs with spectral types between M7 and M9.5, 17 L dwarfs with spectral types between L0 and L8, and three T dwarfs. Broadband…

Unlike areas such as computer vision and speech recognition where convolutional and recurrent neural networks-based approaches have proven effective to the nature of the respective areas of application, deep learning (DL) still lacks a…

We present a catalog of 9888 M, L and T dwarfs detected in the Pan-STARRS1 3$\pi$ Survey (PS1), covering three-quarters of the sky. Our catalog contains nearly all known objects of spectral types L0-T2 in the PS1 field, with objects as…

We present a technique to identify spectrophotometrically variable L7-T3 brown dwarfs with single-epoch, low-resolution, near-infrared SpeX spectra. We calculated spectral indices on known variable brown dwarfs and used them to select 11…

We present an analysis of the 0.95-14.5 micron spectral energy distributions of nine field ultracool dwarfs with spectral types ranging from L1 to T4.5. Effective temperatures, gravities, and condensate cloud sedimentation efficiencies are…

We present a low-resolution (R = 90), 5.5-38 micron spectral sequence of a sample of M, L, and T dwarfs obtained with the Infrared Spectrograph (IRS) onboard the Spitzer Space Telescope. The spectra exhibit prominent absorption bands of…

Dusty stellar point sources are a significant stage in stellar evolution and contribute to the metal enrichment of galaxies. These objects can be classified using photometric and spectroscopic observations with color-magnitude diagrams…

We used the Project 1640 near-infrared coronagraph and integral field spectrograph to observe 19 young solar type stars. Five of these stars are known binary stars and we detected the late-type secondaries and were able to measure their JH…

Most brown dwarfs show some level of photometric or spectral variability. However, finding the most variable dwarfs more suited for a thorough variability monitoring campaign remained a challenge until a few years ago with the design of…

The application of machine learning principles in the photometric search of elusive astronomical objects has been a less-explored frontier of research. Here we have used three methods: the Neural Network and two variants of k-Nearest…

We present a novel multimodal neural network (MNN) for classifying astronomical sources in multiband ground-based observations, from optical to near infrared, to separate sources in stars, galaxies and quasars. Our approach combines a…

Brown dwarf spectra are rich in information revealing of the chemical and physical processes operating in their atmospheres. We apply a recently developed atmospheric retrieval tool to an ensemble of late T-dwarf (600-800K) near infrared…

The evolution of brown dwarfs from L to T spectral types is one of the least understood aspects of the ultracool population, partly for lack of a large, well-defined, and well-characterized sample in the L/T transition. To improve the…