Related papers: Efficient Fermi Source Identification with Machine…
We provide a new methodology for statistical recovery of single linear mixtures of piecewise constant signals (sources) with unknown mixing weights and change points in a multiscale fashion. We show exact recovery within an…
Context:Blazars are the rarest and most powerful active galactic nuclei, playing a crucial and growing role in today multi-frequency and multi-messenger astrophysics. Current blazar catalogs, however, are incomplete and particularly…
Fine-grained object recognition concerns the identification of the type of an object among a large number of closely related sub-categories. Multisource data analysis, that aims to leverage the complementary spectral, spatial, and…
In the event of a nuclear accident, or the detonation of a radiological dispersal device, quickly locating the source of the accident or blast is important for emergency response and environmental decontamination. At a specified time after…
Various galaxy merger detection methods have been applied to diverse datasets. However, it is difficult to understand how they compare. We aim to benchmark the relative performance of machine learning (ML) merger detection methods. We…
The machine learning (ML) techniques to predict unitarity (UNI) and bounded from below (BFB) constraints in multi-scalar models is employed. The effectiveness of this approach is demonstrated by applying it to the two and three Higgs…
Active galactic nuclei (AGNs) have been attracting research attention due to their special observable properties. Specifically, a majority of AGNs are detected by Fermi-LAT missions, but not by Fermi-LAT, which raises the question of…
The emission of very-high-energy photons (VHE, E>100 GeV$) in blazars is closely connected to the production of ultra-relativistic particles and the role of these gamma-ray sources as cosmic particle accelerators. This work focuses on a…
We present ELDAR, a new method that exploits the potential of medium- and narrow-band filter surveys to securely identify active galactic nuclei (AGN) and determine their redshifts. Our methodology improves on traditional approaches by…
BL Lac objects are an extreme type of active galactic nuclei (AGNs) that belong to the largest population of $\gamma$-ray sources: blazars. This class of AGNs shows a double-bumped spectral energy distribution that is commonly described in…
We use nine years of gamma-ray data provided by the Fermi Large Area Telescope (LAT) to systematically study the light curves of more than two thousand active galactic nuclei (AGN) included in recent Fermi-LAT catalogs. Ten different…
We searched for gamma-ray blazar candidates among the 382 unidentified hard X-ray sources of the 3rd Palermo BAT Catalog (3PBC) obtained from the analysis of 66 months of SWIFT-BAT survey data and listing 1586 sources. We adopted a recently…
The goal of this work is to develop accurate Machine Learning (ML) models for predicting the assembly axial neutron flux profiles in the SAFARI-1 research reactor, trained by measurement data from historical cycles. The data-driven nature…
This research proposes a machine learning-based attack detection model for power systems, specifically targeting smart grids. By utilizing data and logs collected from Phasor Measuring Devices (PMUs), the model aims to learn system…
Restricted Boltzmann Machines (RBMs) offer a versatile architecture for unsupervised machine learning that can in principle approximate any target probability distribution with arbitrary accuracy. However, the RBM model is usually not…
Active Galaxies with a jet pointing towards us, so-called blazars, play an important role in the field of high-energy astrophysics. One of the most important features in the classification scheme of blazars is the peak frequency of the…
We recently developed a procedure to recognize gamma-ray blazar candidates within the positional uncertainty regions of the unidentified/unassociated gamma-ray sources (UGSs). Such procedure was based on the discovery that Fermi blazars…
We present a multiwavelength study of the unidentified Fermi gamma-ray source 0FGL J1830.3+0617, which exhibits variability above 200 MeV on timescales of days to weeks. Within the Fermi 95% confidence error contour lies B1827+0617, a radio…
The faint radio-source population includes sources dominated both by star formation and active galactic nuclei (AGN), encoding the evolution of activity in the Universe. To investigate its nature, we probabilistically classified 4,471 radio…
High-energy observations of extreme BL Lac objects, such as 1ES0229+200 or 1ES 0347-121, recently focused interest both for blazar and jet physics and for the implication on the extragalactic background light and intergalactic magnetic…