Related papers: A stacking method to study the gamma-ray emission …

We present an analysis of a general machine learning technique called 'stacking' for the estimation of photometric redshifts. Stacking techniques can feed the photometric redshift estimate, as output by a base algorithm, back into the same…

Most of the celestial gamma rays detected by the Large Area Telescope (LAT) aboard the Fermi Gamma-ray Space Telescope originate from the interstellar medium when energetic cosmic rays interact with interstellar nucleons and photons.…

One of the largest uncertainties in the Point Source (PS) studies, at Fermi-LAT energies, is the uncertainty in the diffuse background. In general there are two approaches for PS analysis: background-dependent methods, that include modeling…

The Fermi Large Area Telescope (LAT) has detected thousands of sources since its launch in 2008, with many remaining unidentified. Some of these point sources may arise from source confusion. Specifically, there could be extended sources…

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.…

Employing the 12 NaI detectors in the Fermi GBM, the Earth Occultation Technique (EOT) can be used to measure the fluxes of x-ray and gamma-ray sources. Each time a source passes behind the Earth (or emerges from behind the Earth), a…

Stacking, a potent ensemble learning method, leverages a meta-model to harness the strengths of multiple base models, thereby enhancing prediction accuracy. Traditional stacking techniques typically utilize established learning models, such…

Stacking as a tool for studying objects that are not individually detected is becoming popular even for radio interferometric data, and will be widely used in the SKA era. Stacking is typically done using imaged data rather than directly…

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 report a statistically significant detection of cumulative $\gamma$-ray emission from a stacked sample of SPT-SZ selected galaxy clusters using 16.4 years of data from the Fermi Gamma-Ray Space Telescope's Large Area Telescope (LAT). By…

The field of gamma ray astronomy relies heavily on the statistical analysis of data. Because of the paucity of data, and the often large errors associated with detecting gamma rays, analysis and interpretation of the data require…

We report the detection of a new gamma-ray source in the Fermi-LAT sky using a source detection tool based on the Minimal Spanning Tree algorithm. The source, not reported in previous LAT catalogues but very recently observed in the X-rays…

Line-intensity mapping (LIM) is an emerging observational technique that is used to observe the universe on large scales at low resolution through spectral line emission. Stacking analyses coadd cutouts of LIM data on positions of known…

The increasing volume of gamma-ray data demands new analysis approaches that can handle large-scale datasets while providing robustness for source detection. We present a Deep Learning (DL) based pipeline for detection, localization, and…

We present a new algorithm for stacking radio interferometric data in the uv-domain. The performance of uv-stacking is compared to the stacking of fully imaged data using simulated Atacama Large Millimeter/sub-millimeter Array (ALMA) and…

Since its launch in 2008 the Fermi Large Area Telescope provides regular monitoring of a large sample of gamma-ray sources on time scales from hours to years. Together with observations at other wavelengths it is now possible to study…

We present a new method for identifying blazar candidates by examining the locus, i.e. the region occupied by the Fermi gamma-ray blazars in the three-dimensional color space defined by the WISE infrared colors. This method is a refinement…

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…

Recently, a spatially extended excess of gamma rays collected by the Fermi-LAT from the inner region of the Milky Way has been detected by different groups and with increasingly sophisticated techniques. Yet, any final conclusion about the…

Star-forming galaxies are predicted to contribute considerably to the cosmic gamma-ray background (CGB) as they are confirmed gamma-ray emitters and are the most numerous population of gamma-ray sources, although individually faint. Even…