Shower Particles Research Articles

Reconstructing non-repeating radio pulses with Information Field Theory

Particle showers in dielectric media produce radio signals which are used for the detection of both ultra-high energy cosmic rays and neutrinos with energies above a few PeV. The amplitude, polarization, and spectrum of these short, broadband radio pulses allow us to draw conclusions about the primary particles that caused them, as well as the mechanics of shower development and radio emission. However, confidently reconstructing the radio signals can pose a challenge, as they are often obscured by background noise. Information Field Theory offers a robust approach to this challenge by using Bayesian inference to calculate the most likely radio signal, given the recorded data. In this paper, we describe the application of Information Field Theory to radio signals from particle showers in both air and ice and demonstrate how accurately pulse parameters can be obtained from noisy data.

Backward shower particle production in high energy nucleus–nucleus collisions — an outlook to centrality dependence

A detailed study on the centrality dependence of the different aspects multiplicity distribution of the shower particles produced in the backward direction ([Formula: see text] in nuclear emulsion track detector has been carried out for [Formula: see text]Ne-emulsion interactions at 4.1 AGeV/c. Depending on the values of the total charges or sum of the charges of noninteracting projectile fragments, event samples were classified into four centrality classes. From the analysis presented in this paper, qualitative information about the assembly of shower particles in the backward direction has been extracted.

Performance of the ISS-CREAM calorimeter in a calibration beam test

The Cosmic Ray Energetics And Mass experiment for the International Space Station (ISS-CREAM) was installed on the ISS to measure high-energy cosmic-ray elemental spectra for the charge range Z=1 to 26. The ISS-CREAM instrument includes a tungsten scintillating-fiber calorimeter preceded by a carbon target for energy measurements. The carbon target induces hadronic interactions, and showers of secondary particles develop in the calorimeter. The energy deposition in the calorimeter is proportional to the particle energy.As a predecessor to ISS-CREAM, the balloon-borne CREAM instrument was successfully flown seven times over Antarctica for a cumulative exposure of 191 days. The CREAM calorimeter demonstrated its capability to measure energies of cosmic-ray particles, and the ISS-CREAM calorimeter is expected to have a similar performance. Before the launch, an engineering-unit calorimeter was shipped to CERN for calibration and performance tests. This beam test included position, energy, and angle scans of electron and pion beams together with a high-voltage scan for calibration and characterization. Additionally, an attenuation effect in the scintillating fibers was studied. In this paper, beam test results, including corrections for the attenuation effect, are presented.

Detection of a particle shower at the Glashow resonance with IceCube.

The Glashow resonance describes the resonant formation of a W- boson during the interaction of a high-energy electron antineutrino with an electron1, peaking at an antineutrino energy of 6.3petaelectronvolts (PeV) in the rest frame of the electron. Whereas this energy scale is out of reach for currently operating and future planned particle accelerators, natural astrophysical phenomena are expected to produce antineutrinos with energies beyond the PeV scale. Here we report the detection by the IceCube neutrino observatory of a cascade of high-energy particles (a particle shower) consistent with being created at the Glashow resonance. A shower with an energy of 6.05±0.72PeV (determined from Cherenkov radiation in the Antarctic Ice Sheet) was measured. Features consistent with the production of secondary muons in the particle shower indicate the hadronic decay of a resonant W- boson, confirm that the source is astrophysical and provide improved directional localization. The evidence of the Glashow resonance suggests the presence of electron antineutrinos in the astrophysical flux, while also providing further validation of the standard model of particle physics. Its unique signature indicates a method of distinguishing neutrinos from antineutrinos, thus providing a way to identify astronomical accelerators that produce neutrinos via hadronuclear or photohadronic interactions, with or without strong magnetic fields. As such, knowledge of both the flavour (that is, electron, muon or tau neutrinos) and charge (neutrino or antineutrino) will facilitate the advancement of neutrino astronomy.

Towards an all-orders calculation of the electroweak bubble wall velocity

We analyze Higgs condensate bubble expansion during a first-orderelectroweak phase transition in the early Universe. The interaction ofparticles with the bubble wall can be accompanied by the emission ofmultiple soft gauge bosons. When computed at fixed order inperturbation theory, this process exhibits large logarithmicenhancements which must be resummed to all orders when the wallvelocity is large. We perform this resummation both analytically andnumerically at leading logarithmic accuracy. The numerical simulationis achieved by means of a particle shower in the broken phase of theelectroweak theory. The two approaches agree to the 10% level. Forfast-moving walls, we find the scaling of the thermal pressure exertedagainst the wall to be P~γ2T4, independent of theparticle masses, implying a significantly slower terminal velocitythan previously suggested.

