Related papers: Application of Machine Learning to 21 cm Cosmology
Land surface temperature (LST) is vital for land-atmosphere interactions and climate processes. Accurate LST retrieval remains challenging under heterogeneous land cover and extreme atmospheric conditions. Traditional split window (SW)…
21 cm cosmology is a promising new probe of the evolution of visible matter in our universe, especially during the poorly-constrained Cosmic Dawn and Epoch of Reionization. However, in order to separate the 21 cm signal from bright…
The 21-cm forest is a promising probe of the Epoch of Reionization. The local state of the intergalactic medium (IGM) is encoded in the spectrum of a background source (radio-loud quasars or gamma ray burst afterglow) by absorption at the…
Detection of redshifted \ion{H}{i} 21-cm emission is a potential probe for investigating the Universe's first billion years. However, given the significantly brighter foreground, detecting 21-cm is observationally difficult. The Earth's…
The 21-cm signal of neutral hydrogen - emitted during the Epoch of Reionization - promises to be an important source of information for the study of the infant universe. However, its detection is impossible without sufficient mitigation of…
Machine learning (ML) can facilitate efficient thermoelectric (TE) material discovery essential to address the environmental crisis. However, ML models often suffer from poor experimental generalizability despite high metrics. This study…
21cm Intensity Mapping (IM) has been proposed about 15 years ago as a cost effective method to carry out cosmological surveys and to map the 3D distribution of matter in the universe, over a large range of post EoR redshifts, from z=0 to…
First-principles computations are the driving force behind numerous discoveries of hydride-based superconductors, mostly at high pressures, during the last decade. Machine-learning (ML) approaches can further accelerate the future…
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…
Synthesis of advanced inorganic materials with minimum number of trials is of paramount importance towards the acceleration of inorganic materials development. The enormous complexity involved in existing multi-variable synthesis methods…
Scientific progress is tightly coupled to the emergence of new research tools. Today, machine learning (ML)-especially deep learning (DL)-has become a transformative instrument for quantum science and technology. Owing to the intrinsic…
Nuclear materials are often demanded to function for extended time in extreme environments, including high radiation fluxes and transmutation, high temperature and temperature gradients, stresses, and corrosive coolants. They also have a…
Machine learning (ML) methods can expand our ability to construct, and draw insight from large datasets. Despite the increasing volume of planetary observations, our field has seen few applications of ML in comparison to other sciences. To…
The Epoch of Reionization (EoR) and Cosmic Dawn (CD) are pivotal stages during the first billion years of the universe, exerting a significant influence on the development of cosmic structure. The detection of the redshifted 21-cm signal…
The study of the cosmic Dark Ages, Cosmic Dawn, and Epoch of Reionization (EoR) using the all-sky averaged redshifted HI 21cm signal, are some of the key science goals of most of the ongoing or upcoming experiments, for example, EDGES,…
We investigate the 21-cm signature that may arise from the intergalactic medium (IGM) prior to the epoch of full reionization (z>5). In scenarios in which the IGM is reionized by discrete sources of photoionizing radiation, the neutral gas…
Virtually all aspects of many-body atomic physics are challenging: experiments are technically demanding, datasets have become enormous, and the memory and CPU requirements for classical simulation of generic quantum systems often scale…
Machine learning has rose to become an important research tool in the past decade, its application has been expanded to almost if not all disciplines known to mankind. Particularly, the use of machine learning in astrophysics research had a…
Geological carbon and energy storage are pivotal for achieving net-zero carbon emissions and addressing climate change. However, they face uncertainties due to geological factors and operational limitations, resulting in possibilities of…
The redshifted 21-cm signal of neutral Hydrogen is a promising probe into the period of evolution of our Universe when the first stars were formed (Cosmic Dawn), to the period where the entire Universe changed its state from being…