2025
Quick information
Type Seminar
Date July 18, 2025 - 11:00
Time 11:00
Location Room 105, GANIL, Caen | France
Share this event
More events
Conference
September 22 > 26, 2025
EuNPC 2025
Moho, Caen | France
Seminar
September 19, 2025 - 11:00
CP Violation and Nuclear Schiff Moments: DFT and Ab Initio
Room 105, GANIL, Caen | France
Seminar
September 05, 2025 - 11:00
Accelerate the energy transition
Room 105, GANIL, Caen | France

From nuclear structure to time-dependent dynamics

Alexander Volya (Florida State University, USA)

The exponential decay of unstable states is one of the most pervasive and well-studied phenomena in physics, yet its quantum-mechanical foundations remain obscure in several important respects. Exponential decay is not a straightforward consequence of quantum dynamics; rather, it arises from a subtle equilibrium involving a resonant state with a decaying amplitude, shaped by the interplay between outgoing radiation and internal dynamics. A deeper understanding requires careful attention to the early- and late-time behavior of the system, especially in the case of  near-threshold nuclear states.
 
Unlike bound states, resonant states retain a “memory” of their formation, including non-resonant background contributions and quantum entanglement. Their decaying wave functions remain influenced by internal dynamics, leading to complex, non-exponential, and sometimes noisy decay behavior. These features reveal nuanced transient stages between distinct decay regimes, offering deeper insight into underlying nuclear structure and reactions from a time-dependent perspective.
 
This talk will highlight recent theoretical and experimental efforts to investigate nuclear structure through complex decay processes. Emphasis will be placed on novel methodologies for probing near-threshold behavior and transient phenomena, as well as on identifying observables and developing physical interpretations of near-threshold nuclear structure. The discussion will be supported by realistic nuclear examples and experimental data.