Related papers: Identifying and Characterizing Very Low Mass Spect…
We develop a template-fit method to automatically identify and classify late-type K and M dwarfs in spectra from the LAMOST. A search of the commissioning data, acquired in 2009-2010, yields the identification of 2612 late-K and M dwarfs.…
Mixtures of Linear Regressions (MLR) is an important mixture model with many applications. In this model, each observation is generated from one of the several unknown linear regression components, where the identity of the generated…
Noncentrosymmetric materials play a critical role in many important applications such as laser technology, communication systems,quantum computing, cybersecurity, and etc. However, the experimental discovery of new noncentrosymmetric…
This paper outlines an infrared spectroscopic technique to measure the radial velocities of faint secondaries in known single-lined binaries. The paper presents our H-band observations with the CSHELL and Phoenix spectrographs and describes…
Accurate identification of breast masses is crucial in diagnosing breast cancer; however, it can be challenging due to their small size and being camouflaged in surrounding normal glands. Worse still, it is also expensive in clinical…
We have compiled L' and M' photometry of 63 single and binary M, L,and T dwarfs obtained at UKIRT using the MKO filter set. This compilation includes new L' data for 8 L dwarfs and 13 T dwarfs and new M' data for 7 L dwarfs, 5 T dwarfs, and…
We explore the field of wildlife re-identification by implementing an MLP from scratch using NumPy, A DCNN using Keras, and a binary classifier with LightGBM for the purpose of learning for an assignment. Analyzing the performance of…
Machine learning models can assist with metamaterials design by approximating computationally expensive simulators or solving inverse design problems. However, past work has usually relied on black box deep neural networks, whose reasoning…
The detection rate for compact binary mergers has grown as the sensitivity of the global network of ground based gravitational wave detectors has improved, now reaching the stage where robust automation of the analyses is essential.…
Polaron defects are ubiquitous in materials and play an important role in many processes involving carrier mobility, charge transfer and surface reactivity. Determining the spatial distribution of small polarons is essential to understand…
This work focuses on the problem of unraveling nonlinearly mixed latent components in an unsupervised manner. The latent components are assumed to reside in the probability simplex, and are transformed by an unknown post-nonlinear mixing…
A proof-of-concept framework for identifying molecules of unknown elemental composition and structure using experimental rotational data and probabilistic deep learning is presented. Using a minimal set of input data determined…
Data-driven machine learning (ML) has demonstrated tremendous potential in material property predictions. However, the scarcity of materials data with costly property labels in the vast chemical space presents a significant challenge for ML…
We report the discovery of a faint L6 \pm 1 companion to the previously known M9 dwarf, 2MASS J01303563-4445411, based on our near-infrared imaging and spectroscopic observations with the 3m Infrared Telescope Facility SpeX…
To improve the classification performance and generalization ability of the hyperspectral image classification algorithm, this paper uses Multi-Scale Total Variation (MSTV) to extract the spectral features, local binary pattern (LBP) to…
The transition from the L to the T spectral type of brown dwarfs is marked by a very rapid transition phase, remarkable brightening in the J-band and a higher binary frequency. Despite being an active area of inquiry, this transition regime…
Compact stellar systems such as Ultra-compact dwarfs (UCDs) and Globular Clusters (GCs) around galaxies are known to be the tracers of the merger events that have been forming these galaxies. Therefore, identifying such systems allows to…
Binary representation is desirable for its memory efficiency, computation speed and robustness. In this paper, we propose adjustable bounded rectifiers to learn binary representations for deep neural networks. While hard constraining…
In powder diffraction data analysis, phase identification is the process of determining the crystalline phases in a sample using its characteristic Bragg peaks. For multiphasic spectra, we must also determine the relative weight fraction of…
To improve the statistics of hierarchical multiplicity, secondary components of wide nearby binaries with solar-type primaries were surveyed at the SOAR telescope for evaluating the frequency of subsystems. Images of 17 faint secondaries…