Related papers: Classifying white dwarfs from multi-object spectro…
The spectral energy distributions for pure-hydrogen (DA) hot white dwarfs can be accurately predicted by model atmospheres. This makes it possible to define spectrophotometric calibrators by scaling the theoretical spectral shapes with…
Large volumes of data from material characterizations call for rapid and automatic data analysis to accelerate materials discovery. Herein, we report a convolutional neural network (CNN) that was trained based on theoretic data and very…
Autonomous synthesis and characterization of inorganic materials requires the automatic and accurate analysis of X-ray diffraction spectra. For this task, we designed a probabilistic deep learning algorithm to identify complex multi-phase…
We present a comprehensive investigation of He I line profile calculations used in the spectroscopic analyses of DB white dwarfs. Our study includes an in-depth photometric and spectroscopic analysis of all DB white dwarfs in the Data…
We used 3.1 million spectroscopically labelled sources from the Sloan Digital Sky Survey (SDSS) to train an optimised random forest classifier using photometry from the SDSS and the Widefield Infrared Survey Explorer (WISE). We applied this…
In the last several years the use of neural networks as tools to automate species classification from digital data has increased. This has been due in part to the high classification accuracy of image classification through Convolutional…
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…
Passive millimeter-wave (PMMW) is a significant potential technique for human security screening. Several popular object detection networks have been used for PMMW images. However, restricted by the low resolution and high noise of PMMW…
Deep learning has emerged as the predominant solution for classifying medical images. We intend to apply these developments to the ultra-widefield (UWF) retinal imaging dataset. Since UWF images can accurately diagnose various retina…
We present a detailed model atmosphere analysis of 14001 DA white dwarfs from the Montreal White Dwarf Database with ultraviolet photometry from the GALEX mission. We use the 100 pc sample, where the extinction is negligible, to demonstrate…
Along the life of the IUE project, a large archive with spectral data has been generated, requiring automated classification methods to be analyzed in an objective form. Previous automated classification methods used with IUE spectra were…
We present the white dwarf catalog derived from the third data release of the Javalambre Photometric Local Universe Survey (J-PLUS DR3), which covers 3284 deg2 using 12 optical filters. A particular focus is given to the classification of…
Our aim is to compile a catalog of white dwarfs within 40 parsecs of the Sun, in which newly discovered objects would significantly increase the completeness of the current census. White dwarf candidates are identified from the SUPERBLINK…
Upcoming Fast Radio Burst (FRB) surveys will search $\sim$10\,$^3$ beams on sky with very high duty cycle, generating large numbers of single-pulse candidates. The abundance of false positives presents an intractable problem if candidates…
We present a method to characterize statistically the parameters of a detached binary sample - binary fraction, separation distribution, and mass ratio distribution - using noisy radial-velocity data with as few as two, randomly spaced,…
We present an improved spectroscopic and photometric analysis of hydrogen-line DA white dwarfs from the Sloan Digital Sky Survey Data Release 4 based on model atmospheres that include improved Stark broadening profiles with non-ideal gas…
White dwarf-main sequence (WDMS) binary systems are essential probes for understanding binary stellar evolution and play a pivotal role in constraining theoretical models of various transient phenomena. In this study, we construct a catalog…
Diffractive deep neural networks have been introduced earlier as an optical machine learning framework that uses task-specific diffractive surfaces designed by deep learning to all-optically perform inference, achieving promising…
Diffractive optical networks unify wave optics and deep learning to all-optically compute a given machine learning or computational imaging task as the light propagates from the input to the output plane. Here, we report the design of…
Because hyperspectral remote sensing images contain a lot of redundant information and the data structure is highly non-linear, leading to low classification accuracy of traditional machine learning methods. The latest research shows that…