Liquid Argon Time Projection Chamber Research Articles

Highly-parallelized simulation of a pixelated LArTPC on a GPU

The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on 10^3 pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype.

Scintillation light detection performance for the DUNE ND-LAr 2 × 2 modules

The Deep Underground Neutrino Experiment (DUNE) will be using a liquid argon time projection chamber (LAr TPC) with optically separated modules in the Near Detector (ND) complex. A prototype experiment, DUNE ND-LAr 2 × 2, is composed of four test modules. They detect ionization charge through a pixel-based readout and scintillation light through fibers in light collection modules and light traps called ArCLights. The light detection performance for two modules of DUNE ND-LAr 2 × 2 that took cosmic ray data at the University of Bern is shown. We present further the role of the 2 × 2 prototype in DUNE and how it is used to demonstrate the reconstruction capabilities of its light detectors in terms of energy thresholds and timing resolution.

Search for Dark-Matter-Nucleon Interactions via Migdal Effect with DarkSide-50.

Dark matter elastic scattering off nuclei can result in the excitation and ionization of the recoiling atom through the so-called Migdal effect. The energy deposition from the ionization electron adds to the energy deposited by the recoiling nuclear system and allows for the detection of interactions of sub-GeV/c^{2} mass dark matter. We present new constraints for sub-GeV/c^{2} dark matter using the dual-phase liquid argon time projection chamber of the DarkSide-50 experiment with an exposure of (12 306±184) kg d. The analysis is based on the ionization signal alone and significantly enhances the sensitivity of DarkSide-50, enabling sensitivity to dark matter with masses down to 40 MeV/c^{2}. Furthermore, it sets the most stringent upper limit on the spin independent dark matter nucleon cross section for masses below 3.6 GeV/c^{2}.

Evaluation of Portable Programming Models to Accelerate LArTPC Detector Simulations

The Liquid Argon Time Projection Chamber (LArTPC) technology is widely used in high energy physics experiments, including the upcoming Deep Underground Neutrino Experiment (DUNE). Accurately simulating LArTPC detector responses is essential for analysis algorithm development and physics model interpretations. Accurate LArTPC detector response simulations are computationally demanding, and can become a bottleneck in the analysis workflow. Compute devices such as General-Purpose Graphics Processing Units (GPGPUs) have the potential to substantially accelerate simulations compared to traditional CPU-only processing. The software development for these compute accelerators often carries the cost of specialized code refactorization and porting to match the target hardware architecture. With the rapid evolution and increased diversity of the computer architecture landscape, it is highly desirable to have a portable solution that also maintains reasonable performance. We report our ongoing effort in evaluating Kokkos as a basis for this portable programming model using LArTPC simulations in the context of the Wire-Cell Toolkit, a C++ library for LArTPC simulations, data analysis, reconstruction and visualization.

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

The Exa.TrkX project 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 Large Hadron Collider (LHC). Graphs describing particle interactions are formed by treating each detector hit as a node, with edges describing the relationships between hits. We utilise a multi-head attention message passing network which performs graph convolutions in order to label each node with a particle type.We present an updated variant of our GNN architecture, with several improvements. After testing the model on more realistic simulation with regions of unresponsive wires, the target was modified from edge classification to node classification in order to increase robustness. Removing edges as a classification target opens up a broader possibility space for edge-forming techniques; we explore the model’s performance across a variety of approaches, such as Delaunay triangulation, kNN, and radius-based methods. We also extend this model to the 3D context, sharing information between detector views. By using reconstructed 3D spacepoints to map detector hits from each wire plane, the model naively constructs 2D representations that are independent yet fully consistent.

Light detection with power and signal transmission over fiber

The Deep Underground Neutrino Experiment is a next generation long baseline (1300 km) neutrino oscillation experiment. The neutrino beam measurements will be performed by a near detector and far detector. The far detector will consist of four modules, installed 1.5 km deep underground, based on Liquid Argon Time Projection Chamber (LArTPC) technology to detect particles. The Vertical Drift (VD) LArTPC is a recent technology proposed by the DUNE collaboration for the second FD module. In VD, light collection will be optimized by embedding photon detectors within the LArTPC cathode, which is biased at −300 kV. As a result, power must arrive to the Photon Detection System and signal must be transmitted via non-conductive material. The proposed solution is to use Power-over-Fiber and Signal-over-Fiber. An intense validation of the system is being performed by the collaboration at the CERN Neutrino Platform. The design of the system and the results of the validation collected over the first half of 2022 are presented here.

