Related papers: BBNet: accurate neural network emulator for primor…
After few minutes the Universe evolved through conditions of temperature and density which permitted the first synthesis of astrophysically interesting abundances of D, 3He, 4He and 7Li. The relic abundances are sensitive probes of the…
We present a new astrophysical site of the big bang nucleosynthesis (BBN) that are very peculiar compared with the standard BBN. Some models of the baryogenesis suggest that very high baryon density regions were formed in the early…
The products of primordial nucleosynthesis and the cosmic microwave background (CMB) photons are relics from the early evolution of the Universe whose observations probe the standard model of cosmology and provide windows on new physics…
In recent years, deep learning has shown great promise in the automated detection and classification of brain tumors from MRI images. However, achieving high accuracy and computational efficiency remains a challenge. In this research, we…
With massive amounts of atomic simulation data available, there is a huge opportunity to develop fast and accurate machine learning models to approximate expensive physics-based calculations. The key quantity to estimate is atomic forces,…
Bayesian neural network (BNN) approach is employed to improve the nuclear mass predictions of various models. It is found that the noise error in the likelihood function plays an important role in the predictive performance of the BNN…
In this paper, we present a novel approach to accelerate the Bayesian inference process, focusing specifically on the nested sampling algorithms. Bayesian inference plays a crucial role in cosmological parameter estimation, providing a…
Binary neural networks (BNN) have been studied extensively since they run dramatically faster at lower memory and power consumption than floating-point networks, thanks to the efficiency of bit operations. However, contemporary BNNs whose…
Time domain astronomy has emerged as a vibrant research field in recent years, focusing on celestial objects that exhibit variable magnitudes or positions. Given the urgency of conducting follow-up observations for such objects, the…
Porting state of the art deep learning algorithms to resource constrained compute platforms (e.g. VR, AR, wearables) is extremely challenging. We propose a fast, compact, and accurate model for convolutional neural networks that enables…
We consider Big Bang Nucleosynthesis (BBN) with long lived charged massive particles. Before decaying, the long lived charged particle recombines with a light element to form a bound state like a hydrogen atom. This effect modifies the…
Recently it is reported that there is the discrepancy between big bang nucleosynthesis theory and observations (BBN crisis). We show that BBN predictions agree with the primordial abundances of light elements, He4, D, He3 and Li7 inferred…
We reexamine big bang nucleosynthesis with large-scale baryon density inhomogeneities when the length scale of the density fluctuations exceeds the neutron diffusion length ($\sim 10^7-10^8$ cm at BBN), and the amplitude of the fluctuations…
Applications of new techniques in machine learning are speeding up progress in research in various fields. In this work, we construct and evaluate a deep neural network (DNN) to be used within a Bayesian statistical framework as a faster…
Quantum neural networks (QNNs) and quantum kernels stand as prominent figures in the realm of quantum machine learning, poised to leverage the nascent capabilities of near-term quantum computers to surmount classical machine learning…
Deep neural networks are powerful tools for solving nonlinear problems in science and engineering, but training highly accurate models becomes challenging as problem complexity increases. Non-convex optimization and sensitivity to…
Big Bang Nucleosynthesis (BBN) and the Cosmic Background Radiation (CBR) provide complementary probes of the early evolution of the Universe and of its particle content. Neutrinos play important roles in both cases, influencing the…
Big Bang Nucleosynthesis represents perhaps the first, and still perhaps the most powerful particle-astrophysics connection. As such, it should provide an example for other work in this area. I discuss the current status of standard model…
Electroconvection is a multiphysics problem involving coupling of the flow field with the electric field as well as the cation and anion concentration fields. For small Debye lengths, very steep boundary layers are developed, but standard…
A cosmological model with a time-varying mass of electrons seems a promising solution for the so-called Hubble tension. We examine the big bang nucleosynthesis (BBN) constraints on the time-varying electron mass model, because a larger…