Particle Identification Methods in High Energy Physics
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Va'vra, J.↗
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Selecting XFEL single-particle snapshots by geometric machine learning
A promising new route for structural biology is single-particle imaging with an X-ray Free-Electron Laser (XFEL). This method has the advantage that the samples do not require crystallization and can be examined at room temperature. However, high-resolution structures can only be obtained from a sufficiently large number of diffraction patterns of individual molecules, so-called single particles. Here, we present a method that allows for efficient identification of single particles in very large XFEL datasets, operates at low signal levels, and is tolerant to background. This method uses supervised Geometric Machine Learning (GML) to extract low-dimensional feature vectors from a training dataset, fuse test datasets into the feature space of training datasets, and separate the data into binary distributions of “single particles” and “non-single particles.” As a proof of principle, we tested simulated and experimental datasets of the Coliphage PR772 virus. We created a training dataset and classified three types of test datasets: First, a noise-free simulated test dataset, which gave near perfect separation. Second, simulated test datasets that were modified to reflect different levels of photon counts and background noise. These modified datasets were used to quantify the predictive limits of our approach. Third, an experimental dataset collected at the Stanford Linear Accelerator Center. The single-particle identification for this experimental dataset was compared with previously published results and it was found that GML covers a wide photon-count range, outperforming other single-particle identification methods. Moreover, a major advantage of GML is its ability to retrieve single particles in the presence of structural variability.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Performance of the reconstruction and identification of high-momentum muons in proton-proton collisions at $\sqrt{s} =$ 13 TeV
The CMS detector at the LHC has recorded events from proton-proton collisions, with muon momenta reaching up to 1.8 TeV in the collected dimuon samples. These high-momentum muons allow direct access to new regimes in physics beyond the standard model. Because the physics and reconstruction of these muons are different from those of their lower-momentum counterparts, this paper presents for the first time dedicated studies of efficiencies, momentum assignment, resolution, scale, and showering of very high momentum muons produced at the LHC. These studies are performed using the 2016 and 2017 data sets of proton-proton collisions at &surd;s = 13 TeV with integrated luminosities of 36.3 and 42.1 fb−1, respectively.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
A feature point identification method for positron emission particle tracking with multiple tracers
Not provided.
Instruments & Instrumentation↗
Performance of missing transverse momentum reconstruction in proton-proton collisions at √ s = 13 TeV using the CMS detector
The performance of missing transverse momentum (p → T miss ) reconstruction algorithms for the CMS experiment is presented, using proton-proton collisions at a center-of-mass energy of 13 TeV, collected at the CERN LHC in 2016. The data sample corresponds to an integrated luminosity of 35.9 fb -1 . The results include measurements of the scale and resolution of p → T miss , and detailed studies of events identified with anomalous p → T miss . The performance is presented of a p → T miss reconstruction algorithm that mitigates the effects of multiple proton-proton interactions, using the "pileup per particle identification" method. The performance is shown of an algorithm used to estimate the compatibility of the reconstructed p → T miss with the hypothesis that it originates from resolution effects.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
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 ↗
Tools for estimating fake/non-prompt lepton backgrounds with the ATLAS detector at the LHC
Measurements and searches performed with the ATLAS detector at the CERN LHC often involve signatures with one or more prompt leptons. Such analyses are subject to 'fake/non-prompt' lepton backgrounds, where either a hadron or a lepton from a hadron decay or an electron from a photon conversion satisfies the prompt-lepton selection criteria. These backgrounds often arise within a hadronic jet because of particle decays in the showering process, particle misidentification or particle interactions with the detector material. As it is challenging to model these processes with high accuracy in simulation, their estimation typically uses data-driven methods. Three methods for carrying out this estimation are described, along with their implementation in ATLAS and their performance.
