Particle Identification Methods in High Energy Physics
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Va'vra, J.↗
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Consistent Principles for Particle Identification by PSD Systems
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Nuclear science and engineering, Mathematics and C↗
Development of ML FPGA filter for particle identification and tracking in real time
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Furletov, Sergey↗
Archive of AGR-5/6/7 Particle Radiographs for Identification of Particles with Defective IPyC
As a part of fuel quality control characterization, 2D radiographs of large numbers of particles produced by the Advanced Gas Reactor Fuel Development and Qualification (AGR) Program’s AGR-5/6/7 irradiation were acquired. These radiographs were used for the identification of particles with excessive uranium dispersion from the kernel into the surrounding buffer layer caused by chlorine infiltration through a defective inner pyrolytic carbon (IPyC) layer during silicon carbide (SiC) deposition. Additional features of interest associated with fabrication anomalies were also catalogued. Raw radiography images, along with the noted defective IPyC defects found by analysis at Oak Ridge National Laboratory are reported herein.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Context-Enriched Identification of Particles with a Convolutional Network for Neutrino Events
Particle detectors record the interactions of subatomic particles and their passage through matter. The identification of these particles is necessary for in-depth physics analysis. While particles can be identified by their individual behavior as they travel through matter, the full context of the interaction in which they are produced can aid the classification task substantially. We have developed the first convolutional neural network for particle identification which uses context information. This is also the first implementation of a four-tower siamese-type architecture both for separation of independent inputs and inclusion of context information. The network classifies clusters of energy deposits from the NOvA neutrino detectors as electrons, muons, photons, pions, and protons with an overall efficiency and purity of 83.3% and 83.5%, respectively. We show that providing the network with context information improves performance by comparing our results with a network trained without context information.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
The forward TPC system of the NA61/SHINE experiment at CERN: a tandem TPC concept
This paper presents the Forward Time Projection Chamber (FTPC) system of the NA61/SHINE experiment at the CERN SPS accelerator. This TPC system applies a novel tandem-TPC design to reduce the background originating from particle tracks not synchronous with the event trigger. The FTPC system is composed of three chambers with alternating drift field directions. The chambers were installed directly along the beamline region of the NA61/SHINE detector in a medium- to high-intensity (10-100 kHz) hadron or ion beam. The tandem TPC system has proved to be capable of rejecting out-of-time background tracks not associated with a primary interaction. In addition, the system performs tracking and inclusive dE/dx particle identification for particles at and near the beam momentum. This shows that a tandem-TPC-based chamber design may be used also in other experimental applications with a demand for low material budget, tracking capability, and the need for dE/dx particle identification, all while in the presence of a relatively high particle flux.
47 OTHER INSTRUMENTATION↗
Image segmentation for uranium isotopic analysis by SIMS: Combined adaptive thresholding and marker controlled watershed approach
A novel approach to particle identification and particle isotope ratio determination has been developed for nuclear safeguard applications. This particle search approach combines an adaptive thresholding algorithm and marker-controlled watershed segmentation (MCWS) transform, which improves the secondary ion mass spectrometry (SIMS) isotopic analysis of uranium containing particle populations for nuclear safeguards applications. The Niblack assisted MCWS approach (a.k.a. SEEKER) developed for this work has improved the identification of isotopically unique uranium particles under conditions that have historically presented significant challenges for SIMS image data processing techniques. Particles obtained from five NIST uranium certified reference materials (CRM U129A, U015, U150, U500 and U850) were successfully identified in regions of SIMS image data 1) where a high variability in image intensity existed, 2) where particles were touching or were in close proximity to one another and/or 3) where the magnitude of ion signal for a given region was count limited. Analysis of the isotopic distributions of uranium containing particles identified by SEEKER showed four distinct, accurately identified 235U enrichment distributions, corresponding to the NIST certified 235U/238U isotope ratios for CRM U129A/U015 (not statistically differentiated), U150, U500 and U850. Additionally, comparison of the minor uranium isotope (234U, 235U and 236U) atom percent values verified that, even in the absence of high precision isotope ratio measurements, SEEKER could be used to segment isotopically unique uranium particles from SIMS image data. Although demonstrated specifically for SIMS analysis of uranium containing particles for nuclear safeguards, SEEKER has application in addressing a broad set of image processing challenges.
adaptive thresholding↗
Ice particle type identification for shallow Arctic mixed-phase clouds using X-band polarimetric radar
Not provided.
