相关论文: Electron/pion separation with an Emulsion Cloud Ch…

We introduce a first-ever algorithm for the reconstruction of multiple showers from the data collected with electromagnetic (EM) sampling calorimeters. Such detectors are widely used in High Energy Physics to measure the energy and…

I present an application of a convolutional neural network (CNN) to separate muons and pions in the Belle II electromagnetic calorimeter (ECL). The ECL is designed to measure the energy deposited by charged and neutral particles. It also…

Beams of Carbon nuclei are used or planned to be used in various centers for cancer treatment around the world because of their therapeutic advantages over proton beams. The knowledge of the fragmentation of Carbon nuclei when they interact…

We investigate photon--pion discrimination in regimes where electromagnetic showers overlap at the scale of calorimeter granularity. Using full detector simulations with fine-grained calorimeter segmentation of approximately…

Pions constitute nearly $70\%$ of final state particles in ultra high energy collisions. They act as a probe to understand the statistical properties of Quantum Chromodynamics (QCD) matter i.e. Quark Gluon Plasma (QGP) created in such…

By exploiting structural differences between electromagnetic and hadronic showers in a multivariate analysis we present an efficient Electron-Hadron discrimination algorithm for liquid argon time projection chambers, validated using Geant4…

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…

We present a quantitative assessment of the anticipated impact of future Electron-Ion Collider (EIC) measurements on the extraction of parton-to-pion fragmentation functions (FFs). Our analysis combines simulated semi-inclusive…

The kaon identification is crucial for the flavor physics, and also benefits the flavor and charge reconstruction of the jets. We explore the particle identification capability for tracks with momenta ranging from 2-20 GeV/c using the…

Single-cell prototype drift chambers were built at TRIUMF and tested with a $\sim\unit[210]{MeV/c}$ beam of positrons, muons, and pions. A cluster-counting technique is implemented which improves the ability to distinguish muons and pions…

The OPERA experiment aims at measuring the \nu_{\mu} -> \nu_{\tau} oscillation through the \nu_{\tau} appearance in an almost pure \nu_{\mu} beam (CNGS). For the direct identification of the short-lived {\tau} lepton, produced in \nu_{\tau}…

Machine learning (ML) is no new concept in the high-energy physics community, in fact, many ML techniques have been employed since the early 80s to deal with a broad spectrum of physics problems. In this paper, we present a novel technique…

Measurement of the ultra-rare $K^+\to\pi^+\nu\bar\nu$ decay at the NA62 experiment at CERN requires high-performance particle identification to distinguish muons from pions. Calorimetric identification currently in use, based on a boosted…

We present a comprehensive analysis of electronic recoil vs. nuclear recoil discrimination in liquid/gas xenon time projection chambers, using calibration data from the 2013 and 2014-16 runs of the Large Underground Xenon (LUX) experiment.…

This paper describes a new way to reconstruct and identify muons with high efficiency and high pion rejection. Since muons at the ILC are often produced with or in jets, for many of the physics channels of interest[1], an efficient…

One of the key design choices of any sampling calorimeter is how fine to make the longitudinal and transverse segmentation. To inform this choice, we study the impact of calorimeter segmentation on energy reconstruction. To ensure that the…

A chemical discrimination system based on photonic reservoir computing is demonstrated experimentally for the first time. The system is inspired by the way humans perceive and process visual sensory information. The electro-optical…

Robust point cloud classification is crucial for real-world applications, as consumer-type 3D sensors often yield partial and noisy data, degraded by various artifacts. In this work we propose a general ensemble framework, based on partial…

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…

A conceptual scheme of a hybrid-emulsion spectrometer for investigating various channels of neutrino oscillations is proposed. The design emphasizes detection of $\tau$ leptons by detached vertices, reliable identification of electrons, and…