Related papers: NRFormer: Nationwide Nuclear Radiation Forecasting…
The organizers of this meeting have asked me to present perspectives of nuclear physics. This means to identify the areas where nuclear physics will be expanding in the next future. In six chapters a short overview of these areas will be…
The spectral emission of neutrinos from a plasma in nuclear statistical equilibrium (NSE) is investigated. Particular attention is paid to the possible emission of high energy (>10 MeV) neutrinos or antineutrinos. A newly developed…
The growing number of Earth observation satellites has led to increasingly diverse remote sensing data, with varying spatial, spectral, and temporal configurations. Most existing models rely on fixed input formats and modality-specific…
One of the persisting challenges associated with the peaceful use of the atom is monitoring the subsequent radiation. For example, nuclear power plants need environmental monitoring to ensure public trust. While this may be conducted by the…
Accurate precipitation forecasting is crucial for early warnings of disasters, such as floods and landslides. Traditional forecasts rely on ground-based radar systems, which are space-constrained and have high maintenance costs.…
Nuclear spin optical rotation (NSOR) is a recently developed technique for detection of nuclear magnetic resonance via rotation of light polarization, instead of the usual long-range magnetic fields. NSOR signals depend on hyperfine…
The prediction of cross sections for nuclei far off stability is crucial in the field of nuclear astrophysics. In recent calculations the nuclear level density -- as an important ingredient to the statistical model (Hauser-Feshbach) -- has…
This paper addresses a detection problem where several spatially distributed sensors independently observe a time-inhomogeneous stochastic process. The task is to decide between two hypotheses regarding the statistics of the observed…
Global and regional climate model projections are useful for gauging future patterns of climate variables, including solar radiation, but data from these models is often too coarse to assess local impacts. Within the context of solar…
To ensure safe clinical integration, deep learning models must provide more than just high accuracy; they require dependable uncertainty quantification. While current Medical Vision Transformers perform well, they frequently struggle with…
We investigate how the data from various future neutrino oscillation experiments will constrain the physics parameters for a three active neutrino mixing model. The investigations properly account for the degeneracies and ambiguities…
The demand for clean energy is ever increasing, with new nuclear technologies presenting a complementary solution to renewable energies. However, designing and operating these systems is exceptionally difficult, given the complexity of the…
Accurate forecasting of residual Physical Resource Blocks (PRBs) is critical for proactive network slice provisioning, energy-efficient operation, and spectrum-aware decision making in cellular systems, where residual PRBs serve as a…
A substantial fraction of systematic uncertainties in neutrino oscillation experiments stems from the lack of precision in modeling the nucleus when describing the neutrino-nucleus interactions. The Spectral Function (SF) model features a…
Simulating the long-term dynamics of multi-scale and multi-physics systems poses a significant challenge in understanding complex phenomena across science and engineering. The complexity arises from the intricate interactions between scales…
Precision tests of the Standard Model and searches for beyond the Standard Model physics often require nuclear structure input. There has been a tremendous progress in the development of nuclear ab initio techniques capable of providing…
When solving forecasting problems including multiple time-series features, existing approaches often fall into two extreme categories, depending on whether to utilize inter-feature information: univariate and complete-multivariate models.…
Accurate prediction of energy and forces for 3D molecular systems is one of fundamental challenges at the core of AI for Science applications. Many powerful and data-efficient neural networks predict molecular energies and forces from…
We present the proton and neutron vector form factors in a convenient parametric form that is optimized for momentum transfers $\lesssim$ few GeV$^2$. The form factors are determined from a global fit to electron scattering data and precise…
Most atomic nuclei exhibit ellipsoidal shapes characterized by quadrupole deformation $\beta_2$ and triaxiality $\gamma$, and sometimes even a pear-like octupole deformation $\beta_3$. The STAR experiment introduced a new…