Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination
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A non-intrusive reduced-basis (RB) method is proposed for parametrized unsteady flows. A set of reduced basis functions are extracted from a collection of high-fidelity solutions via a proper orthogonal decomposition (POD), and the coefficients of the redu ...
The 21 cm emission of neutral hydrogen is the most promising probe of the epoch of reionization (EoR). In the next few years, the SKA pathfinders will provide statistical measurements of this signal, such as its power spectrum. Within one decade, SKA shoul ...
This study analyses the use of neural networks to produce accurate forecasts of total bookings and cancellations before departure, of a major European rail operator. Effective forecasting models, can improve revenue performance of transportation companies ...
Aims. Ly alpha emitters (LAEs) can be detected out to very high redshifts during the epoch of reionization. The evolution of the LAE luminosity function with redshift is a direct probe of the Ly alpha transmission of the intergalactic medium (IGM), and the ...
Context. It is predicted that sources emitting UV radiation in the Lyman band during the epoch of reionization show a series of discontinuities in their Ly alpha flux radial profile as a consequence of the thickness of the Lyman-series lines in the primeva ...
We present the results of a systematic search for Ly alpha emitters (LAEs) at 6 less than or similar to z less than or similar to 7.6 using the HST WFC3 Infrared Spectroscopic Parallel (WISP) Survey. Our total volume over this redshift range is similar to ...
Convolutional Neural Networks (CNNs) have been widely adopted for many imaging applications. For image aesthetics prediction, state-of-the-art algorithms train CNNs on a recently-published large-scale dataset, AVA. However, the distribution of the aestheti ...
We update the all-sky Planck catalogue of 1227 clusters and cluster candidates (PSZ1) published in March 2013, derived from detections of the Sunyaev-Zeldovich (SZ) effect using the first 15.5 months of Planck satellite observations. As an addendum, we del ...
Most recent approaches to monocular 3D pose estimation rely on Deep Learning. They either train a Convolutional Neural Network to directly regress from image to 3D pose, which ignores the dependencies between human joints, or model these dependencies via a ...