相关论文: The EB Factory Project I. A Fast, Neural Net Based…

Accurately and rapidly classifying exoplanet candidates from transit surveys is a goal of growing importance as the data rates from space-based survey missions increases. This is especially true for NASA's TESS mission which generates…

Variable stars, or particularly eclipsing binaries, are very essential astronomical occurrence. Surveys are the backbone of astronomy, and many discoveries of variable stars are the results of surveys. All-Sky Automated Survey (ASAS) is one…

In this paper we estimate the Large Synoptic Survey Telescope (LSST) yield of eclipsing binary stars, which will survey ~20,000 square degrees of the southern sky during the period of 10 years in 6 photometric passbands to r ~ 24.5. We…

Aims. We aim to find more eclipsing multiple systems and obtain their parameters, thus increasing our understanding of multiple systems. Methods. The extraneous eclipses on the \textit{kepler} binary light curves indicating extraneous…

LISA will detect $\sim \! 10^4$ Galactic binaries, the majority being double white dwarfs. However, approximately $\sim \! 1 \textrm{--} 5 \%$ of these systems will contain neutron stars which, if they can be correctly identified, will…

Optimal decision making requires that classifiers produce uncertainty estimates consistent with their empirical accuracy. However, deep neural networks are often under- or over-confident in their predictions. Consequently, methods have been…

We discuss methods for modeling eclipsing binary stars using the "physical", "simplified" and "phenomenological" models. There are few realizations of the "physical" Wilson-Devinney (1971) code and its improvements, e.g. Binary Maker,…

Large-scale embedding-based retrieval (EBR) is the cornerstone of search-related industrial applications. Given a user query, the system of EBR aims to identify relevant information from a large corpus of documents that may be tens or…

A number of popular transfer learning methods rely on grid search to select regularization hyperparameters that control over-fitting. This grid search requirement has several key disadvantages: the search is computationally expensive,…

This study presents a bidirectional Long Short-Term Memory (LSTM) neural network for classifying transient astronomical object light curves from the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC) dataset. The…

Edge detection (ED) remains a fundamental task in computer vision, yet its performance is often hindered by the ambiguous nature of non-edge pixels near object boundaries. The widely adopted Weighted Binary Cross-Entropy (WBCE) loss treats…

The Local Group galaxies constitute a fundamental step in the definition of cosmic distance scale. Therefore, obtaining accurate distance determinations to the galaxies in the Local Group, and notably to the Andromeda Galaxy (M31), is…

Few-shot learning aims at rapidly adapting to novel categories with only a handful of samples at test time, which has been predominantly tackled with the idea of meta-learning. However, meta-learning approaches essentially learn across a…

Emulator embedded neural networks, which are a type of physics informed neural network, leverage multi-fidelity data sources for efficient design exploration of aerospace engineering systems. Multiple realizations of the neural network…

Electron beam probe (EBP) is a new principle detector, which makes use of a low-intensity and low-energy electron beam to measure the transverse profile, bunch shape, beam neutralization and beam wake field of an intense beam with small…

Modern nuclear data evaluation increasingly requires not only accurate scattering calculations, but also efficient methods for uncertainty quantification and parameter optimization, tasks that benefit from differentiable solvers amenable to…

We present a simple and interpretable representation of TESS light curves designed for large-scale exploratory analysis. Our goal is not to optimize classification performance, but to construct a computationally efficient mapping in which…

Semidetached binaries, distinguished by their mass transfer phase, play a crucial role in elucidating the physics of mass transfer within interacting binary systems. To identify these systems in eclipsing binary light curves provided by…

This work proposes deep network models and learning algorithms for unsupervised and supervised binary hashing. Our novel network design constrains one hidden layer to directly output the binary codes. This addresses a challenging issue in…

Binary Neural Networks (BNNs) significantly reduce computational complexity and memory usage in machine and deep learning by representing weights and activations with just one bit. However, most existing training algorithms for BNNs rely on…