Related papers: Fast and precise model calculation for KATRIN usin…
Recently announced results from the KATRIN collaboration imply an upper bound on the effective electron anti-neutrino mass $m_{\nu_{e}}$, $m_{\nu_{e}}< 0.8~{\rm eV}/c^{2}$. Here we explore the implications of combining the KATRIN upper…
The ATLAS experiment at the Large Hadron Collider explores the use of modern neural networks for a multi-dimensional calibration of its calorimeter signal defined by clusters of topologically connected cells (topo-clusters). The Bayesian…
ADC non-linearities are a major systematic effect in the search for keV-scale sterile neutrinos with tritium $\beta$-decay experiments like KATRIN. They can significantly distort the spectral shape and thereby obscure the tiny kink-like…
This paper puts forward a framework to accelerate Electromagnetic Transient (EMT) simulations by replacing individual components with trained Physics-Informed Neural Networks (PINNs). EMT simulations are considered the cornerstone of…
Neutron cross section matrices for fission and scattering data are required for each material, temperature, and enrichment level to calculate the neutron transport equation accurately. This information can be a limiting factor when using…
Building on earlier studies, we investigate the possibility to determine the type of neutrino mass spectrum (i.e., "the neutrino mass hierarchy") in a high statistics reactor electron antineutrino experiment with a relatively large…
Neural networks have achieved impressive breakthroughs in both industry and academia. How to effectively develop neural networks on quantum computing devices is a challenging open problem. Here, we propose a new quantum neural network model…
We present an efficient and accurate method for simulating massive neutrinos in cosmological structure formation simulations, together with an easy to use public implementation. Our method builds on our earlier implementation of the linear…
The paper reviews recent experiments on tritium beta spectroscopy searching for the absolute value of the electron neutrino mass $m(\nu_e)$. By use of dedicated electrostatic filters with high acceptance and resolution, the uncertainty on…
Quantum Recurrent Neural Networks (QRNNs) are robust candidates for modelling and predicting future values in multivariate time series. However, the effective implementation of some QRNN models is limited by the need for mid-circuit…
Neural network robustness has become a central topic in machine learning in recent years. Most training algorithms that improve the model's robustness to adversarial and common corruptions also introduce a large computational overhead,…
Deep neural networks provide flexible frameworks for learning data representations and functions relating data to other properties and are often claimed to achieve 'super-human' performance in inferring relationships between input data and…
The absolute scale of the neutrino mass plays a critical role in physics at every scale, from the particle to the cosmological. Measurements of the tritium endpoint spectrum have provided the most precise direct limit on the neutrino mass…
The simulation of power system dynamics poses a computationally expensive task. Considering the growing uncertainty of generation and demand patterns, thousands of scenarios need to be continuously assessed to ensure the safety of power…
Neutrino-nucleus cross section uncertainties are expected to be a dominant systematic in future accelerator neutrino experiments. The cross sections are determined by the linear response of the nucleus to the weak interactions of the…
Right-handed neutrinos are a natural extension of the Standard Model of particle physics. Such particles would only interact through the mixing with the left-handed neutrinos, hence they are called sterile neutrinos. If their mass were in…
Machine learning models are being used extensively in many important areas, but there is no guarantee a model will always perform well or as its developers intended. Understanding the correctness of a model is crucial to prevent potential…
The observation of neutrinoless double-beta ($0\nu\beta\beta$) decay would offer proof of lepton number violation, demonstrating that neutrinos are Majorana particles, while also helping us understand why there is more matter than…
To accelerate the inference of deep neural networks (DNNs), quantization with low-bitwidth numbers is actively researched. A prominent challenge is to quantize the DNN models into low-bitwidth numbers without significant accuracy…
Modern power systems face significant challenges in state estimation and real-time monitoring, particularly regarding response speed and accuracy under faulty conditions or cyber-attacks. This paper proposes a hybrid approach using…