Related papers: MACRIB High efficiency - high purity hadron identi…

We present studies of electron identification (eID) in the MPD experiment at NICA using machine learning techniques. The goal is to improve electron identification efficiency while preserving high purity, which is crucial for dielectron…

Charged-hadron identification (PID) is a critical requirement for the physics program of the Circular Electron-Positron Collider (CEPC). The baseline detector relies on ionization measurements from a time projection chamber (TPC), which…

Detector efficiency determination is essential to correct the measured yields and extract reliable cross sections of particles emitted in nuclear reactions. We investigate the efficiencies for measuring the full energies of light charged…

As Very Large Scale Integration (VLSI) designs continue to scale in size and complexity, layout verification has become a central challenge in modern Electronic Design Automation (EDA) workflows. In practice, congestion can only be…

We present an improved hybrid algorithm for vertexing, that combines deep learning with conventional methods. Even though the algorithm is a generic approach to vertex finding, we focus here on it's application as an alternative Primary…

Machine learning models of materials$^{1-5}$ accelerate discovery compared to ab initio methods: deep learning models now reproduce density functional theory (DFT)-calculated results at one hundred thousandths of the cost of DFT$^{6}$. To…

With the development of deep learning, Neural Network is commonly adopted to the License Plate Detection (LPD) task and achieves much better performance and precision, especially CNN-based networks can achieve state of the art RetinaNet[1].…

This paper presents the custom implementation, optimization, and performance evaluation of convolutional neural networks on field programmable gate arrays, for the purposes of accelerating deep neural network inference on large,…

The Large Hadron Collider (LHC) at the European Organisation for Nuclear Research (CERN) will be upgraded to further increase the instantaneous rate of particle collisions (luminosity) and become the High Luminosity LHC. This increase in…

Standard approaches in entity identification hard-code boundary detection and type prediction into labels (e.g., John/B-PER Smith/I-PER) and then perform Viterbi. This has two disadvantages: 1. the runtime complexity grows quadratically in…

Machine learning methods incorporating deep neural networks have been the subject of recent proposals for new hadronic resonance taggers. These methods require training on a dataset produced by an event generator where the true class labels…

Particle identification (PID) capabilities are studied by using the Time Projection Chamber (TPC) and a Time-Of-Flight (TOF) detector together at STAR. The identification capability of charged hadrons is greatly extended compared with that…

The cost involved in training deep neural networks (DNNs) on von-Neumann architectures has motivated the development of novel solutions for efficient DNN training accelerators. We propose a hybrid in-memory computing (HIC) architecture for…

The correct identification of charged hadrons plays a crucial role in flavor-physics measurements. The final detector configurations at the proposed Future Circular Collider are yet to be determined and this study aims to contribute to this…

Materials with heterogeneous structures exhibit complex physical processes such as delamination, cracks, and plasticity, which require micromechanical parameters for understanding. However, identifying these parameters is challenging due to…

Particle IDentification (PID) is a central requirement of the experiments at the future EIC. Hadron PID at high momenta by RICH techniques requires the use of low density gaseous radiators, where the challenge is the limited length of the…

A detailed study of the particle identification by the Focusing Aerogel Ring Imaging CHerenkov subsystem at the future charm superfactory detector is presented. The dedicated signal ring reconstruction algorithm is implemented in the…

Optimal symbol detection for multiple-input multiple-output (MIMO) systems is known to be an NP-hard problem. Conventional heuristic algorithms are either too complex to be practical or suffer from poor performance. Recently, several…

Sourceless efficiency calibration of high-purity germanium (HPGe) detectors can provide accurate detector-response information without experiments using radioactive calibration sources, offering advantages in both convenience and safety. In…

We derive a method to improve particle identification (PID) at high transverse momentum ($p_T$) using the relativistic rise of the ionization energy loss ($rdE/dx$) when charged particles traverse the Time Projection Chamber (TPC) at STAR.…