Related papers: Extracting the gamma-ray source-count distribution…
All-sky explorations by Fermi-LAT have revolutionized our view of the gamma-ray sky. While its ongoing all-sky survey counts thousands of sources, essential issues related to the nature of unassociated sources call for sensitive all-sky…
In this paper we introduce a reliable, fully automated and fast algorithm to detect extended extragalactic radio sources (cluster of galaxies, filaments) in existing and forthcoming surveys (like LOFAR and SKA). The proposed solution is…
The origin of the inner Galactic emission, measured by COMPTEL with a flux of $\sim 10^{-2} ~{\rm MeV~ cm}^{-2}~ {\rm s}^{-1}~ {\rm sr}^{-1}$ in the 1-30 MeV energy range from the inner Galactic region, has remained unsettled since its…
Previous observations with HESS have revealed the existence of an extended very-high-energy (VHE; E>100 GeV) gamma-ray source, HESS J1834-087, coincident with the SNR W41. The origin of the gamma-ray emission has been further investigated…
In recent studies, several asymptotic upper bounds on generalization errors on deep neural networks (DNNs) are theoretically derived. These bounds are functions of several norms of weights of the DNNs, such as the Frobenius and spectral…
Removing the shape noise from the observed weak lensing field, i.e., denoising, enhances the potential of WL by accessing information at small scales where the shape noise dominates without denoising. We utilise two machine learning (ML)…
We present a stacking method that makes use of co-added maps of gamma-ray counts produced from data taken with the Fermi Large Area Telescope. Sources with low integrated gamma-ray fluxes that are not detected individually may become…
The ESA's X-ray Multi-Mirror Mission (XMM-Newton) created a new, high quality version of the XMM-Newton serendipitous source catalogue, 4XMM-DR9, which provides a wealth of information for observed sources. The 4XMM-DR9 catalogue is…
Deep generative models (DGM) are neural networks with many hidden layers trained to approximate complicated, high-dimensional probability distributions using a large number of samples. When trained successfully, we can use the DGMs to…
The diffuse Galactic gamma-ray emission is produced by cosmic rays (CRs) interacting with the interstellar gas and radiation field. Measurements by the Energetic Gamma-Ray Experiment Telescope (EGRET) instrument on the Compton Gamma-Ray…
The Fermi-LAT \gamma-ray data have been used to study the morphological and spectral features of the so-called GeV excess - a diffuse radiation component recently discovered towards the Galactic centre. We used the likelihood method to…
Distributed learning is the problem of inferring a function in the case where training data is distributed among multiple geographically separated sources. Particularly, the focus is on designing learning strategies with low computational…
Federated semi-supervised learning (FSSL) is primarily challenged by two factors: the scarcity of labeled data across clients and the non-independent and identically distribution (non-IID) nature of data among clients. In this paper, we…
We developed a Deep Convolutional Neural Network (CNN), used as a classifier, to estimate photometric redshifts and associated probability distribution functions (PDF) for galaxies in the Main Galaxy Sample of the Sloan Digital Sky Survey…
We report on the first Fermi Large Area Telescope (LAT) measurements of the so-called "extra-galactic" diffuse gamma-ray emission (EGB). This component of the diffuse gamma-ray emission is generally considered to have an isotropic or nearly…
The mass to luminosity ratio of the dwarf satellite galaxies in the Milky Way suggests that these dwarf galaxies may contain substantial dark matter. The dark matter at the dense region such as within or at the vicinity of the centres of…
Deep generative models have emerged as promising tools for detecting arbitrary anomalies in data, dispensing with the necessity for manual labelling. Recently, autoregressive transformers have achieved state-of-the-art performance for…
We present a machine learning method for the reconstruction of the undistorted images of background sources in strongly lensed systems. This method treats the source as a pixelated image and utilizes the Recurrent Inference Machine (RIM) to…
Using 3.7 years of \Fermi-LAT data, we examine the diffuse 80-200 GeV emission in the inner Galaxy and find a resolved gamma-ray feature at $\sim 110-140$ GeV. We model the spatial distribution of this emission with a $\sim3\degree$ FWHM…
We present a catalog of high-energy gamma-ray sources detected by the Large Area Telescope (LAT), the primary science instrument on the Fermi Gamma-ray Space Telescope (Fermi), during the first 11 months of the science phase of the mission,…