47 OTHER INSTRUMENTATION↗
Muon identification using multivariate techniques in the CMS experiment in proton-proton collisions at sqrt(s) = 13 TeV
The identification of prompt and isolated muons, as well asmuons from heavy-flavour hadron decays, is an important task. Wedeveloped two multivariate techniques to provide highly efficientidentification for muons with transverse momentum greater than10 GeV. One provides a continuous variable as an alternative to acut-based identification selection and offers a betterdiscrimination power against misidentified muons. The other oneselects prompt and isolated muons by using isolation requirements toreduce the contamination from nonprompt muons arising inheavy-flavour hadron decays. Both algorithms are developed using59.7 fb$^{-1}$ of proton-proton collisions data at a centre-of-massenergy of √(s)=13 TeV collected in 2018 with the CMSexperiment at the CERN LHC.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
The sweeper spectrometer for neutron invariant-mass spectroscopy at FRIB
Neutron invariant-mass spectroscopy (NIMS) is a key technique for studying unbound and weakly bound nuclei at the limits of stability. At the Facility for Rare Isotope Beams (FRIB), such measurements are performed using the Sweeper spectrometer, a large-gap, high-rigidity dipole system coupled to the MoNA-LISA neutron detector arrays. To meet the demands imposed by higher beam energies (>130 MeV/u) and the broad cocktail-beam selection available at FRIB, the spectrometer has recently been upgraded to improve particle-identification and detection performance. Upstream of the reaction target, a plastic scintillator with Silicon photomultiplier (SiPM) readout provides the global trigger and time reference, two parallel plate avalanche counters (PPACs) track the trajectories of incoming beam particles, and a silicon PIN detector measures the energy loss, ΔE, for charge (Z) identification. After the Sweeper magnet, the trajectories of the reaction products are tracked by two micro-pattern drift chambers (MPDCs), their charge (Z) is identified by a Frisch-grid ionization chamber (FG-IC), and their mass-to-charge ratio (A/Q) is deduced by time-of-flight measurement using a fast plastic scintillator read out by an array of photomultiplier tubes (PMTs). The detection system also incorporates the Modular Neutron Array (MoNA) for neutron detection and the CAESium-iodide scintillator ARray (CAESAR) for high-efficiency γ-ray measurements to enable full kinematic reconstruction. Performance was evaluated using a cocktail beam around 37 Al accelerated at E ≈ 130 MeV/u during the first FRIB campaign, demonstrating the readiness of the upgraded system for future studies of nuclei at and beyond the neutron drip line.
Particle identification methods↗
Selection of ν μ charged–current induced interactions with $N \gt 0$ protons and performance of events with $N=2$ protons in the final state in the MicroBooNE detector from the BNB
Using the MicroBooNE liquid argon time projection chamber (LArTPC) at Fermilab’s Booster Neutrino Beam, we examine samples of charged current events from neutrino scattering on argon with exactly two or with any number of protons in final states with no other hadrons except neutrons. We described the particle identification method employed for protons, and we compare measured kinematic distributions of muons and protons to predictions from several Monte Carlo event generators.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
First Measurement of Charged Current Muon Neutrino-Induced Kaon Production on Argon
The Micro Booster Neutrino Experiment (MicroBooNE) is a liquid argon time projection chamber (LArTPC) neutrino detector with an 85-ton active volume, located on-axis to the Booster Neutrino Beamline (BNB) at Fermilab. The detector is exposed to a neutrino flux with average energy of 800 MeV and was designed to study neutrino interactions on argon, particularly in the 1 GeV energy range. Among its main goals is the investigation of neutrino-induced strange particle production at final-state, with a specific interest on charged kaons ($K^{+}$). The understanding of this neutrino interaction is crucial for refining background models in future nucleon decay searches on others LArTPCs experiments such as the Deep Underground Neutrino Experiment (DUNE). Additionally, the techniques developed for the identification of neutrino-induced $K^{+}$ will contribute to improving particle identification methods in LArTPC-based detectors. This thesis is reporting the first-ever measurement of the flux-integrated cross section for charged-current muon neutrino-induced $K^{+}$ production on argon, determined to be $7.93 \pm 3.27(\text{stat.}) \pm 2.92(\text{syst.}) \times 10^{-42}$ cm$^2$/nucleon, based on the MicroBooNE dataset corresponding to $6.88 \times 10^{20}$ Protons on Target (POT).