Meteorology & Atmospheric Sciences↗
Nuclear particle track identification in inorganic solids
Nuclear particle track identification in inorganic dielectric glasses
Fleischer, R. L.↗
Performance of a Quintuple-GEM Based RICHDetector Prototype
Cerenkov technology is often the optimal choice for particle identification in high energy particle collision applications. Typically, the most challenging regime is at high pseudorapidity (forward) where particle identification must perform well at high laboratory momenta. For the upcoming electron ion collider (EIC), the physics goals require hadron (π, K, p) identification up to ~50 GeV/c. In this region Cerenkov ring-imaging (RICH) is the most viable solution. The speed of light in a radiator medium is inversely proportional to the refractive index. Hence, for particle identification (PID) reaching out to high momenta a small index of refraction is required. Unfortunately, the lowest indices of refraction also result in the lowest light yield ([(dNγ)/dx] ∝ sin2(θC)) driving up the radiator length and thereby the overall detector cost. In this paper we report on a successful test of a compact RICH detector (1 meter radiator) capable of delivering in excess of 10 photoelectrons per ring with a low index radiator gas (CF4). The detector concept is a natural extension of the PHENIX hadron-blind detector (HBD) achieved by adding focusing capability at low wavelength and adequate gain for high efficiency detection of single-electron induced avalanches. Our results indicate that this technology is indeed a viable choice in the forward direction of the EIC. The setup and results are described within.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Using machine learning for particle track identification in the CLAS12 detector
Particle track reconstruction is the most computationally intensive process in nuclear physics experiments. Traditional algorithms use a combinatorial approach that exhaustively tests track measurements ("hits") to identify those that form an actual particle trajectory. In this article, we describe the development of four machine learning (ML) models that assist the tracking algorithm by identifying valid track candidates from the measurements in drift chambers. Several types of machine learning models were tested, including: Convolutional Neural Networks (CNN), Multi-Layer Perceptrons (MLP), Extremely Randomized Trees (ERT) and Recurrent Neural Networks (RNN). As a result of this work, an MLP network classifier was implemented as part of the CLAS12 reconstruction software to provide the tracking code with recommended track candidates. The resulting software achieved accuracy of greater than 99% and resulted in an end-to-end speedup of 35% compared to existing algorithms.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Direct reaction experimental studies with beams of radioactive tin ions
The tin chain of isotopes provides a unique region in which to investigate the evolution of single-particle structure, spreading from N = 50 at {sup 100}Sn, through 10 stable isotopes and the N = 82 shell closure at {sup 132}Sn out into the r-process path. Direct reactions performed on radioactive ion beams are sensitive spectroscopic tools for studying exotic nuclei. Here we present one experiment knocking out neutrons from tin isotopes that are already neutron deficient and two reactions that add a neutron to neutron-rich {sup 130}Sn. Both techniques rely on selective particle identification and the measurement of γ rays in coincidence with charged ions. We present the goals of the two experiments and the particle identification for the channels of interest. The final results will be presented in future publications.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Calorimetric classification of track-like signatures in liquid argon TPCs using MicroBooNE data
The MicroBooNE liquid argon time projection chamber located at Fermilab is a neutrino experiment dedicated to the study of short-baseline oscillations, the measurements of neutrino cross sections in liquid argon, and to the research and development of this novel detector technology. Accurate and precise measurements of calorimetry are essential to the event reconstruction and are achieved by leveraging the TPC to measure deposited energy per unit length along the particle trajectory, with mm resolution. We describe the non-uniform calorimetric reconstruction performance in the detector, showing dependence on the angle of the particle trajectory. Such non-uniform reconstruction directly affects the performance of the particle identification algorithms which infer particle type from calorimetric measurements. This work presents a new particle identification method which accounts for and effectively addresses such non-uniformity. The newly developed method shows improved performance compared to previous algorithms, illustrated by a 93.7% proton selection efficiency and a 10% muon mis-identification rate, with a fairly loose selection of tracks performed on beam data. The performance is further demonstrated by identifying exclusive final states in ν μ CC interactions. While developed using MicroBooNE data and simulation, this method is easily applicable to future LArTPC experiments, such as SBND, ICARUS, and DUNE.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Main Injector Particle Production Experiment (MIPP) at Fermilab
