Isomers in Odd-Odd Nuclei

Informazioni relative al progetto

ISOON

ID dell’accordo di sovvenzione: 101150471

DOI

10.3030/101150471

Progetto chiuso

Data della firma CE 29 Aprile 2024

Data di avvio 3 Giugno 2024

Data di completamento 2 Giugno 2026

Finanziato da

• Marie Skłodowska-Curie Actions (MSCA)

Costo totale

Nessun dato

Contributo UE

€ 211 754,88

Coordinato da

COMMISSARIAT A L ENERGIE ATOMIQUE ET AUX ENERGIES ALTERNATIVES
France

Periodic Reporting for period 1 - ISOON (Isomers in Odd-Odd Nuclei)

Periodo di rendicontazione: 2024-06-03 al 2026-06-02

Sintesi del contesto e degli obiettivi generali del progetto

The ISOON project was designed to address fundamental questions in nuclear structure and isomerism. The primary motivation behind the project was to understand the microscopic origin of long-lived nuclear isomeric states, nuclear size, shape coexistence and exotic electromagnetic transitions in mediumt o heavy mass nuclei. Nuclear Isomers are excited states of atomic nuclei that live significantly longer than typical nuclear states, making the understanding of their formation and decay crucial for advancing fundamental physics.

To tackle these complex many-body problems, the project intergrated traditional theoretical modelling and close collaboration with experimental studies with cutting-edge computational techniques, including machine learning and quantum computing. The expected pathway to impact spans both fundamental scientific discovery and long-term societal applications. By investigating isomer decay hindrance mechanisms and electromagnetic transition rates, the project lays the scientific foundation for potential future technologies in energy storage. A key example is emerging concept of nuclear batteries which relies on the controlled manipulation of isomeric transitions. Furthermore, by building quantum algorithms and interpretable machine-learning frameworks for nuclear physics, the research supports cross-sector technological innovation that can benefit data-driven fields such as material science and quantum technologies.

Lavoro eseguito dall’inizio del progetto fino alla fine del periodo coperto dalla relazione e principali risultati finora ottenuti

During the fellowship period, the project achieved significant milestones across theoretical development, computational modelling and experimental interpretation. The key technical and scientific achievements include
1. Machine Learning for Nuclear Physics: A robust hybrid machine-learning framework was developed to predict nuclear charge radii. This approach combined numerical regression for high accuracy with symbolic regression to extract simplified, interpretable mathematical expressions.
2. Quantum Simulation: The project demonstrated a numerical simulation using the ADAPT-SSVQE algorithm to obtain low-lying states of nuclear systems in a single optimization run. This method was successfully applied to map a two-nucleon system to qubits, perfectly matching exact diagnoalization benchmarks.
3. Isomer Analysis and Predictions: Theoretical analysis directly supported the first lifetime measurement of 9- state in the odd-odd 92Nb. The applied shell model analysis successfully uncovered the curical role of core-excitations in dictating the origin and evolution of these negative-parity states. Besides this, large scale shell model calculations successfully explained complex decay schemes, missing transitions and pair breakups in isotopes such as 201Po, 202Po. Furthermore, the project established direct correlations between nuclear sixe and electric monopole transitions in calcium, argon and titanium isotopes using the interacting boson model.
4. Interdisciplinary Applications: A detailed shell model analysis quantified the dominant role of neutron-proton interactions in 93Mo, an important candidate for nuclear excitations by electronic capture. The study revealed that this favorable structure stems from subtle interaction systematics that do not persist across neighboring isotones.

Progressi oltre lo stato dell’arte e potenziale impatto previsto (incluso l’impatto socioeconomico e le implicazioni sociali più ampie del progetto fino ad ora)

The project has delivered substantial scientific understanding that pushes the state of the art in nuclear structure theory, artificial intelligence and computational physics.

1. The developed machine-learning framework successfully transformed black-box machine-learning models into physically interpretable formulas. This achievement provides physical insights comparable to traditional many-body models and sets a new benchmark for data-driven nuclear physics studies.

2. The project successfully demonstrated an adaptive algorithm capable of finding low-lying nuclear states simultaneously. This establishes a scalable pathway for simulating complex nuclear many-body systems on near-term quantum hardware, aligning with strategic goals in high-performance computing.

3. The research significantly refined theoretical frameworks, including the interacting boson model and the shell model, to provide precise microscopic explanations for long-lived isomers across multiple mass regions. These advancements clarified the quantitative roles of shape coexistence, core excitations and neutron-proton interactions in dictating exotic and parity-changing electromagnetic transitions.

Impact and Future needs: The findings revealed that the possible structure for nuclear excitation by electronic capture in 93Mo stems from highly specific interaction systematics providing a vital framework to identify future isomer candidates for potential energy storage capacities. To ensure further uptake and success, continued interdisciplinary collaboration is required specifically in the challenging experimental manipulation of isomeric transitions to fully realize its applications. The cross-sector data-driven methodologies developed also require further integration to benefit broader many-body quantum fields such as material science, high-energy physics.