The French National Institute of Nuclear and Particle Physics (French: Institut national de physique nucléaire et de physique des particules, IN2P3) is the coordinating body for nuclear and particle physics in France. It was established in 1971 as a division of the French National Centre for Scientific Research (CNRS). Its purpose is "to promote and unite research activities in the various fields of physics"..
We introduce a robust, interpretable machine learning (ML) framework that combines numerical regression for high-accuracy predictions with symbolic regression to uncover the underlying physics. This hybrid approach effciently derives analytical expressions by leveraging the smoothed predictions of optimized ML models, a significant acceleration over direct symbolic regression on raw experimental data. We apply this framework, as an example, to nuclear charge radii across the nuclear chart, notably including light nuclei that are often excluded from such studies. We employ Light Gradient Boosting Machine (LGBM) and Gaussian Process Regression (GPR) models to map correlations between charge radii and key physical features: mass A^1/3 and proton number Z^1/3 dependencies, total binding energy, and for the first time, the pairing gap. Our models are rigorously trained using four-fold cross-validation with automated hyperparameter optimization, ensuring robustness and generalizability, which is critical for the typically small and skewed datasets in nuclear physics. Finally, we distill the knowledge from the initial LGBM and GPR models into simplified, interpretable mathematical expressions via symbolic regression, white-boxing these ML models. The derived formulas provide physical insights comparable to traditional many-body models and demonstrate a powerful pathway for physics expression discovery guided by ML.
The phase-space approximation (PSA) approach, originally applied in [Phys. Rev. D 106, 123006 (2022)] to describe neutrino oscillations from a dense astrophysical source such as a core-collapse supernova in the two-flavor limit, is extended here to describe the more realistic case where neutrinos can oscillate between three different flavors. The approach is successfully validated against the exact solutions up to eight neutrinos. In all cases where the exact solution is feasible, the PSA provides excellent reproduction of the neutrino oscillation dynamics. By replacing the full problem with a set of simple mean-field equations, the PSA offers a versatile, predictive, and easily parallelizable approach for tackling three-flavor problems. This enables the simulation of large-scale neutrino oscillations, as illustrated here with simulations involving up to 300 neutrinos. Even if direct comparison to exact solutions is only feasible for small systems (up to eight neutrinos), where PSA shows excellent agreement, its applicability in the large-N regime appears promising, although a full quantitative validation remains beyond current computational capabilities. Additionally, the method also provides insight into the system's equilibration properties.
We explore the connection of positivity of the imaginary part of forward elastic amplitudes for perturbative scattering with consistency of the entanglement generated by the S-matrix, for states with arbitrary internal quantum numbers such as flavor. We also analyze "disentanglers," certain highly entangled initial states for which the action of the S-matrix is to decrease subsystem entanglement. As a by-product, we develop a framework based on wave packets to regularize the spacetime divergences that appear in the plane-wave derivation of the entanglement expression.
We introduce a fast method to measure the conversion gain in complementary metal-oxide-semiconductor active pixel sensors, which accounts for nonlinearity and interpixel capacitance (IPC). The standard “mean-variance” method is biased because it assumes that pixel values depend linearly on the signal, and existing methods to correct for nonlinearity still introduce significant biases. While current IPC correction methods are prohibitively slow for a per-pixel application, our new method uses separate measurements of the IPC kernel to calculate the gain almost instantaneously. Using test data from a flight detector of the ESA Euclid mission, the IPC correction recovers the results of slower methods with 0.1% accuracy. The nonlinearity correction ensures that the estimated gain is independent of signal, correcting a bias of more than 2.5%.
The nuclear shell model is a general way of expressing the many-body nuclear Hamiltonian and deciphering the underlying nuclear structure. In today's era of modern and high-power computation, the primary limitation of the nuclear shell model is the enormous dimensionality of its Hilbert space, which far exceeds available storage capacity and prevents the diagonalization of the full Hamiltonian matrix in that space. Quantum computing offers a scalable solution to bypass this curse of dimensionality. In this work, we introduce a single-run quantum simulation capable of obtaining multiple shell-model eigenstates simultaneously. The nuclear Hamiltonian is transformed from a bit to a qubit basis using the Jordan-Wigner transformation, explicitly preserving fermionic anti-commutation. We employ a Subspace Search Variational Quantum Eigensolver (SSVQE) along with an Adaptive Derivative-Assembled Pseudo-Trotter (ADAPT) ansatz to construct the quantum circuit required to solve the shell-model problem. The ADAPT-SSVQE algorithm uses a symmetry-preserving single and double-excitation operator pool and optimizes a weighted energy sum to obtain the simultaneous convergence of all eigenstates within a targeted MJ subspace, eliminating the need for post-processing efforts to extract excited spectra. We benchmark this approach by solving the problem for two and three identical nucleons in a j = 9/2 orbital, successfully extracting five and ten mutually orthogonal states, respectively, within a 10-qubit active space. The algorithm achieves spectroscopic accuracy, in simulation, relative to exact diagonalization and intrinsically restores total angular momentum (Ĵ^2) symmetry.
