Related papers: Deep learning reconstruction of three-dimensional …
Emission from the interstellar medium can be a significant contaminant of measurements of the intensity and polarization of the cosmic microwave background (CMB). For planning CMB observations, and for optimizing foreground-cleaning…
We explore how information in images of nearby galaxies can be used to estimate their distance. We train a convolutional Neural Network (NN) to do this, using galaxy images from the Illustris simulation. We show that if the NN is trained on…
3D laser scanning by LiDAR sensors plays an important role for mobile robots to understand their surroundings. Nevertheless, not all systems have high resolution and accuracy due to hardware limitations, weather conditions, and so on.…
Accurately determining neutrino masses is a main objective of contemporary cosmology. Since massive neutrinos affect structure formation and evolution, probes of large scale structure are sensitive to the sum of their masses. In this work,…
Mapping of multiple lines such as the fine-structure emission from [CII] (157.7 $\mu \text{m}$), [OIII] (52 \& 88.4 $\mu \text{m}$), and rotational emission lines from CO are of particular interest for upcoming line intensity mapping (LIM)…
Hydrodynamical simulations play a fundamental role in modern cosmological research, serving as a crucial bridge between theoretical predictions and observational data. However, due to their computational intensity, these simulations are…
Upcoming large astronomical surveys are expected to capture an unprecedented number of strong gravitational lensing systems. Deep learning is emerging as a promising practical tool for the detection and quantification of these galaxy-scale…
We study how 21 cm intensity mapping can be used to measure gravitational lensing over a wide range of redshift. This can extend weak lensing measurements to higher redshifts than are accessible with conventional galaxy surveys. We…
Deep neural networks give state-of-the-art accuracy for reconstructing images from few and noisy measurements, a problem arising for example in accelerated magnetic resonance imaging (MRI). However, recent works have raised concerns that…
Last year, IEEE 802.11 Extremely High Throughput Study Group (EHT Study Group) was established to initiate discussions on new IEEE 802.11 features. Coordinated control methods of the access points (APs) in the wireless local area networks…
The matter distribution of the Universe can be mapped through the weak gravitational lensing (WL) effect: small distortions of the shapes of distant galaxies, which reflects the inhomogeneity of the cosmic density field. The most dominant…
Interpreting the predictions of a black-box deep network can facilitate the reliability of its deployment. In this work, we propose a re-label distillation approach to learn a direct map from the input to the prediction in a…
The abundance of cold molecular gas plays a crucial role in models of galaxy evolution. While deep spectroscopic surveys of CO emission lines have been a primary tool for measuring this abundance, the difficulty of these observations has…
Line intensity mapping (LIM) provides a unique and powerful means to probe cosmic structures by measuring the aggregate line emission from all galaxies across redshift. The method is complementary to conventional galaxy redshift surveys…
Searches and analyses of strong gravitational lenses are challenging due to the rarity and image complexity of these astronomical objects. Next-generation surveys (both ground- and space-based) will provide more opportunities to derive…
The redshifted spectral line radiation emitted from both atomic fine-structure and molecular rotational transitions in the interstellar medium (ISM) of high-redshift galaxies can be detected in the centimetre, millimetre and submillimetre…
We have developed a new geometrical method for identifying and reconstructing a homogeneous and highly complete set of galaxy groups in the next generation of deep, flux-limited redshift surveys. Our method combines information from the…
We demonstrate the ability of convolutional neural networks (CNNs) to mitigate systematics in the virial scaling relation and produce dynamical mass estimates of galaxy clusters with remarkably low bias and scatter. We present two models,…
Galaxy groups are essential for studying the distribution of matter on a large scale in redshift surveys and for deciphering the link between galaxy traits and their associated halos. In this work, we propose a widely applicable method for…
Infrared (IR), sub-millimetre (sub-mm) and millimetre (mm) databases contain a huge quantity of high quality data. However, a large part of these data are photometric, and are thought not to be useful to derive a quantitative information on…