Deep learning to classify ultra-high-energy cosmic rays by means of PMT signals

One of the most captivating problems being faced nowadays in Physics are ultra-high energy cosmic rays. They are high-energy radiations coming mainly from outside the Solar System, and when they enter Earth’s atmosphere, they produce a cascade of particles. This cascade of particles, named as extensive air shower, can be recorded by means of photomultiplier tubes in surface detectors, obtaining different recordings of the energy signal (since the air shower can hit one or more detectors). Based on these signals, different features can be obtained that might give an insight into which particle has caused the extensive air shower, which is of utmost importance for physicists. Therefore, this work presents a supervised learning algorithm to determine that the particle is a photon or a hadron. Convolutional neural networks and feed forward neural networks are compared in order to analyze the importance of spatial information for the classification. Then, a comparison between using the information of each surface detector separately and integrating the information from them for the classification is studied, showing that the integration improves the results compared to using each surface detector’s trace independently. Furthermore, a comparison between manually extracted features from the signal and the automatically extracted features by the convolutional neural network is done, showing the classification potential of the latter. Finally, the addition of particle shower features which are external to the surface detector measurements is assessed, showing that the combination of automatically extracted features and external variables is able to predict the particle that has produced the air shower with an accuracy of 98.87%.

Qualification tests of 997 8-inch photomultiplier tubes for the water Cherenkov detector array of the LHAASO experiment

The Large High-Altitude Air Shower Observatory (LHAASO) is being built at Haizi Mountain, Sichuan province of China at an altitude of 4410 m. One of its main goals is to survey the northern sky for very-high-energy gamma ray sources via its ground-based water Cherenkov detector array (WCDA). 900 8-inch photomultiplier tubes (PMTs) CR365-02-1 from Beijing Hamamatsu Photon Techniques INC. (BHP) are installed in the WCDA, collecting Cherenkov photons produced by air shower particles crossing water. The design of the PMT base with a high dynamic range for CR365-02-1, the PMT batch test system, and the test results of 997 PMTs are presented in this paper.

Dissipative phenomena in QCD plasma state created in heavy ion collisions

Modern heavy ion experiments reach collision energies that span the QCD phase diagram in the region of a partonic plasma state called the quark-gluon plasma (QGP). This relativistic QCD plasma is a strongly coupled hot/dense medium that lasts only a few fm/c. Being such a ephemeral state of matter, the only effective information about it is contained in the final particle shower collected by the experiment. Plethora of measurements show that the QGP behaves like a relativistic liquid with very small dissipative effects. Phenomenologically, a full theoretical framework of the QGP comprises of initial medium conditions, viscous hydrodynamics and particle shower (hadronization) phase. Each of these phases determines the predictive power of the model and puts a direct quantitative constrain on dissipative contributions when compared to data.

Searches for neutrinos from cosmic-ray interactions in the Sun using seven years of IceCube data

Cosmic-ray interactions with the solar atmosphere are expected to produce particle showers which in turn produce neutrinos from weak decays of mesons. These solar atmospheric neutrinos (SAνs) have never been observed experimentally. A detection would be an important step in understanding cosmic-ray propagation in the inner solar system and the dynamics of solar magnetic fields. SAνs also represent an irreducible background to solar dark matter searches and a detection would allow precise characterization of this background. Here, we present the first experimental search based on seven years of data collected from May 2010 to May 2017 in the austral winter with the IceCube Neutrino Observatory. An unbinned likelihood analysis is performed for events reconstructed within 5 degrees of the center of the Sun. No evidence for a SAν flux is observed. After inclusion of systematic uncertainties, we set a 90% upper limit of 1.02+0.20−0.18·10−13 GeV−1cm−2s−1 at 1 TeV.

