Related papers: Radio U-Net: a convolutional neural network to det…
In recent years, many deep learning techniques for single-channel sound source separation have been proposed using recurrent, convolutional and transformer networks. When multiple microphones are available, spatial diversity between…
We present a novel deep learning method to separately extract the two-dimensional flux information of the foreground galaxy (deflector) and background system (source) of Galaxy-Galaxy Strong Lensing events using U-Net (GGSL-Unet for short).…
Ultra-deep radio surveys are an invaluable probe of dust-obscured star formation, but require a clear understanding of the relative contribution from radio AGN to be used to their fullest potential. We study the composition of the $\mu$Jy…
Low-frequency radio observations are revealing an increasing number of diffuse synchrotron sources from galaxy clusters, dominantly in the form of radio halos or radio relics. The existence of this diffuse synchrotron emission indicates the…
Radio galaxies exhibit a rich diversity of characteristics and emit radio emissions through a variety of radiation mechanisms, making their classification into distinct types based on morphology a complex challenge. To address this…
In order to study the status and the possible evolution of clusters of galaxies at intermediate redshifts (z ~ 0.1 - 0.3), as well as their spatial correlation and relationship with the local environment, we built a sample of candidate…
Diffuse radio emission in galaxy clusters is a tracer of ultra-relativistic particles and $\mu$G-level magnetic fields, and is thought to be triggered by cluster merger events. In the distant Universe (i.e. $z>0.6$), such sources have been…
Models for audio source separation usually operate on the magnitude spectrum, which ignores phase information and makes separation performance dependant on hyper-parameters for the spectral front-end. Therefore, we investigate end-to-end…
In this paper we present weak lensing observations of the fields around 8 z~1 luminous radio sources. These data are searched for the lensing signatures of clusters that are either physically associated with the radio objects, or are…
We explore a new Bayesian method of detecting galaxies from radio interferometric data of the faint sky. Working in the Fourier domain, we fit a single, parameterised galaxy model to simulated visibility data of star-forming galaxies. The…
Galaxy clusters appear as extended sources in XMM-Newton images, but not all extended sources are clusters. So, their proper classification requires visual inspection with optical images, which is a slow process with biases that are almost…
Segmenting ultrasound images is critical for various medical applications, but it offers significant challenges due to ultrasound images' inherent noise and unpredictability. To address these challenges, we proposed EUIS-Net, a CNN network…
Blur detection is the separation of blurred and clear regions of an image, which is an important and challenging task in computer vision. In this work, we regard blur detection as an image segmentation problem. Inspired by the success of…
Segmentation is one of the most significant steps in image processing. Segmenting an image is a technique that makes it possible to separate a digital image into various areas based on the different characteristics of pixels in the image.…
Modern high-sensitivity radio telescopes are discovering an increased number of resolved sources with intricate radio structures and fainter radio emissions. These sources often present a challenge because source detectors might identify…
The study of diffuse radio sources is essential to our knowledge of the physical conditions in clusters of galaxies and of the role that large scale magnetic fields play in the propagation of the relativistic particles in the intracluster…
Modern audio source separation techniques rely on optimizing sequence model architectures such as, 1D-CNNs, on mixture recordings to generalize well to unseen mixtures. Specifically, recent focus is on time-domain based architectures such…
Radio halos are synchrotron diffuse sources at the centre of a fraction of galaxy clusters. The study of large samples of clusters with adequate radio and X-ray data is necessary to investigate the origin of radio halos and their connection…
Deep learning (DL) based semantic segmentation methods have been providing state-of-the-art performance in the last few years. More specifically, these techniques have been successfully applied to medical image classification, segmentation,…
In this article we investigate the efficiency of deep learning algorithms in solving the task of detecting anatomical reference points on radiological images of the head in lateral projection using a fully convolutional neural network and a…