The Main Injector Particle Production Experiment at Fermilab uses particle beams of charged pions, kaons, proton and anti-proton with beam momenta of 5 to 90 GeV/c and thin targets spanning the periodic table from (liquid) hydrogen to uranium to measure particle production cross sections in a full acceptance spectrometer with charged particle identification for particles from 0.1 to 120 GeV/c using Time Projection Chamber, Time of Flight, multicell Cherenkov, and Ring Imaging Cherenkov detectors and Calorimeter for neutrons. Particle production using 120 GeV/c protons from Main Injector on the MINOS target was also measured. We describe the physics motivation to perform such cross section measurements and highlight the impact of hadronic interaction data on neutrino physics. Recent results on forward neutron cross sections and analysis of MINOS target data are also presented.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Main Injector Particle Production Experiment (MIPP) at Fermilab
The Main Injector Particle Production Experiment at Fermilab uses particle beams of charged pions, kaons, proton and anti-proton with beam momenta of 5 to 90 GeV/c and thin targets spanning the periodic table from (liquid) hydrogen to uranium to measure particle production cross sections in a full acceptance spectrometer with charged particle identification for particles from 0.1 to 120 GeV/c using Time Projection Chamber, Time of Flight, multicell Cherenkov, and Ring Imaging Cherenkov detectors and Calorimeter for neutrons. Particle production using 120 GeV/c protons from Main Injector on the MINOS target was also measured. We describe the physics motivation to perform such cross section measurements and highlight the impact of hadronic interaction data on neutrino physics. Recent results on forward neutron cross sections and analysis of MINOS target data are also presented.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Pileup mitigation at CMS in 13 TeV data
With increasing instantaneous luminosity at the LHC come additional reconstruction challenges. At high luminosity, many collisions occur simultaneously within one proton-proton bunch crossing. The isolation of an interesting collision from the additional “pileup” collisions is needed for effective physics performance. In the CMS Collaboration, several techniques capable of mitigating the impact of these pileup collisions have been developed. Such methods include charged-hadron subtraction, pileup jet identification, isospin-based neutral particle “δβ” correction, and, most recently, pileup per particle identification. This paper surveys the performance of these techniques for jet and missing transverse momentum reconstruction, as well as muon isolation. The analysis makes use of data corresponding to 35.9 fb−1 collected with the CMS experiment in 2016 at a center-of-mass energy of 13 TeV. The performance of each algorithm is discussed for up to 70 simultaneous collisions per bunch crossing. Significant improvements are found in the identification of pileup jets, the jet energy, mass, and angular resolution, missing transverse momentum resolution, and muon isolation when using pileup per particle identification.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Electron/hadron separation with the TRD based on GaAs pixel detectors
Transition Radiation Detectors (TRDs) are widely used for particle identification in both high-energy physics and astroparticle physics. They are typically equipped with gaseous detectors. The main limitation of these types of detectors is that TR photons and ionization losses cannot be decoupled, which significantly reduces the particle separation power. Recent advancements in the development of pixel detectors based on GaAs sensors offer a unique opportunity to effectively detect TR photons and separate them from ionization losses. Such detectors represent novel devices that combine precise tracking capabilities with particle identification (PID) properties. The present work is dedicated to an experimental study of particle identification properties of the TRD prototype based on 500 μ m-thick GaAs sensor bonded to a Timepix3 chip. Studies were performed at the CERN SPS and it was shown that at a particle momentum of 20 GeV/c, the probability of misidentifying a hadron as an electron is below 10 −2 for an electron detection efficiency of 98%–99%, and it is 1 . 6⋅10 −4 for electron detection efficiency of 90%. This performance surpasses the electron/hadron rejection power of all known TRDs by an order of magnitude for the same detector length.
GaAs detectors↗
Identification of long-lived charged particles using time-of-flight systems at the upgraded LHC detectors
We study the impact of picosecond precision timing detection systems on the LHC experiments' long-lived particle search program during the HL-LHC era. We develop algorithms that allow us to reconstruct the mass of such charged particles and perform particle identification using the time-of-flight measurement. We investigate the reach for benchmark scenarios as a function of the timing resolution, and find sensitivity improvement of up to a factor of ten, depending on the new heavy particle mass.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