Related papers: A Review on Machine Learning for Neutrino Experime…

Machine Learning algorithms are good tools for both classification and prediction purposes. These algorithms can further be used for scientific discoveries from the enormous data being collected in our era. We present ways of discovering…

The search for new interactions of neutrinos beyond those of the Standard Model may help to elucidate the mechanism responsible for neutrino masses. Here we combine existing accelerator neutrino data with restrictions coming from a recent…

The application of machine learning in sciences has seen exciting advances in recent years. As a widely applicable technique, anomaly detection has been long studied in the machine learning community. Especially, deep neural nets-based…

Precision tests of the standard model are essential for constraining models of new physics. Neutrino-electron elastic scattering offers a clean probe into many electroweak effects that are complimentary to the more canonical measurements…

This is an exciting time for folks who are looking at neutrino cross sections, and the especially important quasielastic interaction. We are able to inspect several recent results from K2K and MiniBooNE and are looking forward to a couple…

The signals from outer space and their detection have been playing an important role in particle physics, especially in discoveries of and searches for physics beyond the Standard Model (BSM); beyond the evidence of dark matter (DM), for…

Various statistical issues relevant to searches for new physics or to parameter determination in analyses of data in neutrino experiments are briefly discussed.

The application of deep learning in robotics leads to very specific problems and research questions that are typically not addressed by the computer vision and machine learning communities. In this paper we discuss a number of…

Cutting edge deep learning techniques allow for image segmentation with great speed and accuracy. However, application to problems in materials science is often difficult since these complex models may have difficultly learning physical…

This article attempts to summarize the effort by the particle physics community in addressing the tedious work of determining the parameter spaces of beyond-the-standard-model (BSM) scenarios, allowed by data. These spaces, typically…

Global analyses of particle physics data are integral for validating and scrutinizing published results of experiments. Global fits of anomalous oscillation data which search for one or more eV-scale sterile neutrinos are particularly…

Nontrivial electromagnetic properties of neutrinos are an avenue to physics beyond the Standard Model. To this end, we investigate the power of monophoton signals at neutrino experiments to probe a higher-dimensional operator connecting…

To answer the very interesting questions raised by the discovery of neutrino mass, an effective, coherent strategy is needed. To foster the development of such a strategy, the American Physical Society's Divisions of Nuclear Physics and of…

Machine learning has proven to be a valuable tool to approximate functions in high-dimensional spaces. Unfortunately, analysis of these models to extract the relevant physics is never as easy as applying machine learning to a large dataset…

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive…

An ultralight dark matter may have interesting implications in neutrino physics which have been studied actively in recent years. It is pointed out that there appears yet unexplored medium effect in neutrino transitions which occurs at the…

In the last two decades, the field of neutrino physics has made enormous progress in measuring the strength and frequency of neutrino and antineutrino oscillations. Along the way, there have been many instances of misunderstanding which led…

Machine unlearning (MU) is gaining increasing attention due to the need to remove or modify predictions made by machine learning (ML) models. While training models have become more efficient and accurate, the importance of unlearning…

Low energy neutrino processes are ideal probes for new Physics beyond the Standard Model. Cosmological observations and rare nuclear processes can test neutrino mass scales and give definite answers to unsolved basic questions like the…

Since the discovery of the Higgs boson, testing the many possible extensions to the Standard Model has become a key challenge in particle physics. This paper discusses a new method for predicting the compatibility of new physics theories…