Particle Physics Experiments Research Articles

Learning new physics from data: A symmetrized approach

Thousands of person years have been invested in searches for new physics (NP), the majority of them motivated by theoretical considerations. Yet, no evidence of beyond the Standard Model physics has been found. This suggests that model-agnostic searches might be an important key to explore NP, and help discover unexpected phenomena which can inspire future theoretical developments. A possible strategy for such searches is identifying asymmetries between data samples that are expected to be symmetric within the Standard Model. We propose exploiting neural networks (NNs) to quickly fit and statistically test the differences between two samples. Our method is based on an earlier work, originally designed for inferring the deviations of an observed dataset from that of a much larger reference dataset. We present a symmetric formalism, generalizing the original one, avoiding fine-tuning of the NN parameters and any constraints on the relative sizes of the samples. Our formalism could be used to detect small symmetry violations, extending the discovery potential of current and future particle physics experiments. Published by the American Physical Society 2024

Testing Bell inequalities and probing quantum entanglement at a muon collider

A muon collider represents a promising candidate for the next generation of particle physics experiments after the expected end of LHC operations in the early 2040s. Rare or hard-to-detect processes at the LHC, such as the production of multiple gauge bosons, become accessible at a TeV muon collider. We present here the prospects of detecting quantum entanglement and the violation of Bell inequalities in H → ZZ → 4ℓ events at a potential future muon collider. We show that the spin density matrix of the Z boson pairs can be reconstructed using the kinematics of the charged leptons from the Z boson decays. Once the density matrix is determined, it is straightforward to obtain the expectation values of various Bell operators and test the quantum entanglement between the Z boson pair. Through a detailed study based on Monte-Carlo simulation, we show that the generalized CGLMP inequality can be maximally violated, and testing Bell inequalities could be established with high significance.

Waveform resampling with LMN method

Resampling is a common technique applied in digital signal processing. Based on the Fast Fourier Transformation (FFT), we apply an optimization called here the LMN method to achieve fast and robust re-sampling. In addition to performance comparisons with some other popular methods, we illustrate the effectiveness of this LMN method in a particle physics experiment: re-sampling of waveforms from Liquid Argon Time Projection Chambers.

Particle identification capability of a homogeneous calorimeter composed of oriented crystals

Recent studies have shown that the electromagnetic shower induced by a high-energy electron, positron or photon incident along the axis of an oriented crystal develops in a space more compact than the ordinary. On the other hand, the properties of the hadronic interactions are not affected by the lattice structure. This means that, inside an oriented crystal, the natural difference between the hadronic and the electromagnetic shower profile is strongly accentuated. Thus, a calorimeter composed of oriented crystals could be intrinsically capable of identifying more accurately the nature of the incident particles, with respect to a detector composed only of non-aligned crystals. Since no oriented calorimeter has ever been developed, this possibility remains largely unexplored and can be investigated only by means of numerical simulations. In this work, we report the first quantitative evaluation of the particle identification capability of such a calorimeter, focusing on the case of neutron-gamma discrimination. We demonstrate through Geant4 simulations that the use of oriented crystals significantly improves the performance of a Random Forest classifier trained on the detector data. This work is a proof that oriented calorimeters could be a viable option for all the environments where particle identification must be performed with a very high accuracy, such as future high-intensity particle physics experiments and satellite-based γ-ray telescopes.

Application of the VMM ASIC for SiPM-based calorimetry

Highly integrated multichannel readout electronics is crucial in contemporary particle physics experiments. A novel silicon photomultiplier readout system based on the VMM3a ASIC was developed, for the first time exploiting this chip for calorimetric purposes. To extend the dynamic range the signal from each SiPM channel was processed by two electronics channels with different gain. A fully operational prototype system with 256 SiPM readout channels allowed the collection of data from a prototype of the ALICE Forward Hadron Calorimeter (FoCal-H Prototype 2). The design and the test beam results using high energy hadron beams are presented and discussed, confirming the applicability of VMM3a-based solutions for energy measurements in a high rate environment.

