Related papers: Simulation-based inference for Precision Neutrino …
Complex computer simulations are commonly required for accurate data modelling in many scientific disciplines, making statistical inference challenging due to the intractability of the likelihood evaluation for the observed data.…
Monte Carlo methods are widely used in particle physics to integrate and sample probability distributions (differential cross sections or decay rates) on multi-dimensional phase spaces. We present a Neural Network (NN) algorithm optimized…
Accelerator-based neutrino oscillation experiments have the potential to revolutionise our understanding of fundamental physics, offering an opportunity to characterise charge-parity violation in the lepton section, to determine the…
Available estimates for the energy resolution of DUNE vary by as much as a factor of four. To address this controversy, and to connect the resolution to the underlying physical processes, we build an independent simulation pipeline for…
This paper explores core-collapse supernovae as crucial targets for neutrino telescopes, addressing uncertainties in their simulation results. We comprehensively analyze eighteen modern simulations and discriminate among supernova models…
Future neutrino-oscillation experiments are expected to bring definite answers to the questions of neutrino-mass hierarchy and violation of charge-parity symmetry in the lepton sector. To realize this ambitious program it is necessary to…
As the calorimetric method of neutrino-energy reconstruction is generally considered to be largely insensitive to nuclear effects, its application seems to be an effective way for reducing systematic uncertainties in oscillation…
We propose a simple test of underground neutrino data to discriminate amongst neutrino oscillation models. It uses the asymmetry between downward-going events and upward-going events, for electron and muon events separately. Because of the…
Machine learning techniques are powerful tools for construction of emulators for complex systems. We explore different machine learning methods and conceptual methodologies, ranging from functional approximations to dynamical…
Reactor neutrinos play a substantial role in the study of the fundamental properties of neutrinos. With current and upcoming precision experiments, it is essential more than ever to understand the reactor neutrino flux and spectrum.…
We study the feasibility of using neutrino-electron elastic scattering to measure the neutrino flux in the DUNE neutrino oscillation experiment. The neutrino-electron scattering cross section is precisely known, and the kinematics of the…
We propose a new method of selection of high purity charge current quasielastic neutrino events with a good reconstruction of interacting neutrino energy. Performance of the method was verified with several tests using GENIE, NEUT and NuWro…
Deep neural network models have shown a great potential in accelerating the simulation of fluid dynamic systems. Once trained, these models can make inference within seconds, thus can be extremely efficient. However, they suffer from a…
One of the challenges that must be overcome in order to determine the neutrino mass hierarchy using reactor neutrinos is the theoretical uncertainty in the unoscillated reactor neutrino spectrum: this is one of the reasons why, recently, it…
Geoneutrinos, which are antineutrinos emitted during the decay of long-lived radioactive elements inside Earth, serve as a unique tool for studying the composition and heat budget of our planet. The Jiangmen Underground Neutrino Observatory…
The optical depth to reionization, a key parameter of the $\Lambda$CDM model, can be computed within astrophysical frameworks for star formation by modeling the evolution of the intergalactic medium. Accurate evaluation of this parameter is…
This paper presents an energy resolution study of the JUNO experiment, incorporating the latest knowledge acquired during the detector construction phase. The determination of neutrino mass ordering in JUNO requires an exceptional energy…
Velocity dispersion of the massive neutrinos presents a daunting challenge for non-linear cosmological perturbation theory. We consider the neutrino population as a collection of non-linear fluids, each with uniform initial momentum,…
Neural networks predictions are unreliable when the input sample is out of the training distribution or corrupted by noise. Being able to detect such failures automatically is fundamental to integrate deep learning algorithms into robotics.…
Many-body perturbation theory provides a powerful framework to study the ground state and thermodynamic properties of nuclear matter as well as associated single-particle potentials and response functions within a systematic order-by-order…