Fast-time Variations of Supernova Neutrino Fluxes and Detection Perspectives

AbstractIn the delayed explosion scenario of a core-collapse supernova, the accretion phase shows pronounced convective over-turns and a low-multipole hydrodynamic instability, the so-called standing accretion shock instability (SASI). Neutrino signal variations from the first full-scale three-dimensional core-collapse supernova simulations with sophisticated neutrino transport are presented as well as their detection perspectives in IceCube and Hyper-Kamiokande

Supernova neutrino three-flavor evolution with dominant collective effects

Neutrino and antineutrino fluxes from a core-collapse galactic supernova are studied, within a representative three-flavor scenario with inverted mass hierarchy and tiny 1-3 mixing. The initial flavor evolution is dominated by collective self-interaction effects, which are computed in a full three-family framework along an averaged radial trajectory. During the whole time span considered (t=1-20 s), neutrino and antineutrino spectral splits emerge as dominant features in the energy domain for the final, observable fluxes. Some minor or unobservable three-family features (e.g., related to the muonic-tauonic flavor sector) are also discussed for completeness. The main results can be useful for SN event rate simulations in specific detectors.Comment: 22 pages, including 9 figures (1 section with 3 figures added). Accepted for publication in JCA

Metric-like Lagrangian Formulations for Higher-Spin Fields of Mixed Symmetry

We review the structure of local Lagrangians and field equations for free bosonic and fermionic gauge fields of mixed symmetry in flat space. These are first presented in a constrained setting extending the metric formulation of linearized gravity, and then the ($\gamma$-)trace constraints on fields and gauge parameters are eliminated via the introduction of auxiliary fields. We also display the emergence of Weyl-like symmetries in particular classes of models in low space-time dimensions.Comment: 136 pages, LaTeX. References added. Final version to appear in La Rivista del Nuovo Cimento

Diffuse supernova neutrinos: oscillation effects, stellar cooling and progenitor mass dependence

We estimate the diffuse supernova neutrino background (DSNB) using the recent progenitor-dependent, long-term supernova simulations from the Basel group and including neutrino oscillations at several post-bounce times. Assuming multi-angle matter suppression of collective effects during the accretion phase, we find that oscillation effects are dominated by the matter-driven MSW resonances, while neutrino-neutrino collective effects contribute at the 5-10% level. The impact of the neutrino mass hierarchy, of the time-dependent neutrino spectra and of the diverse progenitor star population is 10% or less, small compared to the uncertainty of at least 25% of the normalization of the supernova rate. Therefore, assuming that the sign of the neutrino mass hierarchy will be determined within the next decade, the future detection of the DSNB will deliver approximate information on the MSW-oscillated neutrino spectra. With a reliable model for neutrino emission, its detection will be a powerful instrument to provide complementary information on the star formation rate and for learning about stellar physics.Comment: 19 pages, including 4 figures and 1 table. Clarifying paragraphs added; results unchanged. Matches published version in JCA

A proposal of quantum-inspired machine learning for medical purposes: An application case

Learning tasks are implemented via mappings of the sampled data set, including both the classical and the quantum framework. Biomedical data characterizing complex diseases such as cancer typically require an algorithmic support for clinical decisions, especially for early stage tumors that typify breast cancer patients, which are still controllable in a therapeutic and surgical way. Our case study consists of the prediction during the pre-operative stage of lymph node metastasis in breast cancer patients resulting in a negative diagnosis after clinical and radiological exams. The classifier adopted to establish a baseline is characterized by the result invariance for the order permutation of the input features, and it exploits stratifications in the training procedure. The quantum one mimics support vector machine mapping in a high-dimensional feature space, yielded by encoding into qubits, while being characterized by complexity. Feature selection is exploited to study the performances associated with a low number of features, thus implemented in a feasible time. Wide variations in sensitivity and specificity are observed in the selected optimal classifiers during cross-validations for both classification system types, with an easier detection of negative or positive cases depending on the choice between the two training schemes. Clinical practice is still far from being reached, even if the flexible structure of quantum-inspired classifier circuits guarantees further developments to rule interactions among features: this preliminary study is solely intended to provide an overview of the particular tree tensor network scheme in a simplified version adopting just product states, as well as to introduce typical machine learning procedures consisting of feature selection and classifier performance evaluation