相关论文: Simulation-based inference for neutrino interactio…
Recent advances in probabilistic modelling have led to a large number of simulation-based inference algorithms which do not require numerical evaluation of likelihoods. However, a public benchmark with appropriate performance metrics for…
We present a measurement of electron neutrino interactions from the Fermilab Booster Neutrino Beam using the MicroBooNE liquid argon time projection chamber to address the nature of the excess of low energy interactions observed by the…
The mixing parameters of sterile neutrino(s) preferred by the MiniBooNE and LNSD experiments are in strong tension with the exclusion limit from the IceCube experiment. Recently it has been claimed that by considering the non-standard…
We analyse the possible role of new interactions of neutrino in the forthcoming tritium beta decay experiment KATRIN aimed at detecting the neutrino mass with the sensitivity of 0.3 - 0.2 eV. It is shown that under certain circumstances the…
The current status of the Short Baseline Neutrino (SBN) project at Fermilab is reviewed. While the installation of SBND is still in progress, ICARUS has taken its first neutrino data on beam: using both the Booster Neutrino Beam (BNB) and…
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 sector; to determine the neutrino…
In particle physics, as in many areas of science, parameter inference relies on simulations to bridge the gap between theory and experiment. Recent developments in simulation-based inference have boosted the sensitivity of analyses;…
MicroBooNE (the Micro Booster Neutrino Experiment) is a short-baseline neutrino experiment based on the technology of a liquid-argon time-projection chamber (LArTPC), and has recently completed its first year of data-taking in the Fermilab…
Predictions of nuclear properties far from measured data are inherently imprecise because of uncertainties in our knowledge of nuclear forces and in our treatment of quantum many-body effects in strongly-interacting systems. While the model…
Adoption of renewable energy is essential to address the challenge of climate change, but that necessitates energy storage technologies. Lithium-ion batteries, the most ubiquitous solution, are insufficient for large-scale applications, so…
The MiniBooNE (Booster Neutrino Experiment) experiment is a precise search for neutrino oscillations but is crucially sensitive to background processes that would mimic an oscillation signal. The background sources include intrinsic…
The central approximation made in classical molecular dynamics simulation of materials is the interatomic potential used to calculate the forces on the atoms. Great effort and ingenuity is required to construct viable functional forms and…
We investigate the effect on neutrino oscillations generated by beyond-the-standard-model interactions between neutrinos and matter. Specifically, we focus on scalar-mediated non-standard interactions (NSI) whose impact fundamentally…
A substantial fraction of systematic uncertainties in neutrino oscillation experiments stem from the lack of precision in modeling the nuclear target in neutrino-nucleus interactions. Whilst this has driven significant progress in the…
We develop the framework of Linear Simulation-based Inference (LSBI), an application of simulation-based inference where the likelihood is approximated by a Gaussian linear function of its parameters. We obtain analytical expressions for…
Simulation-based inference (SBI) has become an important tool in cosmology for extracting additional information from observational data using simulations. However, all cosmological simulations are approximations of the actual universe, and…
We investigate non-standard neutrino interactions (NSIs) in the Zee--Babu model. The size of NSIs predicted by this model is obtained from a full scan over the parameter space, taking into account constraints from low-energy experiments…
Machine learning is applied to derive microscopically parameters of the interacting boson model for nuclear spectroscopy. A physics-guided neural network is proposed, which is trained to map the potential energy landscapes that are…
Over past years, the easy accessibility to the large scale datasets has significantly shifted the paradigm for developing highly accurate prediction models that are driven from Neural Network (NN). These models can be potentially impacted…
Nonstandard interactions (NSI) of neutrinos with matter can significantly alter neutrino flavor evolution in supernovae with the potential to impact explosion dynamics, nucleosynthesis, and the neutrinos signal. In this paper, we explore,…