Related papers: Classifying white dwarfs from multi-object spectro…

Machine learning (ML) and deep learning (DL) models have been employed to significantly improve analyses of medical imagery, with these approaches used to enhance the accuracy of prediction and classification. Model predictions and…

Ultracool dwarf stars and brown dwarfs provide a unique probe of large-scale Galactic structure and evolution; however, until recently spectroscopic samples of sufficient size, depth, and fidelity have been unavailable. Here, we present the…

There is a great need for accurate and autonomous spectral classification methods in astrophysics. This thesis is about training a convolutional neural network (ConvNet) to recognize an object class (quasar, star or galaxy) from…

The study of machine learning (ML) techniques for the autonomous classification of astrophysical sources is of great interest, and we explore its applications in the context of a multifrequency data-frame. We test the use of supervised ML…

We focus on the automated classification of eclipsing binary stars using deep learning methods to handle the vast data generated by large-scale photometric sky surveys. These surveys produce extensive datasets that are impractical for…

Identification of bird species from audio records is one of the challenging tasks due to the existence of multiple species in the same recording, noise in the background, and long-term recording. Besides, choosing a proper acoustic feature…

Motivation: Tumor classification using Imaging Mass Spectrometry (IMS) data has a high potential for future applications in pathology. Due to the complexity and size of the data, automated feature extraction and classification steps are…

We present a spectroscopic analysis of white dwarfs found in the Kiso survey. Spectroscopic observations at high signal-to-noise ratio have been obtained for all DA and DB stars in the Kiso Schmidt ultraviolet excess survey (KUV stars).…

Machine learning researchers strive to develop better and better algorithms to solve computer vision problems, such as image classification. In recent years, the classification of micro-Doppler spectrograms has also benefited from these…

Multispectral pedestrian detection has received extensive attention in recent years as a promising solution to facilitate robust human target detection for around-the-clock applications (e.g. security surveillance and autonomous driving).…

The identification of new white dwarfs (WDs) polluted with heavy elements is important since they provide a valuable tool for inferring chemical properties of putative planetary systems accreting material on their surfaces. The Gaia space…

Identification of white dwarfs (WD) with main-sequence (MS) companions and characterization of their properties can put important constraints on our understanding of binary stellar evolution and guide the theoretical predictions for a wide…

Mass spectrometry is a widespread approach to work out what are the constituents of a material. Atoms and molecules are removed from the material and collected, and subsequently, a critical step is to infer their correct identities based…

Machine learning has been widely applied to clearly defined problems of astronomy and astrophysics. However, deep learning and its conceptual differences to classical machine learning have been largely overlooked in these fields. The broad…

We present the Ultracool dwarf Science with MachIne LEarning (USMILE), a program developing machine-learning tools for the discovery and characterization of ultracool dwarfs. We introduce USMILE Avocado, a spectral classification framework…

Deep representation learning is a crucial procedure in multimedia analysis and attracts increasing attention. Most of the popular techniques rely on convolutional neural network and require a large amount of labeled data in the training…

A significant fraction of white dwarfs, the degenerate remnants of low- and intermediate-mass stars, host strong magnetic fields; yet, the origin and evolution of these magnetic fields remain poorly understood. Building a large,…

We report our findings on a spectroscopic survey of seven unresolved DA+DB binary white dwarf candidates. We have discovered extreme spectroscopic variations in one of these candidates, SDSS J084716.21+484220.40. Previous analysis failed to…

In recent years, spiking neural networks (SNNs) emerge as an alternative to deep neural networks (DNNs). SNNs present a higher computational efficiency using low-power neuromorphic hardware and require less labeled data for training using…

In modern astrophysics, the machine learning has increasingly gained more popularity with its incredibly powerful ability to make predictions or calculated suggestions for large amounts of data. We describe an application of the supervised…