Related papers: Extracting Signal Electron Trajectories in the COM…
An implementation of A Common Tracking Software (ACTS) toolkit for signal electron reconstruction for the COMET muon to electron conversion experiment is discussed. The COMET experiment in J-PARC, Japan, will search for neutrinoless…
We argue that the COMET experiment --- a dedicated experiment for the $\mu$-$e$ conversion search --- can be a powerful facility to search for heavy neutrinos in the mass range $1\,{\rm MeV} \lesssim M \lesssim 100\,{\rm MeV}$. The stopped…
We present a comprehensive analysis of the potential sensitivity of the Electron-Ion Collider (EIC) to charged lepton flavor violation (CLFV) in the channel $ep\to \tau X$, within the model-independent framework of the Standard Model…
The experimental search for coherent neutrinoless conversion of muon to electron in the presence of a nucleus, aims to probe the possibility of charged lepton flavour violation. The COMET experiment at J-PARC is one such setup offering…
The semiconductor quantum point contact has long been a focal point for studies of one-dimensional electron transport. Their electrical properties are typically studied using ac conductance methods, but recent work has shown that the dc…
Recent advances in scanning transmission electron and scanning probe microscopies have opened exciting opportunities in probing the materials structural parameters and various functional properties in real space with angstrom-level…
The accurate and precise extraction of information from a modern particle physics detector, such as an electromagnetic calorimeter, may be complicated and challenging. In order to overcome the difficulties we propose processing the detector…
The COMET experiment focuses on searching for the direct conversion of a muon into an electron on an aluminum nucleus without emitting a neutrino (so-called $\mu\rightarrow e$ conversion). This conversion violates the lepton flavor…
In the domain of battery research, the processing of high-resolution microscopy images is a challenging task, as it involves dealing with complex images and requires a prior understanding of the components involved. The utilization of deep…
Training wide and deep neural networks (DNNs) require large amounts of storage resources such as memory because the intermediate activation data must be saved in the memory during forward propagation and then restored for backward…
Strongly interacting electron systems can provide insight into quantum many-body phenomena, such as Mott insulating behavior and spin liquidity, facilitating semiconductor optimization. The Fermi-Hubbard model is the prototypical model used…
We investigate the feasibility of using deep learning techniques, in the form of a one-dimensional convolutional neural network (1D-CNN), for the extraction of signals from the raw waveforms produced by the individual channels of liquid…
We introduce a machine learning approach to determine the transition dynamics of silicon atoms on a single layer of carbon atoms, when stimulated by the electron beam of a scanning transmission electron microscope (STEM). Our method is…
Modern Deep Learning (DL) models have grown to sizes requiring massive clusters of specialized, high-end nodes to train. Designing such clusters to maximize both performance and utilization--to amortize their steep cost--is a challenging…
Deep learning has achieved spectacular performance in image and speech recognition and synthesis. It outperforms other machine learning algorithms in problems where large amounts of data are available. In the area of measurement technology,…
The ATLAS muon spectrometer uses drift-tube chambers for precision tracking. The performance of these chambers in the presence of magnetic field and high radiation fluxes is studied in this article using test-beam data recorded in the Gamma…
The purpose of this project is to investigate the use of charge couple devices (CCDs) to detect electrons directly. This can be done in transmission electron microscopy (TEM) for electrons over 100 KeV, but for space plasma instruments,…
Cellular Electron Cryo-Tomography (CECT) is a powerful imaging technique for the 3D visualization of cellular structure and organization at submolecular resolution. It enables analyzing the native structures of macromolecular complexes and…
Radiation damage on front-end readout and trigger electronics is an important issue in the COMET Phase-I experiment at J-PARC, which plans to search for the neutrinoless transition of a muon to an electron. To produce an intense muon beam,…
Applying deep learning to object detection provides the capability to accurately detect and classify complex objects in the real world. However, currently, few mobile applications use deep learning because such technology is…