Related papers: Dark Matter-induced electron excitations in silico…
Deep kernel learning aims at designing nonlinear combinations of multiple standard elementary kernels by training deep networks. This scheme has proven to be effective, but intractable when handling large-scale datasets especially when the…
Deep neural networks have received considerable attention in clinical imaging, particularly with respect to the reduction of radiation risk. Lowering the radiation dose by reducing the photon flux inevitably results in the degradation of…
As dark matter (DM) direct detection experiments continue to improve their sensitivity they will inevitably encounter an irreducible background arising from coherent neutrino scattering. This so-called "neutrino floor" may significantly…
Dual-energy computed tomography (DECT) enables material-specific imaging through acquisitions at two different X-ray energy spectra. Material decomposition from DECT data is an ill-posed inverse problem that is highly sensitive to noise…
The electron ionization predicted by the Migdal effect in dark matter-nucleus scattering enhances experimental sensitivity to sub-GeV dark matter. In this work, we demonstrate that lower-energy electron excitations provide a novel and…
Modeling and simulating a power distribution network (PDN) for printed circuit boards (PCBs) with irregular board shapes and multi-layer stackup is computationally inefficient using full-wave simulations. This paper presents a new concept…
In Deep Neural Networks (DNN) and Spiking Neural Networks (SNN), the information of a neuron is computed based on the sum of the amplitudes (weights) of the electrical potentials received in input from other neurons. We propose here a new…
Deep neural networks (DNNs) are successful in many computer vision tasks. However, the most accurate DNNs require millions of parameters and operations, making them energy, computation and memory intensive. This impedes the deployment of…
We examine the signals produced by dark matter interactions with electrons, which play a crucial role in direct detection experiments employing heavy target materials, particularly in many well-motivated sub-GeV dark matter scenarios. When…
We perform a systematic study of the electric and magnetic dipole moments of dark matter (DM) that are induced at the one-loop level when DM experiences four-fermion interactions with Standard Model (SM) charged fermions. Related to their…
A deep neural network (DNN) has been developed to generate the distributions of nuclear charge density, utilizing the training data from the relativistic density functional theory and incorporating available experimental charge radii of…
As the search space for light dark matter (DM) has shifted to sub-GeV DM candidate particles, increasing attention has turned to solid state detectors built from quantum materials. While traditional solid state detector targets (e.g. Si or…
Deep neural networks (DNNs) are reshaping the field of information processing. With their exponential growth challenging existing electronic hardware, optical neural networks (ONNs) are emerging to process DNN tasks in the optical domain…
Partial Differential Equations (PDEs) are used to model a variety of dynamical systems in science and engineering. Recent advances in deep learning have enabled us to solve them in a higher dimension by addressing the curse of…
Predicting physical response of an artificially structured material is of particular interest for scientific and engineering applications. Here we use deep learning to predict optical response of artificially engineered nanophotonic…
Dark matter plays a crucial role in our comprehension of the universe, but its mysterious nature poses challenges for direct detection. A primary obstacle in detecting dark matter is distinguishing genuine signals from the prevailing…
We consider the absorption by bound electrons of dark matter in the form of dark photons and axion-like particles, as well as of dark photons from the Sun, in current and next-generation direct detection experiments. Experiments sensitive…
The current work reports an efficient deep neural network (DNN) accelerator where synaptic weight elements are controlled by ferroelectric domain dynamics. An integrated device-to-algorithm framework for benchmarking novel synaptic devices…
We observe that sub-GeV Dark Matter (DM) induces Casimir-Polder forces between nucleons, that can be accessed by experiments from nuclear to molecular scales. We calculate the nucleon-nucleon potentials arising in the DM effective theory…
Two-dimensional (2D) materials have attracted extensive attention due to their unique characteristics and application potentials. Raman spectroscopy, as a rapid and non-destructive probe, exhibits distinct features and holds notable…