相关论文: Modeling blazar broadband emission with convolutio…

Accurate prediction of epileptic seizures allows patients to take preventive measures in advance to avoid possible injuries. In this work, a novel convolutional neural network (CNN) is proposed to analyze time, frequency, and channel…

Blazars are known to emit a broad band emission from radio to gamma-rays with rapid time variations, particularly, in X- and gamma-rays. Synchrotron radiation and inverse Compton scattering are thought to play an important role in emission…

Forecasting power consumptions of integrated electrical, heat or gas network systems is essential in order to operate more efficiently the whole energy network. Multi-energy systems are increasingly seen as a key component of future energy…

PKS 1424+240 is a distant very high energy gamma-ray BL Lac object with redshift $z=0.601$. It was found that pure synchrotron self-Compton (SSC) process normally need extreme input parameters (e.g., very low magnetic field intensity and…

Radiation of supersoft X-ray sources (SSS) dominates both the supersof X-ray and the far-UV domain. A fraction of their radiation can be reprocessed into the thermal nebular emission, seen in the spectrum from the near-UV to longer…

The last years have seen a revolution in ground-based gamma-ray detectors. We can now detect the spectra of nearby TeV blazars like Mrk 421 and 501 out to approximately 20 TeV, and during the strongest flares, we can now follow fluctuations…

Blazars, the jet dominated class of AGN comprising flat spectrum radio quasars (FSRQs) and BL Lac objects (BL Lacs) are now increasingly identified as potential sources of high energy neutrinos. Such neutrino blazars are ideal targets to…

We present a description of our numerical code BLAZAR. This code calculates spectra and light curves of blazars during outbursts. The code is based on a model in which the non-thermal flares in blazars are produced in thin shells…

We present a theoretical analysis of the expected X-ray and gamma-ray polarization signatures resulting from synchrotron self-Compton emission in leptonic models, compared to the polarization signatures from proton synchrotron and cascade…

We model the morphology and spectrum of a pulsar wind nebula using a leptonic emission code. This code is a time-dependent, multi-zone model that investigates the changes in the particle spectrum as they traverse the nebula. We calculate…

We introduce a methodology for analysis of multiwavelength data from X-ray selected BL Lac (XBL) objects detected in the TeV regime. By assuming that the radio--through--X-ray flux from XBLs is nonthermal synchrotron radiation emitted by…

Machine learning models have been employed to perform either physics-free data-driven or hybrid dynamical downscaling of climate data. Most of these implementations operate over relatively small downscaling factors because of the challenge…

Data scarcity in medical imaging poses significant challenges due to privacy concerns. Diffusion models, a recent generative modeling technique, offer a potential solution by generating synthetic and realistic data. However, questions…

We test one-zone synchrotron self-Compton (SSC) models with high-quality multiwavelength spectral energy distribution (SED) data of Mrk 421. We use Markov chain Monte Carlo (MCMC) technique to fit twelve day-scale SEDs of Mrk 421 with…

In this letter, we propose a pseudo-siamese convolutional neural network (CNN) architecture that enables to solve the task of identifying corresponding patches in very-high-resolution (VHR) optical and synthetic aperture radar (SAR) remote…

We examine the applicability of the stochastic electron acceleration to two high synchrotron peaked blazars, Mrk 421 and Mrk 501, assuming synchrotron self-Compton emission of gamma-rays. Our model considers an emitting region moving at…

We present simultaneous optical and X-ray data from Swift for a sample of FSRQs selected from the EMSS survey. We present also a complete analysis of Swift and INTEGRAL data on 4 blazars recently discussed as possibly challenging the trends…

State-of-the-art sound event detection (SED) methods usually employ a series of convolutional neural networks (CNNs) to extract useful features from the input audio signal, and then recurrent neural networks (RNNs) to model longer temporal…

Recently, outstanding identification rates in image classification tasks were achieved by convolutional neural networks (CNNs). to use such skills, selective CNNs trained on a dataset of well-known images of metal surface defects captured…

Convolutional Neural Networks (CNN) outperform traditional classification methods in many domains. Recently these methods have gained attention in neuroscience and particularly in brain-computer interface (BCI) community. Here, we introduce…