Related papers: Detecting Fast Neutrino Flavor Conversions with Ma…
We derive general constraints on the mixing of heavy Seesaw neutrinos with the SM fields from a global fit to present flavour and electroweak precision data. We explore and compare both a completely general scenario, where the heavy…
Supernova neutrinos can exhibit a rich variety of flavor conversion mechanisms. In particular, they can experience "fast" self-induced flavor conversions almost immediately above the core. Very recently, a novel method has been proposed to…
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…
The neutrino fast flavor instability (FFI) can change neutrino flavor on time scales of nanoseconds and length scales of centimeters. It is expected to be ubiquitous in core-collapse supernovae and neutron star mergers, potentially…
Flavor-changing neutral currents (FCNCs) are forbidden at tree level in the Standard Model (SM), but they can be enhanced in physics Beyond the Standard Model (BSM) scenarios.In this paper, we investigate the effectiveness of deep learning…
Most of the free parameters in the Standard Model (SM) -- a quantum field theory which has successfully elucidated the behaviors of strong, weak and electromagnetic interactions of all the known fundamental particles, come from the lepton…
Neutrinos play pivotal roles in determining fluid dynamics, nucleosynthesis, and their observables in core-collapse supernova (CCSN) and binary neutron star merger (BNSM). In this paper, we present a novel phenomenon, collisional flavor…
Molecular communication (MC) is a promising paradigm for applications where traditional electromagnetic communications are impractical. However, decoding chemical signals, especially in multi-transmitter systems, remains a key challenge due…
We study neutrino masses in the framework of the supersymmetric inverse seesaw model. Different from the non-supersymmetric version a minimal realization with just one pair of singlets is sufficient to explain all neutrino data. We compute…
Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science. Luckily, several promising and closely related neural network models invariant to molecular symmetries have already…
We calculate rates of flavor exchange within clouds of neutrinos interacting with each other through the standard model coupling, assuming a conventional mass matrix. For cases in which there is an angular dependence in the relation among…
Neutrino flavor evolution in dense astrophysical environments such as core-collapse supernova (CCSN) is influenced by collective effects. While the Fast Flavor Instability (FFI) and the Collisional Flavor Instability (CFI) are recognized as…
Collective oscillations in dense neutrino gases (flavor waves) are notable for their instabilities that cause fast flavor conversion. We develop a quantum theory of interacting neutrinos and flavor wave quanta, which are analogous to…
Identifying phase transitions and classifying phases of matter is central to understanding the properties and behavior of a broad range of material systems. In recent years, machine-learning (ML) techniques have been successfully applied to…
We study the flavor evolution of a dense gas initially consisting of pure mono-energetic $\nu_e$ and $\bar\nu_e$. Using adiabatic invariants and the special symmetry in such a system we are able to calculate the flavor evolution of the…
In the dense supernova environment, neutrinos can undergo fast flavor conversions which depend on the large neutrino-neutrino interaction strength. It has been recently shown that both their presence and outcome can be affected when passing…
Fast flavor conversions of supernova neutrinos, possible near the neutrinosphere, depends on an interesting interplay of collisions and neutrino oscillations. Contrary to naive expectations, the rate of self-induced neutrino oscillations,…
Machine learning (ML) has emerged as a powerful tool for accelerating the computational design and production of materials. In materials science, ML has primarily supported large-scale discovery of novel compounds using first-principles…
Neutrino masses and mixings produce vacuum oscillations, an established quantum mechanical phenomenon. In matter, the Mikheev-Smirnov-Wolfenstein effect, due to neutrino interactions with the background particles, triggers resonant flavor…
Fast and collisional flavor instabilities possibly occur in the neutrino decoupling regions of core-collapse supernovae and neutron-star mergers. To gain a better understanding of the relevant flavor dynamics, we numerically solve for the…