Muon–Electron Pulse Shape Discrimination for Water Cherenkov Detectors Based on FPGA/SoC

The distinction of secondary particles in extensive air showers, specifically muons and electrons, is one of the requirements to perform a good measurement of the composition of primary cosmic rays. We describe two methods for pulse shape detection and discrimination of muons and electrons implemented on FPGA. One uses an artificial neural network (ANN) algorithm; the other exploits a correlation approach based on finite impulse response (FIR) filters. The novel hls4ml package is used to build the ANN inference model. Both methods were implemented and tested on Xilinx FPGA System on Chip (SoC) devices: ZU9EG Zynq UltraScale+ and ZC7Z020 Zynq. The data set used for the analysis was captured with a data acquisition system on an experimental site based on a water Cherenkov detector. A comparison of the accuracy of the detection, resources utilization and power consumption of both methods is presented. The results show an overall accuracy on particle discrimination of 96.62% for the ANN and 92.50% for the FIR-based correlation, with execution times of 848 ns and 752 ns, respectively.

GPU-Accelerated Machine Learning Inference as a Service for Computing in Neutrino Experiments.

Machine learning algorithms are becoming increasingly prevalent and performant in the reconstruction of events in accelerator-based neutrino experiments. These sophisticated algorithms can be computationally expensive. At the same time, the data volumes of such experiments are rapidly increasing. The demand to process billions of neutrino events with many machine learning algorithm inferences creates a computing challenge. We explore a computing model in which heterogeneous computing with GPU coprocessors is made available as a web service. The coprocessors can be efficiently and elastically deployed to provide the right amount of computing for a given processing task. With our approach, Services for Optimized Network Inference on Coprocessors (SONIC), we integrate GPU acceleration specifically for the ProtoDUNE-SP reconstruction chain without disrupting the native computing workflow. With our integrated framework, we accelerate the most time-consuming task, track and particle shower hit identification, by a factor of 17. This results in a factor of 2.7 reduction in the total processing time when compared with CPU-only production. For this particular task, only 1 GPU is required for every 68 CPU threads, providing a cost-effective solution.

Graph Neural Network for Object Reconstruction in Liquid Argon Time Projection Chambers

This paper presents a graph neural network (GNN) technique for low-level reconstruction of neutrino interactions in a Liquid Argon Time Projection Chamber (LArTPC). GNNs are still a relatively novel technique, and have shown great promise for similar reconstruction tasks in the LHC. In this paper, a multihead attention message passing network is used to classify the relationship between detector hits by labelling graph edges, determining whether hits were produced by the same underlying particle, and if so, the particle type. The trained model is 84% accurate overall, and performs best on the EM shower and muon track classes. The model’s strengths and weaknesses are discussed, and plans for developing this technique further are summarised.

Preliminary analysis of neutron time-delay histograms from Changvan latitude surveys

The name “Changvan” is given to a transportable neutron monitor, modernized by researchers in Thailand and housed inside a standard-size shipping container. It contains three neutron-sensitive proportional counters, which contain enriched 10BF3 gas, to detect cosmic ray showers. Primary cosmic rays are high-energy particles arriving from outer space. By colliding with atoms in the atmosphere, they produce a shower of sub-atomic particles, called secondary particles. Some particles can reach the Earth’s surface, including neutrons. The Changvan is designed to measure atmospheric neutrons during a latitude survey. The side counter tubes are ringed with lead producer to produce evaporation neutrons when secondary particles collide with it. The center counter lacks the lead producer but can receive neutrons from nearby counters. The reflector and moderator made from polyethylene thermalize the evaporation neutrons and protect against disturbance by external low-energy neutrons. In this work, we pursue methods to determine spectral changes in Galactic cosmic rays using data from the Changvan, avoiding the systematic inconsistencies of cross-station comparisons. We examine neutron time delay histograms from the three counter tubes, as recorded by specially designed readout electronics, where the time delay refers to the time interval between consecutive neutron detections in one counter. We perform a preliminary analysis of such histograms to measure the leader fraction (L) of neutrons that do not follow a previous neutron from the same primary cosmic ray. The electronic dead time will be considered in the L calculation.

Fast and Accurate Electromagnetic and Hadronic Showers from Generative Models

Generative machine learning models offer a promising way to efficiently amplify classical Monte Carlo generators’ statistics for event simulation and generation in particle physics. Given the already high computational cost of simulation and the expected increase in data in the high-precision era of the LHC and at future colliders, such fast surrogate simulators are urgently needed. This contribution presents a status update on simulating particle showers in high granularity calorimeters for future colliders. Building on prior work using Generative Adversarial Networks (GANs), Wasserstein-GANs, and the information-theoretically motivated Bounded Information Bottleneck Autoencoder (BIB-AE), we further improve the fidelity of generated photon showers. The key to this improvement is a detailed understanding and optimisation of the latent space. The richer structure of hadronic showers compared to electromagnetic ones makes their precise modeling an important yet challenging problem. We present initial progress towards accurately simulating the core of hadronic showers in a highly granular scintillator calorimeter.

