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We present a new machine learning model for estimating photometric redshifts with improved accuracy for galaxies in Pan-STARRS1 data release 1. Depending on the estimation range of redshifts, this model based on neural networks can handle…
The Two-Micron All-Sky Survey (2MASS) has mapped out the low-redshift Universe down to $K_S\sim14$ mag. As its near-infrared photometry primarily probes the featureless Rayleigh-Jeans tail of galaxy spectral energy distributions,…
We present the Extragalactic Serendipitous Swift Survey (ExSeSS), providing a new well-defined sample constructed from the observations performed using the Swift X-ray Telescope. The ExSeSS sample consists of 79,342 sources detected in the…
We present a novel interpretation of the $\gamma$-ray diffuse emission measured by Fermi-LAT and H.E.S.S. in the Galactic center (GC) region and the Galactic ridge (GR). In the first part we perform a data-driven analysis based on PASS8…
Dark matter annihilations taking place in nearby subhalos could appear as gamma-ray sources without detectable counterparts at other wavelengths. In this study, we consider the collection of unassociated gamma-ray sources reported by the…
We explore the use of Deep Learning to infer physical quantities from the observable transmitted flux in the Lyman-alpha forest. We train a Neural Network using redshift z=3 outputs from cosmological hydrodynamic simulations and mock…
Deep Neural Networks (DNNs) are powerful algorithms that have been proven capable of extracting non-Gaussian information from weak lensing (WL) data sets. Understanding which features in the data determine the output of these nested,…
Diffusion models have shown remarkable performance on many generative tasks. Despite recent success, most diffusion models are restricted in that they only allow linear transformation of the data distribution. In contrast, broader family of…
We show the results of the fluctuation analysis applied to the off-source areas from the two 1 Million second Chandra Deep Fields, including our new results on the Chandra Deep Field-South (CDF-S) in the 0.5-2 keV band in addition to those…
We study the capabilities of the Fermi-LAT instrument for identifying particle Dark Matter properties as mass, annihilation cross section and annihilation channels, with gamma-ray observations from the Galactic Center. For the potential…
The deepest extragalactic X-ray observation, the 2 Ms Chandra Deep Field North (CDF-N), resolves ~80% of the total extragalactic cosmic X-ray background (CXB) in the 1-2 keV band. Recent work has shown that 70% of the remaining CXB flux is…
Compressed sensing can increase resolution, and decrease electron dose and scan time of electron microscope point-scan systems with minimal information loss. Building on a history of successful deep learning applications in compressed…
We present results on spectral index distributions, number counts, redshift distribution and other general statistical properties of extragalactic point sources in the NEWPS5 sample L\'opez-Caniego et al. (2007). The flux calibrations at…
In light of the recent advancements in machine learning, we propose a novel approach to neutron source distribution estimation through the utilisation of probabilistic generative models. The estimation is based on a Monte Carlo particle…
This paper explores the application of machine learning methods for classifying astronomical sources using photometric data, including normal and emission line galaxies (ELGs; starforming, starburst, AGN, broad line), quasars, and stars. We…
We study the $\gamma$-ray and broadband spectral energy distribution (SED) properties of a complete sample of 104 bright, radio-selected low-synchrotron peaked (LSP) blazars. Most of the sources have already been detected in the…
Strong generative models can accurately learn channel distributions. This could save recurring costs for physical measurements of the channel. Moreover, the resulting differentiable channel model supports training neural encoders by…
Galactic cosmic ray (CRs) sources, classically proposed to be Supernova Remnants (SNRs), must meet the energetic particle content required by direct measurements of high energy CRs. Indirect gamma-ray measurements of SNRs with the Fermi…
It is assumed that the unresolved fraction of the X-ray background (XRB) consists of a truly diffuse component and a population of the weak sources below the present detection threshold. Albeit these weak sources are not observed directly,…
The Large Area Telescope (LAT) onboard the Fermi satellite is observing the gamma-ray sky in the high energy region, above 20 MeV. We have developed a method to reconstruct the energy spectra of the gamma-rays detected by the Fermi LAT…