Real-time portable muography with Hankuk Atmospheric-muon Wide Landscaping : HAWL

Cosmic ray muons prove valuable across various fields, from particle physics experiments to non-invasive tomography, thanks to their high flux and exceptional penetrating capability. Utilizing a scintillator detector, one can effectively study the topography of mountains situated above tunnels and underground spaces. The Hankuk Atmospheric-muon Wide Landscaping (HAWL) project successfully charts the mountainous region of eastern Korea by measuring cosmic ray muons with a detector in motion. The real-time muon flux measurement shows a tunnel length accuracy of 6.0%, with a detectable overburden range spanning from 8 to 400 meter-water-equivalent depth. This is the first real-time portable muon tomography.

Deep probabilistic direction prediction in 3D with applications to directional dark matter detectors

We present the first method to probabilistically predict 3D direction in a deep neural network model. The probabilistic predictions are modeled as a heteroscedastic von Mises-Fisher distribution on the sphere S2 , giving a simple way to quantify aleatoric uncertainty. This approach generalizes the cosine distance loss which is a special case of our loss function when the uncertainty is assumed to be uniform across samples. We develop approximations required to make the likelihood function and gradient calculations stable. The method is applied to the task of predicting the 3D directions of electrons, the most complex signal in a class of experimental particle physics detectors designed to demonstrate the particle nature of dark matter and study solar neutrinos. Using simulated Monte Carlo data, the initial direction of recoiling electrons is inferred from their tortuous trajectories, as captured by the 3D detectors. For 40 keV electrons in a 70% He 30% CO2 gas mixture at STP, the new approach achieves a mean cosine distance of 0.104 (26∘) compared to 0.556 (64∘) achieved by a non-machine learning algorithm. We show that the model is well-calibrated and accuracy can be increased further by removing samples with high predicted uncertainty. This advancement in probabilistic 3D directional learning could increase the sensitivity of directional dark matter detectors.

Novel Liquid Argon Time-Projection Chamber Readouts

Liquid argon time-projection chambers (LArTPCs) have become a prominent tool for experiments in particle physics. Recent years have yielded significant advances in the techniques used to capture the signals generated by these cryogenic detectors. This article summarizes these novel developments for detection of ionization electrons and scintillation photons in LArTPCs. New methods to capture ionization signals address the challenges of scaling traditional techniques to the large scales necessary for future experiments. Pixelated readouts improve signal fidelity and expand the applicability of LArTPCs to higher-rate environments. Methods that leverage amplification in argon enable measurements in the keV regime and below. Techniques to enhance collection of argon scintillation photons improve calorimetry and expand the physics program for very large detectors. Future efforts aim to demonstrate systems for the combined detection of both electrons and photons.

Influence of beam content composition on testbeam studies of hadron calorimeters

Abstract Hadron calorimeters are one of the most important detectors in the contemporary nuclear and particle physics experiments. Their construction and verification process takes years in designing, testing and data analysis of prototypes. Crucial step for the prototypes are tests performed by exposing the detectors to charge particle beams. Here we study the impact of the different beam content composition to one of the main properties of the hadron calorimeters – their energy resolution. We implement a test detector - Metal Hadronic Calorimeter (MACA) in GEANT4 environment and we perform different simulations to study the influence to the main energy resolution parameters.

Spatial resolution improvements with finer-pitch GEMs

Gas Electron Multipliers (GEMs) are used in many particle physics experiments, employing their `standard' configuration with amplification holes of 140 μm pitch in a hexagonal pattern. However, the collection of the charge cloud from the primary ionisation electrons from the drift region of the detector into the GEM holes affects the position information from the initial interacting particle. In this paper, the results from studies with a triple-GEM detector with an X-Y-strip readout anode are presented. It is demonstrated that GEMs with a finer hole pitch of here 90 μm improve the detector's spatial resolution. Within these studies, also the impact of the front-end electronics on the spatial resolution was investigated, which is briefly discussed in the paper.

