Related papers: Constraining nuclear effects in Argon using machin…
We study the effects of non-standard interactions on the oscillation pattern of atmospheric neutrinos. We use neutrino oscillograms as our main tool to infer the role of non-standard interactions (NSI) parameters at the probability level in…
Measuring the sum of the three active neutrino masses, $M_\nu$, is one of the most important challenges in modern cosmology. Massive neutrinos imprint characteristic signatures on several cosmological observables in particular on the…
Training deep neural networks using simulations typically requires very large numbers of simulated events. This can be a large computational burden and a limitation in the performance of the deep learning algorithm when insufficient numbers…
A precise description of neutrino-nucleus reactions will play a key role in addressing fundamental questions such as the leptonic CP violation and the neutrino mass hierarchy through analyzing data from next-generation neutrino oscillation…
The measurement of neutrino oscillations and exotic physics searches are important parts of the physics program in the near future, with new state-of-the-art experiments planned within the next decade. Future and modern experiments in these…
The final results of the MACRO experiment on atmospheric neutrino oscillations are presented and discussed. The data concern different event topologies with average neutrino energies of ~3 and ~50 GeV. Multiple Coulomb Scattering of the…
We discuss the accuracy of the usual procedure for neutrino energy reconstruction which is based on the quasielastic kinematics. Our results are described in terms of a probability distribution for a real neutrino energy value. Several…
The mass-induced neutrino oscillation is a well established phenomenon that is based on the unitary mixing among three light active neutrinos. Remarkable precision on neutrino mixing parameters over the last decade or so has opened up the…
Heavy Neutral Leptons (HNLs) with masses $\mathcal{O}(0.1 - 1\,\,\text{GeV}/c^{2})$ are promising candidates for the simultaneous explanation of the smallness of the observed neutrino masses as well as the matter-antimatter asymmetry in the…
Traditionally, spline or kernel approaches in combination with parametric estimation are used to infer the linear coefficient (fixed effects) in a partially linear mixed-effects model for repeated measurements. Using machine learning…
The final results of the MACRO experiment on atmospheric neutrino oscillations are presented. The data concern different event topologies with average neutrino energies of ~ 3 and ~ 50 GeV. Multiple Coulomb Scattering of the high energy…
A next-generation neutrino oscillation experiment using reactor neutrinos could give important information on the size of mixing angle $\theta_{13}$. The motivation and goals for a new reactor measurement are discussed in the context of…
High-quality charged current quasielastic scattering data have recently been reported for both muon neutrinos and antineutrinos from several accelerator-based neutrino experiments. Measurements from MiniBooNE were the first to indicate that…
We consider the problem of determination of the neutrino mass ordering via precise study of the vacuum neutrino oscillations in the JUNO and other future medium baseline reactor neutrino experiments. We are proposing to resolve neutrino…
We perform a joint analysis of current data from cosmology and laboratory experiments to constrain the neutrino mass parameters in the framework of bayesian statistics, also accounting for uncertainties in nuclear modeling, relevant for…
One endeavour of modern physical chemistry is to use bottom-up approaches to design materials and drugs with desired properties. Here we introduce an atomistic structure learning algorithm (ASLA) that utilizes a convolutional neural network…
In this document we will review the current status of reactor neutrino oscillation experiments and present their physics potentials for measuring the $\theta_{13}$ neutrino mixing angle. The neutrino mixing angle $\theta_{13}$ is currently…
A deep convolutional neural network (CNN) is developed to study symmetry energy $E_{\rm sym}(\rho)$ effects by learning the mapping between the symmetry energy and the two-dimensional (transverse momentum and rapidity) distributions of…
In this work we discuss the impact of nuisance parameters on the effectiveness of machine learning in high-energy physics problems, and provide a review of techniques that allow to include their effect and reduce their impact in the search…
One of the largest sources of systematic uncertainties in ongoing neutrino-oscillation measurements is the description of nuclear effects. Its considerable reduction is expected thanks to the dedicated studies of (anti)neutrino-nucleus…