Related papers: Extracting the gamma-ray source-count distribution…

We present detailed analysis of the two gamma-ray sources,1FGL J1801.3-2322c and 1FGL J1800.5-2359c,that have been found toward the supernova remnant(SNR) W28 with the Large Area Telescope(LAT) on board the Fermi Gamma-ray Space…

Deconvolution of large survey images with millions of galaxies requires to develop a new generation of methods which can take into account a space variant Point Spread Function (PSF) and have to be at the same time accurate and fast. We…

Supernovae (SNe) exploding in a dense circumstellar medium (CSM) are predicted to accelerate cosmic rays in collisionless shocks and emit GeV gamma rays and TeV neutrinos on a time scale of several months. Here we summarize the results of…

We present 0.5-2 keV, 2-8 keV, 4-8 keV, and 0.5-8 keV cumulative and differential number counts (logN-logS) measurements for the recently completed ~4 Ms Chandra Deep Field-South (CDF-S) survey, the deepest X-ray survey to date. We…

Line intensity mapping is emerging as a novel method that can measure the collective intensity fluctuations of atomic/molecular line emission from distant galaxies. Several observational programs with various wavelengths are ongoing and…

In order to retrieve cosmological parameters from photometric surveys, we need to estimate the distribution of the photometric redshift in the sky with excellent accuracy. We use and apply three different machine learning methods to…

Learning to generate a task-aware base learner proves a promising direction to deal with few-shot learning (FSL) problem. Existing methods mainly focus on generating an embedding model utilized with a fixed metric (eg, cosine distance) for…

We present the first Fermi Large Area Telescope (LAT) catalog of long-term $\gamma$-ray transient sources (1FLT). This comprises sources that were detected on monthly time intervals during the first decade of Fermi-LAT operations. The…

Nearly one-third of the gamma-ray sources detected by Fermi are still unidentified, despite significant recent progress in this effort. On the other hand, all the gamma-ray extragalactic sources associated in the second Fermi-LAT catalog…

We present a new approach for the identification of ultra-high energy cosmic rays from sources using dynamic graph convolutional neural networks. These networks are designed to handle sparsely arranged objects and to exploit their short-…

Cold dark matter subhalos are expected to populate galaxies in numbers. If dark matter self-annihilates, these objects turn into prime targets for indirect searches, in particular with gamma-ray telescopes. Incidentally, the Fermi-LAT…

Supernova remnants (SNRs) have been regarded for many decades as the sources of Galactic cosmic rays (CRs) up to a few PeV. However, only with the advent of Fermi-LAT it has been possible to detect - at least in some SNRs - \gamma-rays…

Studies of Fermi-Large Area Telescope (LAT) data coincident with dwarf spheroidal satellite galaxies (dSphs) of the Milky Way (MW) have put the most stringent constraints on models of annihilating dark matter (DM) with candidate masses in…

We train deep learning models on thousands of galaxy catalogues from the state-of-the-art hydrodynamic simulations of the CAMELS project to perform regression and inference. We employ Graph Neural Networks (GNNs), architectures designed to…

Weak lensing maps contain information beyond two-point statistics on small scales. Much recent work has tried to extract this information through a range of different observables or via nonlinear transformations of the lensing field. Here…

We present a search for spatial extension in high-latitude ($|b|>5^\circ$) sources in recent Fermi point source catalogs. The result is the Fermi High-Latitude Extended Sources Catalog, which provides source extensions (or upper limits…

In the past decade, deep neural networks (DNNs) came to the fore as the leading machine learning algorithms for a variety of tasks. Their raise was founded on market needs and engineering craftsmanship, the latter based more on trial and…

Deep neural networks (DNNs) for supervised learning can be viewed as a pipeline of a feature extractor (i.e. last hidden layer) and a linear classifier (i.e. output layer) that is trained jointly with stochastic gradient descent (SGD). In…

Employing Fermi-LAT gamma ray observations, several independent groups have found excess extended gamma ray emission at the Galactic center (GC). Both, annihilating dark matter (DM) or a population of $\sim 10^3$ unresolved millisecond…

With the development of deep learning, supervised learning has frequently been adopted to classify remotely sensed images using convolutional networks (CNNs). However, due to the limited amount of labeled data available, supervised learning…