Related papers: Geant4Reweight: a framework for evaluating and pro…
Neutrino cross section and oscillation measurements depend critically on modeling of hadronic final state interactions (FSI). Often, this is one of the largest components of uncertainty in a measurement. This is because of the difficulty in…
Networks are powerful tools for modeling interactions in complex systems. While traditional networks use scalar edge weights, many real-world systems involve multidimensional interactions. For example, in social networks, individuals often…
Total-body positron emission tomography (PET) imaging has the potential to transform medical care of a number of diseases and augment our knowledge of systems biology. Various detector designs and geometries are currently under development…
In this work, we investigate the implications of a novel non-standard interaction (NSI) of neutrinos. This interaction is geometric in origin -- it arises because the propagation of fermions in curved spacetime induces torsion. This torsion…
We derive new constraints on effective four-fermion neutrino non-standard interactions with both quarks and electrons. This is done through the global analysis of neutrino oscillation data and measurements of coherent elastic…
The Recurrent Neural Networks and their variants have shown promising performances in sequence modeling tasks such as Natural Language Processing. These models, however, turn out to be impractical and difficult to train when exposed to very…
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…
Primordial Heavy neutrinos of 4th generation might explain different astrophysical puzzles: indeed the simplest 4th neutrino scenario may be still consistent with known 4th neutrino physics, cosmic ray anti-matter and gamma fluxes and…
A scheme is presented for accurately propagating the gravitational field constraints in finite difference implementations of numerical relativity. The method is based on similar techniques used in astrophysical magnetohydrodynamics and…
The development of a package for the management of physics data is described: its design, implementation and computational benchmarks. This package improves the data management tools originally developed for Geant4 physics models based on…
We present GERN, a novel scalable framework for training GNNs in node classification tasks, based on effective resistance, a standard tool in spectral graph theory. Our method progressively refines the GNN weights on a sequence of random…
Surface simulations are important for accurately modeling particle interactions in experiments where background contributions from surface contaminants can significantly affect detector performance. In rare event searches, such as dark…
This paper presents nonlinear tracking control systems for a quadrotor unmanned aerial vehicle under the influence of uncertainties. Assuming that there exist unstructured disturbances in the translational dynamics and the attitude…
Structural reliability evaluation for composites constitutes a fundamentally high-dimensional multiscale problem, as microscale material uncertainties must propagate to the macroscale and can be quantified as high-dimensional random fields.…
Upcoming experiments need improved simulations of neutrino scattering. This work uses the popular GENIE event generator to test a variety of neutrino interaction models against recent MicroBooNE measurements of pionless charged-current…
This work aims to combine machine learning and control approaches for legged robots, and developed a hybrid framework to achieve new capabilities of balancing against external perturbations. The framework embeds a kernel which is a fully…
Theoretical interpretations of particle physics data, such as the determination of the Wilson coefficients of the Standard Model Effective Field Theory (SMEFT), often involve the inference of multiple parameters from a global dataset.…
In many learning problems, the training and testing data follow different distributions and a particularly common situation is the \textit{covariate shift}. To correct for sampling biases, most approaches, including the popular kernel mean…
We present a conceptually simple, flexible and effective framework for weight generating networks. Our approach is general that unifies two current distinct and extremely effective SENet and CondConv into the same framework on weight space.…
This paper develops a renormalized perturbation theory framework for nonlinear structure formation in a broad class of modified gravity models that exhibit Vainshtein screening, with a focus on a viable subclass of Horndeski theories. We…