Related papers: Particle identification with the Belle II calorime…

This paper presents a data processing algorithm with machine learning for polarization extraction and event selection applied to photoelectron track images taken with X-ray polarimeters. The method uses a convolutional neural network (CNN)…

We present an electron identification algorithm based on a neural network approach applied to the ZEUS uranium calorimeter. The study is motivated by the need to select deep inelastic, neutral current, electron proton interactions…

We design a Convolutional Neural Network (CNN) which studies correlation between discretized inverse temperature and spin configuration of 2D Ising model and show that it can find a feature of the phase transition without teaching any a…

In the context of a gas-sampling Digital Hadronic Calorimeter (DHCAL), we explore the potential of using Graph Neural Networks (GNN) for hadron energy reconstruction and Particle Identification (PID) in future collider experiments. For PID,…

This paper describes the implementation and performance of CsI(Tl) pulse shape discrimination for the Belle II electromagnetic calorimeter, representing the first application of CsI(Tl) pulse shape discrimination for particle identification…

We study the electron/pion identification performance of the ALICE Transition Radiation Detector (TRD) prototypes using a neural network (NN) algorithm. Measurements were carried out for particle momenta from 2 to 6 GeV/c. An improvement in…

The success of Convolutional Neural Networks (CNNs) in image classification has prompted efforts to study their use for classifying image data obtained in Particle Physics experiments. Here, we discuss our efforts to apply CNNs to 2D and 3D…

The Any Light Particle Search II (ALPS II) is a light shining through a wall experiment probing the existence of axions and axion-like particles using a 1064 nm laser source. While ALPS II is already taking data using a heterodyne based…

Shashlyk-type electromagnetic calorimeter (ECal) of the Multi-Purpose Detector at heavy-ion NICA collider is optimized to provide precise spatial and energy measurements for photons and electrons in the energy range from about 40 MeV to 2-3…

Convolutional neural networks (CNNs) have been widely applied in the computer vision community to solve complex problems in image recognition and analysis. We describe an application of the CNN technology to the problem of identifying…

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino…

A novel method images to estimate cosmological parameters based on images is presented. In this paper, we demonstrate the use of a convolutional neural network (CNN) for constraining the mass of dark matter particle. For this purpose, we…

Solar energy is one of the most dependable renewable energy technologies, as it is feasible almost everywhere globally. However, improving the efficiency of a solar PV system remains a significant challenge. To enhance the robustness of the…

The Belle II experiment is presently in phase-2 operation at the SuperKEKB electron-positron collider in KEK (Tsukuba, Japan). The detector is an upgrade of the Belle experiment at the KEKB collider and it is optimized for the study of rare…

This paper introduces a new methodology for extreme spatial dependence structure selection. It is based on deep learning techniques, specifically Convolutional Neural Networks -CNNs. Two schemes are considered: in the first scheme, the…

The CMS Electromagnetic Calorimeter (ECAL) is a high resolution crystal calorimeter operating at the CERN LHC. It is responsible for the identification and precise reconstruction of electrons and photons in CMS, which were crucial in the…

The performance of the Baby Barrel Electromagnetic Calorimeter (Baby BCAL) - a small-scale lead-scintillating-fiber (Pb/ScFi) prototype of the GlueX Barrel Electromagnetic Calorimeter (BCAL) - was tested in a dedicated beam campaign at the…

Position-sensitive detection of ultracold neutrons (UCNs) is demonstrated using an imaging charge-coupled device (CCD) camera. A spatial resolution less than 15 $\mu$m has been achieved, which is equivalent to an UCN energy resolution below…

Using smart wearable devices to monitor patients electrocardiogram (ECG) for real-time detection of arrhythmias can significantly improve healthcare outcomes. Convolutional neural network (CNN) based deep learning has been used successfully…

We have developed a convolutional neural network (CNN) that can make a pixel-level prediction of objects in image data recorded by a liquid argon time projection chamber (LArTPC) for the first time. We describe the network design, training…