Related papers: Particle Identification at VAMOS++ with Machine Le…
The large angular and momentum acceptance magnetic spectrometer VAMOS++, at GANIL, France, is frequently used for nuclear structure and reaction dynamics studies. It provides an event-by-event identification of heavy ions produced in…
Charge state recognition in quantum dot devices is important in the preparation of quantum bits for quantum information processing. Toward auto-tuning of larger-scale quantum devices, automatic charge state recognition by machine learning…
Recent progress in building large-scale quantum devices for exploring quantum computing and simulation paradigms has relied upon effective tools for achieving and maintaining good experimental parameters, i.e. tuning up devices. In many…
In this paper we propose a one-dimensional convolutional neural network (CNN)-based state of charge estimation algorithm for electric vehicles. The CNN is trained using two publicly available battery datasets. The influence of different…
We conduct a detailed exploration of charged Higgs boson masses $M_{H^{\pm}}$ within the range of $100-190~GeV$. This investigation is grounded in the benchmark points that comply with experimental constraints, allowing us to systematically…
The Particle-Identification Silicon-Telescope Array (PISTA) is a new detection system designed for high-resolution studies of the fission process induced by multi-nucleon transfer in inverse kinematics. It is specifically optimized for…
The paper describes a method of the charged particle identification, developed for the \mbox{CMD-3} detector, installed at the VEPP-2000 $e^{+}e^{-}$ collider. The method is based on the application of the boosted decision trees…
A VME-based experiment system for n-{\gamma} discrimination using the charge comparison method was established. A data acquisition program for controlling the programmable modules and processing data online via VME64X bus was developed…
Organic scintillators are important in advancing nuclear detection and particle physics experiments. Achieving a high signal-to-noise ratio necessitates efficient pulse shape discrimination techniques to accurately distinguish between…
Particle identification in large high-energy physics experiments typically relies on classifiers obtained by combining many experimental observables. Predicting the probability density function (pdf) of such classifiers in the multivariate…
We propose a fast beam orientation selection method, based on deep neural networks (DNN), capable of developing a plan comparable to those by the state-of-the-art column generation method. The novelty of Our model lies in its supervised…
In typical nuclear physics experiments with radioactive ion beams (RIBs) selected by the in-flight separation technique, Si detectors or ionization chambers are usually equipped for the charge determination of RIBs. The obtained charge…
Modern laboratory techniques like ultrafast laser excitation and shock compression can bring matter into highly nonequilibrium states with complex structural transformation, metallization and dissociation dynamics. To understand and model…
Interatomic potentials learned using machine learning methods have been successfully applied to atomistic simulations. However, accurate models require large training datasets, while generating reference calculations is computationally…
The discriminant-analysis method has been applied to optimize the exotic-beam charge recognition in a projectile fragmentation experiment. The experiment was carried out at the GSI using the fragment separator (FRS) to produce and select…
Photomultiplier tubes (PMTs) are widely used in particle experiments for photon detection. PMT waveform analysis is crucial for high-precision measurements of the position and energy of incident particles in liquid scintillator (LS)…
A deep neural network (DNN) model consisting of two hidden layers was proposed for predicting the immediate environments of specific atoms based on X-ray absorption near-edge spectra (XANES). The output layer of the DNN can be adjusted to…
In beam test experiments have been carried out for particle identification using digital pulse shape analysis in a 500~$\mu$m thick Neutron Transmutation Doped (nTD) silicon detector with an indigenously developed FPGA based 12 bit…
Interest in the influence of the neutron-to-proton (N/Z) ratio on multifragmenting nuclei has demanded an improvement in the capabilities of multi-detector arrays as well as the companion analysis methods. The particle identification method…
This work presents a machine learning approach based on support vector machines (SVMs) for quantum entanglement detection. Particularly, we focus in bipartite systems of dimensions 3x3, 4x4, and 5x5, where the positive partial transpose…