Related papers: Deep Learning for Line Intensity Mapping Observati…
Line intensity mapping (LIM) has the potential to produce highly precise measurements of scale-dependence bias from primordial non-Gaussianity (PNG) due to its ability to map much larger volumes than are available from galaxy surveys. PNG…
Cosmic microwave background (CMB) experiments that constrain the tensor-to-scalar ratio $r$ are now approaching the sensitivity at which delensing---removing the $B$ modes induced by the gravitational lensing of large-scale structure---is…
Understanding the large-scale structure of the Universe and unravelling the mysteries of dark matter are fundamental challenges in contemporary cosmology. Reconstruction of the cosmological matter distribution from lensing observables,…
Line intensity mapping (LIM) is an emerging technique for probing the large-scale structure (LSS) in the post-reionisation era. This captures the integrated flux of a particular spectral line emission from multiple sources within a patch of…
The cosmic infrared background (CIB) is a powerful probe of large-scale structure across a very large redshift range, and consists of unresolved redshifted infrared emission from dusty galaxies. It can be used to study the astrophysics of…
Learning from Noisy Labels (LNL) remains a fundamental challenge in deep learning because real-world datasets often contain corrupted annotations. Most existing methods rely on label correction or sample selection mechanisms. In contrast,…
Label noise is a critical factor that degrades the generalization performance of deep neural networks, thus leading to severe issues in real-world problems. Existing studies have employed strategies based on either loss or uncertainty to…
Learning from noisy labels (LNL) is a challenge that arises in many real-world scenarios where collected training data can contain incorrect or corrupted labels. Most existing solutions identify noisy labels and adopt active learning to…
Extracting cosmological information from microwave sky observations requires accurate estimation of the underlying Cosmic Microwave Background (CMB) by removing foreground contamination, instrumental noise, and the effects of beam…
We investigate and demonstrate the use of convolutional neural networks (CNNs) for the task of distinguishing between merging and non-merging galaxies in simulated images, and for the first time at high redshifts (i.e. $z=2$). We extract…
Line Intensity Mapping (LIM) is a new observational technique that uses low-resolution observations of line emission to efficiently trace the large-scale structure of the Universe out to high redshift. Common mm/sub-mm emission lines are…
Line intensity mapping experiments seek to trace large scale structure by measuring the spatial fluctuations in the combined emission, in some convenient spectral line, from individually unresolved galaxies. An important systematic concern…
The technique of intensity mapping (IM) has emerged as a powerful tool to explore the universe at $z < 6$. IM measures the integrated emission from sources over a broad range of frequencies, unlocking significantly more information than…
The evolution of large-scale structure, galaxies and the intergalactic medium (IGM) during the Epoch of Reionization (EoR) can be probed by upcoming Line Intensity Mapping (LIM) experiments, which sample in redshift and direction without…
Constraints on primordial non-Gaussianity (PNG) will shed light on the origin of primordial fluctuations and the physics of the early universe. The intensity mapping technique is a promising probe of structure formation on large scales; at…
Real-world datasets commonly have noisy labels, which negatively affects the performance of deep neural networks (DNNs). In order to address this problem, we propose a label noise robust learning algorithm, in which the base classifier is…
Cosmic Dawn Intensity Mapper is a "Probe Class" mission concept for reionization studies of the universe. It will be capable of spectroscopic imaging observations between 0.7 to 6-7 microns in the near-Infrared. The primary observational…
This paper investigates the problem of dim frequency line detection and recovery in the so-called lofargram. Theoretically, time integration long enough can always enhance the detection characteristic. But this does not hold for irregularly…
We investigate the detection prospects for gravitational lensing of three-dimensional maps from upcoming line intensity surveys, focusing in particular on the impact of gravitational nonlinearities on standard quadratic lensing estimators.…
Recently, significant progress has been achieved in sensing real large-scale outdoor 3D environments, particularly by using modern acquisition equipment such as LiDAR sensors. Unfortunately, they are fundamentally limited in their ability…