Axion-like particles (ALPs) coupled to nucleons can be efficiently produced in core-collapse supernovae (SNe) and then, if they couple to photons, convert into gamma rays in cosmic magnetic fields, generating short gamma-ray bursts. Though ALPs from a Galactic SN would induce an intense and easily detectable gamma-ray signal, such events are exceedingly rare. In contrast, a few SNe per year...
The study of semileptonic $B$ decays into charmed mesons $D^{(*)}$ plays a crucial role in the determination of CKM matrix element $V_{cb}$, improving and studying the nonperturbative dynamics of QCD, in testing heavy-quark symmetry. Indeed, these processes are described by matrix elements between hadronic states, which are nonperturbative. They can be expressed in terms of functions of $q^2$...
We present an updated global determination of the neutrino oscillation parameters governing the (ν1 ,ν2 ) sector, including the latest SNO+ results and the first measurement from JUNO. In the three-neutrino framework, we focus on the solar mass splitting δm2 and the mixing parameter sin2θ12 , which drive solar and medium-baseline reactor oscillations.
The new constraints are incorporated into...
The Unruh effect predicts that a uniformly accelerated observer in the Minkowski vacuum perceives a thermal bath, so that an accelerating detector behaves as an open quantum system interacting with an effective thermal environment. We exploited this perspective to study entanglement generation between two identical two-level atoms located at the same spacetime point and weakly coupled to the...
Despite the countless experimental confirmations, the Standard Model (SM) leaves many unanswered questions. Tensions have emerged over time between its predictions and experimental data, mostly in the flavor sector. For these reasons, a theory capable to account at least partially for the observed discrepancies is actively sought for.
Notable examples in the exploration of physics beyond...
A powerful way to study quantum systems is through their symmetries, or equivalently, the conserved quantities associated with the Hamiltonian. Standard quantum mechanics tells us that these correspond to operators commuting with the Hamiltonian. Once the Hamiltonian is known, its conserved quantities can, at least in principle, be identified.
However, only those symmetries that persist under...
In this work, we combine coarse-grained Brownian dynamics simulations and mean-field theory to study supercoiling dynamics, as well as the steady-state profiles of twist and writhe, in an open DNA polymer where one of the free ends is subjected to a constant torque. Even though the other end is free, and hence can spin and release torsional stress, we observe that the entire chain transitions...
High-frequency gravitational waves may provide a unique signature for the existence of exotic physics. The lack of current and future gravitational-wave experiments sensitive at those frequencies leads to the need of employing different indirect techniques. Notably, one of the most promising ones is constituted by graviton-photon conversions in magnetic fields. Our research focuses on the...
Waveguide Quantum Electrodynamics (Waveguide QED) is a promising and versatile platform for studying fundamental light-matter interactions and quantum technology implementations. Notably, interesting effects emerge when two or more quantum emitters are coupled to the waveguide, including collective phenomena, e.g., superradiance and formation of bound states in the continuum (BICs).
An...
We investigate a two-dimensional chiral fluid composed of Brownian disks interacting via a Lennard-Jones potential and subjected to a nonconservative transverse force, mimicking colloids spinning at a given rate.
Focusing on the liquid phase, characterized by rotating hexatic patches, we demonstrate that increasing chiral activity modifies the system’s effective temperature. In the solid...
Machine learning permeates our everyday life, with applications ranging from disease diagnosis and environmental monitoring to fraud detection. Despite its successes, modern machine learning still faces major challenges, including the need for extensive computational resources, large training datasets, and a high number of trainable parameters. In recent years, an exciting avenue to overcome...