Rodriguez Rondon, Jairo H. [South Dakota Sch. Mine↗
CsI(Tl) pulse shape discrimination with the Belle II electromagnetic calorimeter as a novel method to improve particle identification at electron–positron colliders
Here we describe 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 at an electron–positron collider. The pulse shape characterization algorithms applied by the Belle II calorimeter are described. Control samples of $γ, μ^+, π^±, K^±$ and $ρ/ \bar{ρ}$ are used to demonstrate the significant insight into the secondary particle composition of calorimeter clusters that is provided by CsI(Tl) pulse shape discrimination. Comparisons with simulation are presented and provide further validation for newly developed CsI(Tl) scintillation response simulation techniques, which when incorporated with GEANT4 simulations allow the particle dependent scintillation response of CsI(Tl) to be modelled. Comparisons between data and simulation also demonstrate that pulse shape discrimination can be a new tool to identify sources of improvement in the simulation of hadronic interactions in materials. The $K^0_L$ efficiency and photon-as-hadron fake-rate of a multivariate classifier that is trained to use pulse shape discrimination is presented and comparisons are made to a shower-shape based approach. CsI(Tl) pulse shape discrimination is shown to reduce the photon-as-hadron fake-rate by over a factor of 3 at photon energies of 0.2 GeV and over a factor 10 at photon energies of 1 GeV.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
A CUPID Li2100MoO4 scintillating bolometer tested in the CROSS underground facility
A scintillating bolometer based on a large cubic Li$_{2}$$^{100}$MoO$_4$ crystal (45 mm side) and a Ge wafer (scintillation detector) has been operated in the CROSS cryogenic facility at the Canfranc underground laboratory in Spain. The dual-readout detector is a prototype of the technology that will be used in the next-generation $0\nu2\beta$ experiment CUPID. The measurements were performed at 18 and 12 mK temperature in a pulse tube dilution refrigerator. This setup utilizes the same technology as the CUORE cryostat that will host CUPID and so represents an accurate estimation of the expected performance. The Li$_{2}$$^{100}$MoO$_4$ bolometer shows a high energy resolution of 6 keV FWHM at the 2615 keV $\gamma$ line. The detection of scintillation light for each event triggered by the Li$_{2}$$^{100}$MoO$_4$ bolometer allowed for a full separation ($\sim$8$\sigma$) between $\gamma$($\beta$) and $\alpha$ events above 2 MeV. The Li$_{2}$$^{100}$MoO$_4$ crystal also shows a high internal radiopurity with $^{228}$Th and $^{226}$Ra activities of less than 3 and 8 $\mu$Bq/kg, respectively. Taking also into account the advantage of a more compact and massive detector array, which can be made of cubic-shaped crystals (compared to the cylindrical ones), this test demonstrates the great potential of cubic Li$_{2}$$^{100}$MoO$_4$ scintillating bolometers for high-sensitivity searches for the $^{100}$Mo $0\nu2\beta$ decay in CROSS and CUPID projects.
47 OTHER INSTRUMENTATION↗
Evaluation of the response to electrons and pions in the scintillating fiber and lead calorimeter for the future electron-ion collider
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 Fermilab Test Beam Facility (FTBF). This study provides a benchmark for the Pb/ScFi component of the future Barrel Imaging Calorimeter (BIC) in the ePIC detector at the Electron-Ion Collider (EIC). The detector response to electrons and pions was studied at beam energies between 4 and 10 GeV, extending previous GlueX tests to a higher energy regime. The calibrated detector exhibits good linearity within uncertainties, and its electron energy resolution meets EIC requirements. The data further constrain the constant term in the energy resolution to below 1.9%, improving upon previous constraints at lower energies. Simulations reproduce key features of the electron and pion data within the limitations of the collected dataset and the FTBF test environment. Electron-pion separation in the test beam setup was analyzed using multiple methods, incorporating varying degrees of beam-related effects. The inclusion of longitudinal shower profile information enhanced the separation performance, underscoring its relevance for the full-scale BIC in ePIC. These results provide essential benchmarks for the Pb/ScFi section of the future BIC, validating detector simulations and guiding optimization strategies for electron-pion discrimination.
