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[Background] A quantitative understanding of neutrino interactions with nuclei is needed for precision era neutrino long baseline experiments (MINOS, NOvA, DUNE) which all use nuclear targets. Pion production is the dominant reaction…
This is a methodological guide to the use of deep neural networks in the processing of pulsed dipolar spectroscopy (PDS) data encountered in structural biology, organic photovoltaics, photosynthesis research, and other domains featuring…
Motivated by learning from experience and exploiting existing knowledge in civil nuclear operations, we have developed in-house generic PSA models for pressurized and boiling water reactors. The models are computationally light, handy,…
There have been suggestions to measure atomic parity nonconservation (PNC) along an isotopic chain, by taking ratios of observables in order to cancel complicated atomic structure effects. Precise atomic PNC measurements could make a…
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…
Present contribution represents a significant improvement of our previous calculation of Maxwellian-averaged cross sections and astrophysical reaction rates. Addition of newly-evaluated neutron reaction libraries, such as ROSFOND and…
The input data pipeline is an essential component of each machine learning (ML) training job. It is responsible for reading massive amounts of training data, processing batches of samples using complex transformations, and loading them onto…
Rigorous error estimation is a fundamental topic in numerical analysis. With the increasing use of physics-informed neural networks (PINNs) for solving partial differential equations, several approaches have been developed to quantify the…
Neutrino\antineutrino deep inelastic scattering (DIS) data provide useful constrains for the flavor decomposition in global fits of parton distribution functions (PDF). The smallness of the cross-sections requires the use of nuclear targets…
Nuclear masses play a crucial role in both nuclear physics and astrophysics, driving sustained efforts toward their precise experimental determination and reliable theoretical prediction. In this work, we compile the newly measured masses…
Cross-project defect prediction (CPDP) plays an important role in estimating the most likely defect-prone software components, especially for new or inactive projects. To the best of our knowledge, few prior studies provide explicit…
Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino…
By operating an antineutrino detector of simple design during several fuel cycles, we have observed long term changes in antineutrino flux that result from the isotopic evolution of a commercial Pressurized Water Reactor (PWR). Measurements…
Data-intensive science is increasingly reliant on real-time processing capabilities and machine learning workflows, in order to filter and analyze the extreme volumes of data being collected. This is especially true at the energy and…
We introduce a global analysis of collinearly factorized nuclear parton distribution functions (PDFs) including, for the first time, data constraints from LHC proton-lead collisions. In comparison to our previous analysis, EPS09, where data…
We present a model-independent determination of the nuclear parton distribution functions (nPDFs) using machine learning methods and Monte Carlo techniques based on the NNPDF framework. The neutral-current deep-inelastic nuclear structure…
It is demonstrated that the inherent structure of Boris pusher may result in the accumulation of errors in numerical integration when estimating the works of electric fields on particles. This, in turn, leads to an incorrect estimations of…