A diffused background from axion-like particles in the microwave sky

The nature of dark matter is an unsolved cosmological problem and axions are one of the weakly interacting cold dark matter candidates. Axions or ALPs (Axion-like particles) are pseudo-scalar bosons predicted by beyond-standard model theories. The weak coupling of ALPs with photons leads to the conversion of CMB photons to ALPs in the presence of a transverse magnetic field. If they have the same mass as the effective mass of a photon in a plasma, the resonant conversion would cause a polarized spectral distortion leading to temperature fluctuations with the distortion spectrum. The probability of resonant conversion depends on the properties of the cluster such as the magnetic field, electron density, and its redshift. We show that this kind of conversion can happen in numerous unresolved galaxy clusters up to high redshifts, which will lead to a diffused polarised anisotropy signal in the microwave sky. The spectrum of the signal and its shape in the angular scale will be different from the lensed CMB polarization signal. This new polarised distortion spectrum will be correlated with the distribution of clusters in the universe and hence, with the large-scale structure. The spectrum can then be probed using its spectral and spatial variation with respect to the CMB and various foregrounds. An SNR of ~ 4.36 and ~ 93.87 are possible in the CMB-S4 145 GHz band and CMB-HD 150 GHz band respectively for a photon-ALPs coupling strength ofgaγ = 10-12GeV-1using galaxy clusters beyond redshiftz= 1. The same signal would lead to additional RMS fluctuations of ~7.5 × 10-2 μK at 145 GHz. In the absence of any signal, future CMB experiments such as Simons Observatory (SO), CMB-S4, and CMB-HD can put constraints on the coupling strength better than current bounds from particle physics experiment CERN Axion Solar Telescope (CAST).

Estimation of waveform deformation with the matched filter

In many particle physics experiments the data processing is based on the analysis of the digitized waveforms provided by the detector. While the waveform amplitude is usually correlated to the event energy, the shape may carry useful information for event discrimination. Thanks to the high signal to noise ratio they provide, matched filters are often applied. Their original design is however intended for the estimation of the waveform amplitude only. In this work we introduce an analytical extension of the original matched filter for the estimation of a possible shape deformation with respect to a reference template. The new filter is validated on simulations and, with respect to shape parameters calculated on unfiltered waveforms or derived from the original matched filter, it improves the discrimination capability by at least a factor 2 both at low and high signal to noise ratios, making it applicable to the data of several experiments.

Training toward significance with the decorrelated event classifier transformer neural network

Experimental particle physics uses machine learning for many tasks, where one application is to classify signal and background events. This classification can be used to bin an analysis region to enhance the expected significance for a mass resonance search. In natural language processing, one of the leading neural network architectures is the transformer. In this work, an event classifier transformer is proposed to bin an analysis region, in which the network is trained with special techniques. The techniques developed here can enhance the significance and reduce the correlation between the network’s output and the reconstructed mass. It is found that this trained network can perform better than boosted decision trees and feed-forward networks. Published by the American Physical Society 2024

Singlet-doublet fermion Dark Matter with Dirac neutrino mass, (g − 2)μ and ∆Neff

We study the possibility of generating light Dirac neutrino mass via scotogenic mechanism where singlet-doublet fermion Dark Matter (DM) plays non-trivial role in generating one-loop neutrino mass, anomalous magnetic moment of muon: (g − 2)μ as well as additional relativistic degrees of freedom ∆Neff within reach of cosmic microwave background (CMB) experiments. We show that the Dirac nature of neutrinos can bring interesting correlations within the parameter space satisfying the (g − 2)μ, DM relic density and the effective relativistic degrees of freedom ∆Neff. While we stick to thermal singlet-doublet DM with promising detection prospects, both thermal and non-thermal origin of ∆Neff have been explored. In addition to detection prospects of the model at DM, (g − 2)μ and other particle physics experiments, it remains verifiable at future CMB experiments like CMB-S4 and SPT-3G.

HighNESS conceptual design report: Volume II. The NNBAR experiment.

