相关论文: Understanding the Impact of Semi-numeric Reionizat…

Radar sensors are crucial for environment perception of driver assistance systems as well as autonomous vehicles. With a rising number of radar sensors and the so far unregulated automotive radar frequency band, mutual interference is…

During the epoch of reionization (EoR), the 21-cm signal allows direct observation of the neutral hydrogen (HI) in the intergalactic medium (IGM). In the post-reionization era, this signal instead probes HI in galaxies, which traces the…

The Universe's Epoch of Reionization (EoR) can be studied using a number of observational probes that provide complementary or corroborating information. Each of these probes suffers from its own systematic and statistical uncertainties. It…

Deep Convolutional Neural Networks (CNNs) have recently reached state-of-the-art Handwritten Text Recognition (HTR) performance. However, recent research has shown that typical CNNs' learning performance is limited since they are…

We investigate the potential of second generation measurements of redshifted 21 cm radiation from before and during the epoch of reionization (EOR) to reconstruct the matter density fluctuations along the line of sight. To do so we…

Like in many other research fields, recent developments in computational imaging have focused on developing machine learning (ML) approaches to tackle its main challenges. To improve the performance of computational imaging algorithms,…

We perform a Fisher analysis to estimate the expected constraints on the Epoch of Reionization (EoR) model parameters (i.e., minimum virial temperature, the ionizing efficiency and the mean free path of ionizing photons) taking into account…

We present a sophisticated statistical point-source foreground model for low-frequency radio Epoch of Reionization (EoR) experiments using the 21 cm neutral hydrogen emission line. Motivated by our understanding of the low-frequency radio…

The cosmic 21 cm signal is set to revolutionise our understanding of the early Universe, allowing us to probe the 3D temperature and ionisation structure of the intergalactic medium (IGM). It will open a window onto the unseen first…

The use of Convolutional Neural Networks (CNN) to estimate the galaxy photometric redshift probability distribution by analysing the images in different wavelength bands has been developed in the recent years thanks to the rapid development…

Convolutional Neural Networks (CNNs) have shown to be powerful medical image segmentation models. In this study, we address some of the main unresolved issues regarding these models. Specifically, training of these models on small medical…

One weakness of machine-learning algorithms is the need to train the models for a new task. This presents a specific challenge for biometric recognition due to the dynamic nature of databases and, in some instances, the reliance on subject…

Denoising is a fundamental challenge in scientific imaging. Deep convolutional neural networks (CNNs) provide the current state of the art in denoising natural images, where they produce impressive results. However, their potential has…

Spectroscopic studies of high-redshift objects and increasingly precise data on the cosmic microwave background (CMB) are beginning to independently place strong complementary bounds on the epoch of hydrogen reionization. Parameter…

The morphology of the 21-cm signal emitted by the neutral hydrogen present in the intergalactic medium (IGM) during the Epoch of Reionization (EoR) depends both on the properties of the sources of ionizing radiation and on the underlying…

Neural saturation in Deep Neural Networks (DNNs) has been studied extensively, but remains relatively unexplored in Convolutional Neural Networks (CNNs). Understanding and alleviating the effects of convolutional kernel saturation is…

Semi-numeric methods have made it possible to efficiently model the epoch of reionisation (EoR). While most implementations involve a reduction to a simple three-parameter model, we introduce a new mass-dependent ionising efficiency…

Convolutional Neural Networks (CNNs) have proven to be a powerful state-of-the-art method for image classification tasks. One drawback however is the high computational complexity and high memory consumption of CNNs which makes them…

In the last ten years, Convolutional Neural Networks (CNNs) have formed the basis of deep-learning architectures for most computer vision tasks. However, they are not necessarily optimal. For example, mathematical morphology is known to be…

The Epoch of Reionization (EoR) 21-cm signal is expected to become increasingly non-Gaussian as reionization proceeds. We have used semi-numerical simulations to study how this affects the error predictions for the EoR 21-cm power spectrum.…