Multi-particle reconstruction in the High Granularity Calorimeter using object condensation and graph neural networks

The high-luminosity upgrade of the LHC will come with unprecedented physics and computing challenges. One of these challenges is the accurate reconstruction of particles in events with up to 200 simultaneous protonproton interactions. The planned CMS High Granularity Calorimeter offers fine spatial resolution for this purpose, with more than 6 million channels, but also poses unique challenges to reconstruction algorithms aiming to reconstruct individual particle showers. In this contribution, we propose an end-to-end machine-learning method that performs clustering, classification, and energy and position regression in one step while staying within memory and computational constraints. We employ GravNet, a graph neural network, and an object condensation loss function to achieve this task. Additionally, we propose a method to relate truth showers to reconstructed showers by maximising the energy weighted intersection over union using maximal weight matching. Our results show the efficiency of our method and highlight a promising research direction to be investigated further.

Detection of a Particle Shower at the Glashow Resonance with IceCube

Detection of a Particle Shower at the Glashow Resonance with IceCube

Properties of a Simple е/γ Detector Consisting of a Lead Convertor and a Hodoscope

The results of the calculations of coordinate resolution and hadron rejection factor for a simple e/gamma detector consisting of a lead convertor followed by a hodoscope are presented. For the simulation of showers, initiated in the converter by electrons and hadrons with energies upto 1 TeV GEANT4 is used. It is shown that the best coordinate resolution for electrons is achieved when the converter thickness is closed to the shower maximum. For example, at 200 GeV with 2 mm strip width hodoscope it is equal to sigma=89 microns provided a "truncated mean" coordinate estimation is used. The optimal thickness of the converter for hadron rejection is also close to tmax. For 200 GeV beam of electrons and protons the rejection factor of 10^-4 for 0.9 electron detection efficiency can be reached using only data on charged particles multiplicities. Information on the spatial distribution of the shower particles after the converter allows to enhance further the rejection by several times.

Investigation of the Features of Etched Tracks of Superheavy Nuclei in Olivines from Meteorites

In this study, we investigate etched channels of a special shape that arise during the search for superheavy nuclei in olivines from meteorites. The possibility of the appearance of tracks of this shape due to electrons penetrating olivine at a distance of several microns from the track is considered. The calculation results related to the spatio-temporal development of an electron shower during the passage of a heavy ion through olivine are presented. It has been shown that it is not possible to explain the observed effect only by the presence of δ electrons. In addition, the correspondence of a number of assumptions of some models of ion passage through matter to the obtained characteristics of δ electrons is discussed.

Radio-detection of neutrino-induced air showers: The influence of topography

Neutrinos of astrophysical origin could be detected through the electromagnetic radiation of the particle showers induced in the atmosphere by their interaction in the Earth. This applies in particular for tau neutrinos of energies E>1016eV following Earth-skimming trajectories. The ∼1° beaming of the radio emission in the forward direction however implies that the radio signal will likely fly above a detector deployed over a flat site and would therefore not be detected.We study here how a non-flat detector topography can improve the detection probability of these neutrino-induced air showers. We do this by computing with three distinct tools the neutrino detection efficiency for a radio array deployed over a toy-model mountainous terrain, also taking into account experimental and topographic constraints. We show in particular that ground topographies inclined by few degrees only induce detection efficiencies typically three times larger than those obtained for flat areas for favorable trajectories. We conclude that the topography of the area where the detector is deployed will be a key factor for an experiment like GRAND.

Signatures of secondary leptons in radio-neutrino detectors in ice

The detection of the radio emission following a neutrino interaction in ice is a promising technique to obtain significant sensitivities to neutrinos with energies above PeV. The detectable radio emission stems from particle showers in the ice. So far, detector simulations have considered only the radio emission from the primary interaction of the neutrino. For this study, existing simulation tools have been extended to cover secondary interactions from muons and taus. We find that secondary interactions of both leptons add up to 25\% to the effective volume of neutrino detectors. Also, muon and tau neutrinos can create several detectable showers, with the result that double signatures do not constitute an exclusive signature for tau neutrinos. We also find that the background of atmospheric muons from cosmic rays is non-negligible for in-ice arrays and that an air shower veto should be considered helpful for radio detectors.