Related papers: Neutron Yield Predictions with Artificial Neural N…
In light of the recent advancements in machine learning, we propose a novel approach to neutron source distribution estimation through the utilisation of probabilistic generative models. The estimation is based on a Monte Carlo particle…
Fast neutrons from cosmic-ray muons are an important background to underground low energy experiments. The estimate of such background is often hampered by the difficulty of measuring and calculating neutron production with sufficient…
With the ever increasing beam power at particle accelerator-based facilities for nuclear and particle physics, radioactive isotope production, and nuclear engineering, targets that can withstand this power, and shielding of secondary…
This study proposes a novel design methodology for neutron beam shutters that integrates Monte Carlo simulations (MCNP) with machine learning techniques to enhance shielding performance and accelerate the design process. The target facility…
We propose a new scheme of producing intense neutron beam whose yields exceed those of existing facilities by many orders of magnitude. This scheme uses the recently proposed photon beam extracted from circulating quantum ions, which is…
Recently, neural networks have found many applications in different fields including Genetics, Pharmacy, Astrophysics and High Energy Physics [1-3]. In the field of accelerator physics it has been used for control systems [4]. In this paper…
Contemporary ultraintense, short-pulse laser systems provide extremely compact setups for the production of high-flux neutron beams, such as those required for nondestructive probing of dense matter, research on neutron-induced damage in…
Precise modeling of detector energy response is crucial for next-generation neutrino experiments which present computational challenges due to lack of analytical likelihoods. We propose a solution using neural likelihood estimation within…
One of the main challenges in modeling massive stars to the onset of core collapse is the computational bottleneck of nucleosynthesis during advanced burning stages. The number of isotopes formed requires solving a large set of…
Scintillation and ionisation yields for nuclear recoils in liquid xenon above 10 keVnr (nuclear recoil energy) are deduced from data acquired using broadband Am-Be neutron sources. The nuclear recoil data from several exposures to two…
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…
This work demonstrates the Python package mlreflect which implements an optimized pipeline for the automized analysis of reflectometry data using machine learning. The package combines several training and data treatment techniques…
Fragments productions in spallation reactions are key infrastructure data for various applications. Based on the empirical parameterizations {\sc spacs}, a Bayesian-neural-network (BNN) approach is established to predict the fragment cross…
Accurate channel modeling is the foundation of communication system design. However, the traditional measurement-based modeling approach has increasing challenges for the scenarios with insufficient measurement data. To obtain enough data…
Charged and neutral current low energy neutrino cross section predictions from a variety of Monte Carlo generators in present use are compared against existing experimental data. Comparisons are made to experimental data on quasi-elastic,…
In the future several Spallation Source facilities will be available worldwide. Spallation Sources produce large amount of neutrinos from decay-at-rest muons and thus can be well adapted to accommodate state-of-the-art neutrino experiments.…
The spallation-neutron yield was studied experimentally by bombarding a thick lead target with 400 MeV/u carbon beam. The data were obtained with the activation analysis method using foils of Au, Mn, Al, Fe and In. The yields of produced…
A measurement is presented of the neutron production rate in lead by high energy cosmic-ray muons at a depth of 2850 m water equivalent (w.e.) and a mean muon energy of 260 GeV. The measurement exploits the delayed coincidences between…
We present a novel methodology of augmenting the scattering data measured by small angle neutron scattering via an emerging deep convolutional neural network (CNN) that is widely used in artificial intelligence (AI). Data collection time is…
The prediction of nuclear observables beyond the finite model spaces that are accessible through modern ab initio methods, such as the no-core shell model, pose a challenging task in nuclear structure theory. It requires reliable tools for…