47 OTHER INSTRUMENTATION↗
Identification of tau leptons using a convolutional neural network with domain adaptation
A tau lepton identification algorithm,DeepTau, based on convolutional neural network techniques, has been developed in the CMS experiment to discriminate reconstructed hadronic decays of tau leptons (τ h ) from quark or gluon jets and electrons and muons that are misreconstructed as τ h candidates. The latest version of this algorithm, v2.5, includes domain adaptation by backpropagation, a technique that reduces discrepancies between collision data and simulation in the region with the highest purity of genuine τh candidates. Additionally, a refined training workflow improves classification performance with respect to the previous version of the algorithm, with a reduction of 30–50% in the probability for quark and gluon jets to be misidentified as τ h candidates for given reconstruction and identification efficiencies. This paper presents the novel improvements introduced in theDeepTau algorithm and evaluates its performance in LHC proton-proton collision data at √(s) = 13 and 13.6 TeV collected in 2018 and 2022 with integrated luminosities of 60 and 35 fb -1 , respectively. Techniques to calibrate the performance of the τ h identification algorithm in simulation with respect to its measured performance in real data are presented, together with a subset of results among those measured for use in CMS physics analyses.
Large detector-systems performance↗
Normalizing flows for domain adaptation when identifying Λ hyperon events
Here this study focuses on the application of a normalizing flow as a method of domain adaptation when classifying physics data. Normalizing flows offer a way to transform data points between two different distributions. The present study investigates a novel method of transforming latent representations of physics data to a normal distribution and then to a physics distribution again. The final distribution models a simulated distribution. After being transformed, the data can be classified by a neural network trained on labeled simulation data. The present study succeeds in training two normalizing flows that can transform between data (or simulation) and a Gaussian distribution.
47 OTHER INSTRUMENTATION↗
Particle-flow reconstruction and global event description with the CMS detector
The CMS apparatus was identified, a few years before the start of the LHC operation at CERN, to feature properties well suited to particle-flow (PF) reconstruction: a highly-segmented tracker, a fine-grained electromagnetic calorimeter, a hermetic hadron calorimeter, a strong magnetic field, and an excellent muon spectrometer. A fully-fledged PF reconstruction algorithm tuned to the CMS detector was therefore developed and has been consistently used in physics analyses for the first time at a hadron collider. For each collision, the comprehensive list of final-state particles identified and reconstructed by the algorithm provides a global event description that leads to unprecedented CMS performance for jet and hadronic τ decay reconstruction, missing transverse momentum determination, and electron and muon identification. This approach also allows particles from pileup interactions to be identified and enables efficient pileup mitigation methods. The data collected by CMS at a centre-of-mass energy of 8\TeV show excellent agreement with the simulation and confirm the superior PF performance at least up to an average of 20 pileup interactions.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Simulations and Data analysis for the 35 ton Liquid Argon detector as a prototype for the DUNE experiment
The Deep Underground Neutrino Experiment (DUNE) is a next-generation neutrino experiment which will be built at the Sanford Underground Research Facility (SURF), and will receive a wide-band neutrino beam from Fermilab, 1300~km away. At this baseline DUNE will be able to study many of the properties of neutrino mixing, including the neutrino mass hierarchy and the value of the CP-violating complex phase ($\delta_{CP}$). DUNE will utilise Liquid Argon (LAr) Time Projection Chamber (TPC) (LArTPC) technology, and the Far Detector (FD) will consist of four modules, each containing 17.1~kt of LAr with a fiducial mass of around 10~kt. Each of these FD modules represents around an order of magnitude increase in size, when compared to existing LArTPC experiments. \\ The 35 ton detector is the first DUNE prototype for the single (LAr) phase design of the FD. There were two running periods, one from November 2013 to February 2014, and a second from November 2015 to March 2016. During t he second running period, a system of TPCs was installed, and cosmic-ray data were collected. A method of particle identification was developed using simulations, though this was not applied to the data due to the higher than expected noise level. A new method of determining the interaction time of a track, using the effects of longitudinal diffusion, was developed using the cosmic-ray data. A camera system was also installed in the detector for monitoring purposes, and to look for high voltage breakdowns. \\ Simulations concerning the muon-induced background rate to nucleon decay are performed, following the incorporation of the MUon Simulations UNderground (MUSUN) generator into the DUNE software framework. A series of cuts which are based on Monte Carlo truth information is developed, designed to reject simulated background events, whilst preserving simulated signal events in the $n \rightarrow K^{+} + e^{-}$ decay channel. No background events are seen to survive the app lication of these cuts in a sample of 2~$\times$~10$^9$ muon! s, representing 401.6~years of detector live time. This corresponds to an annual background rate of <~0.44~events$\cdot$Mt$^{-1}\cdot$year$^{-1}$ at 90\% confidence, using a fiducial mass of 13.8~kt.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