Related papers: Classification of Fermi-LAT sources with deep lear…
The Large Area Telescope (LAT) on board the \emph{Fermi} Gamma-ray Space Telescope has been continuously providing good quality survey data of the entire sky in the high energy range from 30 MeV to 500 GeV and above since August 2008. A…
Classification of sources is one of the most important tasks in astronomy. Sources detected in one wavelength band, for example using gamma rays, may have several possible associations in other wavebands, or there may be no plausible…
Since 2008 August the Fermi Large Area Telescope (LAT) has provided continuous coverage of the gamma-ray sky yielding more than 5000 gamma-ray sources, but 54% of the detected sources remain with no certain or unknown association with a low…
We apply a number of statistical and machine learning techniques to classify and rank gamma-ray sources from the Third Fermi Large Area Telescope (LAT) Source Catalog (3FGL), according to their likelihood of falling into the two major…
During its first 2 years of mission the Fermi-LAT instrument discovered more than 1,800 gamma-ray sources in the 100 MeV to 100 GeV range. Despite the application of advanced techniques to identify and associate the Fermi-LAT sources with…
Machine learning has emerged as a powerful tool in the field of gamma-ray astrophysics. The algorithms can distinguish between different source types, such as blazars and pulsars, and help uncover new insights into the high-energy universe.…
We have investigated a number of factors that can have significant impacts on the classification performance of $\gamma$-ray sources detected by Fermi Large Area Telescope (LAT) with machine learning techniques. We show that a framework of…
The classifications of Fermi-LAT unassociated sources are studied using multiple machine learning (ML) methods. The update data from 4FGL-DR3 are divided into high Galactic latitude (HGL, Galactic latitude $|b|>10^\circ$) and low Galactic…
The Fermi-LAT unassociated sources represent some of the most enigmatic gamma-ray sources in the sky. Observations with the Swift-XRT and -UVOT telescopes have identified hundreds of likely X-ray and UV/optical counterparts in the…
We reconstruct the extra-galactic gamma-ray source-count distribution, or $dN/dS$, of resolved and unresolved sources by adopting machine learning techniques. Specifically, we train a convolutional neural network on synthetic 2-dimensional…
A large fraction of Fermi-Large Area Telescope (LAT) sources in the fourth Fermi-LAT 14 yr catalog (4FGL) still remain unidentified (unIDed). We continued to improve our machine-learning pipeline and used it to classify 1206 X-ray sources…
The latest $\textit{Fermi}$-LAT gamma-ray catalog, 4FGL-DR3, presents a large fraction of sources without clear association to known counterparts, i.e., unidentified sources (unIDs). In this paper, we aim to classify them using machine…
Machine learning is an automatic technique that is revolutionizing scientific research, with innovative applications and wide use in astrophysics. The aim of this study was to developed an optimized version of an Artificial Neural Network…
In the latest data release from the Fermi $\gamma$-ray Space Telescope (the 4th Fermi LAT 14 yr Catalog, or 4FGL), more than 50% of the Galactic sources are yet to be identified. We observed 13 unidentified Fermi LAT sources with the…
The Fermi Large Area Telescope First Source Catalog (1FGL) provided spatial, spectral, and temporal properties for a large number of gamma-ray sources using a uniform analysis method. After correlating with the most-complete catalogs of…
The second Fermi-LAT source catalog (2FGL) is the deepest all-sky survey available in the gamma-ray band. It contains 1873 sources, of which 576 remain unassociated. Machine-learning algorithms can be trained on the gamma-ray properties of…
The Large Area Telescope (LAT) aboard the Fermi satellite allows us to study the high-energy gamma-ray sky with unprecedented sensitivity. However, the origin of 31% of the detected gamma-ray sources remains unknown. This population of…
The second Fermi-LAT source catalog (2FGL) is the deepest survey of the gamma-ray sky ever compiled, containing 1873 sources that constitute a very complete sample down to an energy flux of about 10^(-11) erg cm^(-2) s^(-1). While…
Unassociated Fermi-LAT sources provide a population with discovery potential. We discuss efforts to find new source associations for this population, and summarize the successes to date. We discuss how the measured gamma-ray properties of…
At GeV energies, the sky is dominated by the interstellar emission from the Galaxy. With limited statistics and spatial resolution, accurately separating point sources is therefore challenging. Here we present the first application of deep…