Related papers: Machine Learning applied to Multifrequency Data in…

Stellar spectroscopic classification has been successfully automated by a number of groups. Automated classification and parameterization work best when applied to a homogeneous data set, and thus these techniques primarily have been…

A maximum likelihood (ML) technique for detecting compact sources in images of the x-ray sky is examined. Such images, in the relatively low exposure regime accessible to present x-ray observatories, exhibit Poissonian noise at background…

Hyper-spectral satellite imagery is now widely being used for accurate disaster prediction and terrain feature classification. However, in such classification tasks, most of the present approaches use only the spectral information contained…

Recent developments in applied mathematics increasingly employ machine learning (ML)-particularly supervised learning-to accelerate numerical computations, such as solving nonlinear partial differential equations. In this work, we extend…

This article provides an overview of Supervised Machine Learning (SML) with a focus on applications to banking. The SML techniques covered include Bagging (Random Forest or RF), Boosting (Gradient Boosting Machine or GBM) and Neural…

In this paper, the fourth version the Sloan Digital Sky Survey (SDSS-4), Data Release 16 dataset was used to classify the SDSS dataset into galaxies, stars, and quasars using machine learning and deep learning architectures. We efficiently…

We investigate the extent to which supervised machine learning techniques can distinguish between neutron-star matter models using macroscopic and oscillation-related quantities derived from theoretical stellar configurations. Four…

We present the results of a photon cluster search in the 7-years Fermi-Large Area Telescope extragalactic Pass 8 gamma-ray sky by means of the Minimum Spanning Tree (MST) algorithm, at energies higher than 10 GeV. We found 16 clusters of…

We propose a novel concept of operations using optimal planning methods and machine learning (ML) to collect spaceborne data that is unprecedented for monitoring wildfires, process it to create new or enhanced products in the context of…

Cosmic shear is a primary cosmological probe for several present and upcoming surveys investigating dark matter and dark energy, such as Euclid or WFIRST. The probe requires an extremely accurate measurement of the shapes of millions of…

The Fermi Large Area Telescope (LAT) has detected more than 5000 gamma-ray sources in its first 8 years of operation. More than 3000 of them are blazars. About 60 per cent of the Fermi-LAT blazars are classified as BL Lacertae objects (BL…

In this work, we make use of a supervised machine learning algorithm based on Logistic Regression (LR) to select TeV blazar candidates from the 4FGL-DR2 / 4LAC-DR2, 3FHL, 3HSP, and 2BIGB catalogs. LR constructs a hyperplane based on a…

Hyperparameters in machine learning (ML) have received a fair amount of attention, and hyperparameter tuning has come to be regarded as an important step in the ML pipeline. But just how useful is said tuning? While smaller-scale…

We study the $\gamma$-ray and broadband spectral energy distribution (SED) properties of a complete sample of 104 bright, radio-selected low-synchrotron peaked (LSP) blazars. Most of the sources have already been detected in the…

Low-frequency radio arrays are opening a new window for the study of the sky, both to study new phenomena and to better characterize known source classes. Being flat-spectrum sources, blazars are so far poorly studied at low radio…

Automatic species classification of birds from their sound is a computational tool of increasing importance in ecology, conservation monitoring and vocal communication studies. To make classification useful in practice, it is crucial to…

Globular clusters (GCs) have been at the heart of many longstanding questions in many sub-fields of astronomy and, as such, systematic identification of GCs in external galaxies has immense impacts. In this study, we take advantage of M87's…

The use of single-photon sources (SPSs) is central to numerous systems and devices proposed amidst a modern surge in quantum technology. However, manufacturing schemes remain imperfect, and single-photon emission purity must often be…

The design of high-entropy alloys (HEA) with desired properties is challenging due to their large compositional space. While various machine learning (ML) models can predict specific HEA solid-solution phases (SS), predicting high-entropy…

Membership analysis is an important tool for studying star clusters. There are various approaches to membership determination, including supervised and unsupervised machine learning (ML) methods. We perform membership analysis using the…