Related papers: BBNet: accurate neural network emulator for primor…
We study the big-bang nucleosynthesis (BBN) with the long-lived exotic particle, called X. If the lifetime of X is longer than \sim 0.1 sec, its decay may cause non-thermal nuclear reactions during or after the BBN, altering the predictions…
Big-Bang nucleosynthesis (BBN) represents one of the earliest phenomena that can lead to observational constraints on the early Universe properties. It is well-known that many important mechanisms and phase transitions occurred before BBN.…
Recent works on Binary Neural Networks (BNNs) have made promising progress in narrowing the accuracy gap of BNNs to their 32-bit counterparts. However, the accuracy gains are often based on specialized model designs using additional 32-bit…
Neural networks have achieved remarkable performance across various problem domains, but their widespread applicability is hindered by inherent limitations such as overconfidence in predictions, lack of interpretability, and vulnerability…
We describe the main features of a new and updated version of the program PArthENoPE, which computes the abundances of light elements produced during Big Bang Nucleosynthesis. As the previous first release in 2008, the new one, PArthENoPE…
The light relic density affects the thermal and expansion history of the early Universe leaving a number of observable imprints. We focus on the primordial abundances of light elements produced during the process of Big Bang nucleosynthesis…
I show that early-time (pre-recombination) solutions to the Hubble tension are generically expected to increase the preferred baryon density $\omega_b$. This puts these models in tension with Big Bang Nucleosynthesis (BBN), as measurements…
This paper addresses the need for deep learning models to integrate well-defined constraints into their outputs, driven by their application in surrogate models, learning with limited data and partial information, and scenarios requiring…
Combined with other CMB experiments, the WMAP survey provides an accurate estimate of the baryon density of the Universe. In the framework of the standard Big Bang Nucleosynthesis (BBN), such a baryon density leads to predictions for the…
The light elements up to 7Li were produced by the Standard Big Bang nucleosynthesis (SBBN) in the early universe assuming standard conditions. All observed primordial abundances of these light elements match very well the predicted ones by…
We derive constraints from standard (with $N_\nu = 3$) BBN arising solely from two cosmologically produced nuclides, \he4 and \li7, from which the extrapolation to primordial abundances is straightforward. The abundances of D and \he3 are…
We report the results of a new accurate evaluation of light nuclei yields in primordial nucleosynthesis. All radiative effects, finite nucleon mass, thermal and plasma corrections are included in the proton to neutron conversion rates. The…
Big Bang Nucleosynthesis (BBN) explores the first few minutes of nuclei formation after the Big Bang. We present updates that result in new constraints at the 2{\sigma} level for the abundances of the four primary light nuclides -…
Emulator embedded neural networks, which are a type of physics informed neural network, leverage multi-fidelity data sources for efficient design exploration of aerospace engineering systems. Multiple realizations of the neural network…
We investigate the impact of a nonstandard electron mass $m_e$ on early-Universe thermal history, focusing on neutrino decoupling and Big Bang Nucleosynthesis (BBN). In the standard cosmology, neutrino--electron interactions keep neutrinos…
Within an early Universe scenario, nonextensive thermostatistics (NET) is investigated on the basis of data concerning primordial Helium abundance. We obtain first order corrections to the energy densities and weak interactions rates, and…
Ordinary-sterile neutrino oscillations can generate significant neutrino asymmetry in the early Universe. In this paper we extend this work by computing the evolution of neutrino asymmetries and light element abundances during the Big Bang…
We propose a novel deep learning tool in order to study the evolution of dark energy models. The aim is to combine two architectures: the Recurrent Neural Networks (RNN) and the Bayesian Neural Networks (BNN), we named this full network as…
This work presents a quantum convolutional neural network (QCNN) for the classification of high energy physics events. The proposed model is tested using a simulated dataset from the Deep Underground Neutrino Experiment. The proposed…
Deep neural networks have significantly improved performance on a range of tasks with the increasing demand for computational resources, leaving deployment on low-resource devices (with limited memory and battery power) infeasible. Binary…