相关论文: Semi-supervised Learning for Detecting Inverse Com…

In this paper we develop a new unsupervised machine learning technique comprised of a feature extractor, a convolutional autoencoder (CAE), and a clustering algorithm consisting of a Bayesian Gaussian mixture model (BGM). We apply this…

Ion Cyclotron Emission (ICE) is a ubiquitous magnetised plasma phenomenon previously detected on virtually all large magnetic fusion devices and whose diagnostic potential for future power plants rests upon an accurate mapping of plasma…

We calculate the spectra of inverse Compton (IC) emissions in gamma-ray burst (GRB) shocks produced when relativistic ejecta encounters the external interstellar medium, assuming a broken power-law approximation to the synchrotron seed…

The electron cyclotron emission (ECE) diagnostics suite at ITER utilizes a front-end quasi-optical (QO) system whose design is fundamentally constrained by a field-stop concept. The field-stop defines the Gaussian beam variation throughout…

We present the results of a joint XMM-Newton and NuSTAR observation (200 ks) of the galaxy cluster Abell 523 at $z=0.104$. The peculiar morphology of the cluster radio halo and its outlier position in the radio power P(1.4 GHz) - X-ray…

For community detection problem, spectral clustering is a widely used method for detecting clusters in networks. In this paper, we propose an improved spectral clustering (ISC) approach under the degree corrected stochastic block model…

Fermi has resolved several star-forming galaxies, but the vast majority of the star-forming universe is unresolved and thus contributes to the extragalactic gamma ray background (EGB). Here, we calculate the contribution from star-forming…

High-impedance faults (HIF) are difficult to detect because of their low current amplitude and highly diverse characteristics. In recent years, machine learning (ML) has been gaining popularity in HIF detection because ML techniques learn…

Anomaly detection in supercomputers is a very difficult problem due to the big scale of the systems and the high number of components. The current state of the art for automated anomaly detection employs Machine Learning methods or…

Some clusters of galaxies in addition to thermal bremsstrahlung (TB), emit diffuse radiation from the intercluster medium (ICM) at radio, EUV and hard x-ray (HXR) ranges. The radio radiation is due to synchrotron by relativistic electrons,…

In this work we simulate the $50-200$ MHz radio sky that is constrained in the field of view ($5^{\circ}$ radius) of the 21 Centimeter Array (21CMA), by carrying out Monte-Carlo simulations to model redshifted cosmological reionization…

The origin of the diverse population of galaxy clusters remains an unexplained aspect of large-scale structure formation and cluster evolution. We present a novel method of using X-ray images to identify cool core (CC), weak cool core…

We report about the detection of 10 clusters of galaxies in the ongoing Swift/BAT all-sky survey. This sample, which comprises mostly merging clusters, was serendipitously detected in the 15--55 keV band. We use the BAT sample to…

Semi-supervised medical image segmentation has attracted much attention in recent years because of the high cost of medical image annotations. In this paper, we propose a novel Inherent Consistent Learning (ICL) method, aims to learn robust…

Most classification algorithms used in high energy physics fall under the category of supervised machine learning. Such methods require a training set containing both signal and background events and are prone to classification errors…

In gamma-ray astronomy and cosmic-ray physics, the continuous approximation of inverse Compton scattering (ICS) is widely adopted to model the evolution of electron energy. However, when the initial electron energy approaches $\sim100$ TeV,…

X-ray luminous cool-core (CC) galaxy clusters contain powerful cosmic ray (CR) sources. High-energy CRs powering GHz synchrotron lose energy rapidly, but long-lived (~Gyr-old) populations of 0.1-1 GeV CRs persist, propagating to ~100 kpc…

Current large-scale astrophysical experiments produce unprecedented amounts of rich and diverse data. This creates a growing need for fast and flexible automated data inspection methods. Deep learning algorithms can capture and pick up…

We investigate the spatial and spectral properties of non-thermal emission from clusters of galaxies at gamma-ray energies between 10 keV and 10 TeV due to inverse-Compton (IC) emission, pion-decay and non-thermal bremsstrahlung (NTB) from…

Unsupervised image classification, or image clustering, aims to group unlabeled images into semantically meaningful categories. Early methods integrated representation learning and clustering within an iterative framework. However, the rise…