A key aim of the HighNESS project for the European Spallation Source is to enable cutting-edge particle physics experiments. This volume presents a conceptual design report for the NNBAR experiment. NNBAR would exploit a new cold lower moderator to make the first search in over thirty years for free neutrons converting to anti-neutrons. The observation of such a baryon-number-violating signature would be of fundamental significance and tackle open questions in modern physics, including the origin of the matter-antimatter asymmetry. This report shows the design of the beamline, supermirror focusing system, magnetic and radiation shielding, and anti-neutron detector necessary for the experiment. A range of simulation programs are employed to quantify the performance of the experiment and show how background can be suppressed. For a search with full background suppression, a sensitivity improvement of three orders of magnitude is expected, as compared with the previous search. Civil engineering studies for the NNBAR beamline are also shown, as is a costing model for the experiment.

A low-latency graph computer to identify metastable particles at the Large Hadron Collider for real-time analysis of potential dark matter signatures

Image recognition is a pervasive task in many information-processing environments. We present a solution to a difficult pattern recognition problem that lies at the heart of experimental particle physics. Future experiments with very high-intensity beams will produce a spray of thousands of particles in each beam-target or beam-beam collision. Recognizing the trajectories of these particles as they traverse layers of electronic sensors is a massive image recognition task that has never been accomplished in real time. We present a real-time processing solution that is implemented in a commercial field-programmable gate array using high-level synthesis. It is an unsupervised learning algorithm that uses techniques of graph computing. A prime application is the low-latency analysis of dark-matter signatures involving metastable charged particles that manifest as disappearing tracks.

A Special Issue in Memory of Masatoshi Koshiba, a Pioneer in Experimental Particle Physics and Astrophysics: II

A Special Issue in Memory of Masatoshi Koshiba, a Pioneer in Experimental Particle Physics and Astrophysics: II

Searching for lepton flavor violation with the CMS experiment

Searches for Lepton Flavor Violation (LFV) stand at the forefront of experimental particle physics research, offering a sensitive probe to many scenarios of physics beyond the Standard Model. The high proton–proton collision energy and luminosity provided by the CERN Large Hadron Collider (LHC) and the excellent CMS detector performance allow for an extensive program of LFV searches. This paper reviews a broad range of LFV searches conducted at the CMS experiment using data collected in LHC Run 2, including [Formula: see text] decays, Higgs boson decays, and top quark production and decays. In each analysis, the online and offline event selections, signal modeling, background suppression and estimation, and statistical interpretation are elucidated. These searches involve various final state particles in a large transverse momentum range, showcasing the capability of the CMS experiment in exploring fundamental questions in particle physics.

Study of residual artificial neural network for particle identification in the CEPC high-granularity calorimeter prototype

Particle Identification (PID) plays a central role in associating the energy depositions in calorimeter cells with the type of primary particle in a particle flow oriented detector system. In this paper, we propose novel PID methods based on the Residual Network (ResNet) architecture which enable the training of very deep networks, bypass the need to reconstruct feature variables, and ensure the generalization ability among various geometries of detectors, to classify electromagnetic showers and hadronic showers. Using Geant4 simulation samples with energy ranging from 5 GeV to 120 GeV, the efficacy of Residual Connections is validated and the performance of our model is compared with Boosted Decision Trees (BDT) and other pioneering Artificial Neural Network (ANN) approaches. In shower classification, we observe an improvement in background rejection over a wide range of high signal efficiency (> 95%). These findings highlight the prospects of ANN with Residual Blocks for imaging detectors in the PID task of particle physics experiments.

Experimental setup for the measurement of optical properties in the vacuum ultraviolet region

Abstract This work presents an experimental setup designed for measuring optical properties in the vacuum ultraviolet (VUV) region. Originally developed for the characterization of ICARUS photomultiplier tubes, the setup has been recently employed to measure the conversion efficiency of para-Terphenyl coatings, acting as wavelength shifters, as a function of thickness. The VUV region is of particular interest in particle physics experiments utilizing cryogenic noble gases as scintillating media. The setup includes a deuterium light source, a mirror system, an intensity monitor, and a sample chamber to host sample and photodetector. An example measurement of para-Terphenyl conversion efficiency — as wavelength shifter — with varying thickness is presented, demonstrating the versatility and effectiveness of the presented system.