Related papers: A Review on Machine Learning for Neutrino Experime…
Modeling of physical systems includes extensive use of software packages that implement the accurate finite element method for solving differential equations considered along with the appropriate initial and boundary conditions. When the…
An elementary general overview of the neutrino physics and astrophysics is given. We start by a historical account of the development of our understanding of neutrinos and how they helped to unravel the structure of the Standard Model. We…
Direct dark matter detection experiments will soon be sensitive to neutrinos from astrophysical sources, including the Sun, the atmosphere, and supernova. This sets an important benchmark for these experiments, and opens up a new window in…
Neutrino geophysics, the study of the Earth's interior by measuring the fluxes of geologically produced neutrino at its surface, is a new interdisciplinary field of science, rapidly developing as a synergy between geology, geophysics and…
The next generation of particle physics experiments will face a new era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environments and operational constraints. Harnessing this data for…
From several neutrino oscillation experiments, we understand now that neutrinos have mass. However, we really don't know what mechanism is responsible for producing this neutrino mass. Current or planned neutrino experiments utilize…
Machine learning has emerged as a powerful tool in materials discovery, enabling the rapid design of novel materials with tailored properties for countless applications, including in the context of energy and sustainability. To ensure the…
Machine learning techniques have found their way into computational chemistry as indispensable tools to accelerate atomistic simulations and materials design. In addition, machine learning approaches hold the potential to boost the…
Over the past few decades, outstanding progress in the neutrino experiment frontier has greatly advanced our understanding of the flavor oscillations of active neutrinos. The remaining key questions such as the neutrino mass ordering, the…
The Deep Underground Neutrino Experiment is a next-generation neutrino oscillation experiment that aims to measure $CP$-violation in the neutrino sector as part of a wider physics program. A deep learning approach based on a convolutional…
Attempts to apply Neural Networks (NN) to a wide range of research problems have been ubiquitous and plentiful in recent literature. Particularly, the use of deep NNs for understanding complex physical and chemical phenomena has opened a…
Deep learning, a branch of artificial intelligence, is a data-driven method that uses multiple layers of interconnected units or neurons to learn intricate patterns and representations directly from raw input data. Empowered by this…
There is significant interest in using modern neural networks for scientific applications due to their effectiveness in modeling highly complex, non-linear problems in a data-driven fashion. However, a common challenge is to verify the…
The use of machine learning techniques has significantly increased the physics discovery potential of neutrino telescopes. In the upcoming years, we are expecting upgrade of currently existing detectors and new telescopes with novel…
After a fascinating phase of discoveries, neutrino physics still has a few mysteries such as the absolute mass scale, the mass hierarchy, the existence of CP violation in the lepton sector and the existence of right-handed neutrinos. It is…
The excess of electron-like events measured by MiniBooNE challenges our understanding of neutrinos and their interactions. We review the status of this open problem and ongoing efforts to resolve it. After introducing the experiment and its…
Current neutrino experiments measure the neutrino mixing parameters with an unprecedented accuracy. The upcoming generation of experiments will be sensitive to subdominant effects that can give information on the unknown neutrino…
Neural networks are among the most accurate supervised learning methods in use today. However, their opacity makes them difficult to trust in critical applications, especially when conditions in training may differ from those in practice.…
Neutrino astrophysics offers new perspectives on the Universe investigation: high energy neutrinos, produced by the most energetic phenomena in our Galaxy and in the Universe, carry complementary (if not exclusive) information about the…
Experiments in particle physics produce enormous quantities of data that must be analyzed and interpreted by teams of physicists. This analysis is often exploratory, where scientists are unable to enumerate the possible types of signal…