Related papers: Machine learning application to Fermi-LAT data: sh…
Accurately reconstructing a global spatial field from sparse data has been a longstanding problem in several domains, such as Earth Sciences and Fluid Dynamics. Historically, scientists have approached this problem by employing complex…
Identifying the counterparts of submillimetre (submm) galaxies (SMGs) in multiwavelength images is a critical step towards building accurate models of the evolution of strongly star-forming galaxies in the early Universe. However, obtaining…
Synthetic aperture radar (SAR) imaging technology is commonly used to provide 24-hour all-weather earth observation. However, it still has some drawbacks in SAR target classification, especially in fine-grained classification of aircraft:…
Regression methods based in Machine Learning Algorithms (MLA) have become an important tool for data analysis in many different disciplines. In this work, we use MLA in an astrophysical context; our goal is to measure the mean longitudinal…
Multi-wavelength analyses of spectra of active galactic nuclei (AGNs) provide useful information on the physical processes in the accretion disk and jets of black holes. This, however, is limited to bright sources and may not represent the…
Purpose: We address the challenge of inaccurate parameter estimation in diffusion MRI when the signal-to-noise ratio (SNR) is very low, as in the spinal cord. The accuracy of conventional maximum-likelihood estimation (MLE) depends highly…
Machine learning (ML) is often viewed as a black-box regression technique that is unable to provide considerable scientific insight. ML models are universal function approximators and - if used correctly - can provide scientific information…
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…
Atmospheric retrieval determines the properties of an atmosphere based on its measured spectrum. The low signal-to-noise ratio of exoplanet observations require a Bayesian approach to determine posterior probability distributions of each…
Blazars are a subclass of active galactic nuclei (AGN) having relativistic jets aligned within a few degrees of our line-of-sight and form the majority of the AGN detected in the TeV regime. The Fermi-Large Area Telescope (LAT) is a…
Large fraction of studies of active galactic nuclei objects is based on performing follow-up observations using high-sensitivity instruments of high flux states observed by monitoring instruments (the so-called Target of Opportunity, ToO).…
Combining satellite imagery with machine learning (SIML) has the potential to address global challenges by remotely estimating socioeconomic and environmental conditions in data-poor regions, yet the resource requirements of SIML limit its…
Global climate models (GCMs), typically run at ~100-km resolution, capture large-scale environmental conditions but cannot resolve convection and cloud processes at kilometer scales. Convection-permitting models offer higher-resolution…
Spatial extension is an important characteristic for correctly associating gamma-ray-emitting sources with their counterparts at other wavelengths and for obtaining an unbiased model of their spectra. We present a new method for quantifying…
Machine Learning (ML) is the branch of computer science that studies computer algorithms that can learn from data. It is mainly divided into supervised learning, where the computer is presented with examples of entries, and the goal is to…
The unprecedented amount and the excellent quality of lensing data that the upcoming ground- and space-based surveys will produce represent a great opportunity to shed light on the questions that still remain unanswered concerning our…
Fiber nonlinear interference (NLI) modeling and monitoring are the key building blocks to support elastic optical networks (EONs). In the past, they were normally developed and investigated separately. Moreover, the accuracy of the…
Context: Astronomical imaging aims to maximize signal capture while minimizing noise. Enhancing the signal-to-noise ratio directly on detectors is difficult and expensive, leading to extensive research in advanced post-processing…
Machine learning (ML) has been widely applied to image classification. Here, we extend this application to data generated by a camera comprised of only a standard CMOS image sensor with no lens. We first created a database of lensless…
The Large Area Telescope on the Fermi gamma-ray Space Telescope provides unprecedented sensitivity for all-sky monitoring of gamma-ray activity. It has detected a few Galactic sources, including 2 gamma-ray binaries and a microquasar. In…