The light detection system of the ICARUS detector in the Short Baseline Neutrino program at Fermilab

The Short Baseline Neutrino (SBN) program at Fermilab is an extensive experimental programme aiming at searching for sterile neutrino(s) [1], whose existence is one of the fundamental open questions of neutrino physics. It employs three Liquid Argon Time Projection Chamber (LArTPC) detectors, called ICARUS, MicroBooNE and SBND, sampling the Booster Neutrino Beam (BNB) at different locations from its target. The SBN detectors, working near the Earth’s surface, are subjected to a substantial cosmic background, which can mimic genuine neutrino interactions. Thus, it is essential to distinguish the signals related to the neutrino beams from those induced by the cosmic rays. The light detection system is a vital part of LArTPCs, but its role is even more critical for surface-operating detectors like ICARUS. The ICARUS light detection system is based on 360 Photomultiplier Tubes (PMTs). Its main role is to provide an efficient trigger and contribute to the 3D reconstruction of detected events. The light detection system calibration and further trigger system improvements were performed for the final detector configuration during the detector commissioning at Fermilab. The trigger system effectively exploits the information given by the PMTs. To further increase that system’s efficiency in event filtering, an alternative method based on Convolutional Neural Network (CNN) has been developed. Simulation-based results show that this technique can reduce the cosmic background by up to 76% with a neutrino selection efficiency of 99%. However, to filter the real data cases, which are usually not identical to the simulated ones, this method was improved by applying Domain Adversarial Neural Network (DANN). The results prove that adversarial training through a DANN can alleviate the simulation bias, demonstrating the first successful application of DANN for CNN as an event classifier for a LArTPC.

A First Application of Collaborative Learning In Particle Physics

Over the last ten years, the popularity of Machine Learning (ML) has grown exponentially in all scientific fields, including particle physics. The industry has also developed new powerful tools that, imported into academia, could revolutionise research. One recent industry development that has not yet come to the attention of the particle physics community is Collaborative Learning (CL), a framework that allows training the same ML model with different datasets. This work explores the potential of CL, testing the library Colearn with neutrino physics simulation. Colearn, developed by the British Cambridge-based firm Fetch.AI, enables decentralised machine learning tasks. Being a blockchain-mediated CL system, it allows multiple stakeholders to build a shared ML model without needing to rely on a central authority. A generic Liquid Argon Time-Projection Chamber (LArTPC) has been simulated and images produced by fictitious neutrino interactions have been used to produce several datasets. These datasets, called learners, participated successfully in training a Deep Learning (DL) Keras model using blockchain technologies in a decentralised way. This test explores the feasibility of training a single ML model using different simulation datasets coming from different research groups. In this work, we also discuss a framework that instead makes different ML models compete against each other on the same dataset. The final goal is then to train the most performant ML model across the entire scientific community for a given experiment, either using all of the datasets available or selecting the model which performs best among every model developed in the community.

The electronic set-up for the scintillation light detection system of ICARUS-SBN at Fermilab

The ICARUS-T600 Liquid Argon (LAr) Time Projection Chamber (TPC) is presently used as a far detector of the Short Baseline Neutrino (SBN) program at Fermilab (USA) to search for a possible sterile neutrino signal with the Booster Neutrino Beam (BNB). A light detection system, based on 360 Hamamatsu R5912-MOD Photo-Multiplier Tubes (PMTs), has been realized to detect vacuum ultraviolet (VUV) photons produced after the passage of ionizing particles in LAr. High performance electronics allows recording of the PMT signals and provides a fast discrimination for the exploitation of the scintillation light for trigger purposes. This system is fundamental for the TPC operation, contributing to the trigger system and for the 3D reconstruction of events.

The Long Journey of ICARUS: From the LAr-TPC Concept to the First Full-Scale Detector

The Liquid Argon Time Projection Chamber (LAr-TPC) technology was conceived at the end of the 1970s as a way to combine the excellent spatial and calorimetric performance of the traditional bubble chambers with the electronic read-out of the TPCs, obtaining the so-called “electronic bubble chambers”. This technology was intended to be applied in particular to neutrino physics as an alternative to Ring Water Cherenkov detectors. The main technological issues of such an innovative technique were investigated from the very beginning within the ICARUS program, with staged R&D starting from prototypes of increasing mass to arrive, at the end of 1990s, at the largest LAr-TPC detector ever built at that time: ICARUS-T600, with almost 500 tons of active LAr. The successful operations of the ICARUS-T600 LAr-TPC in its more than twenty years of life, from the first run at surface in Pavia (Italy) in 2001 to the LNGS (Italy) underground run being exposed to the CNGS beam from CERN to Gran Sasso (2010–2013) and finally to the ongoing run at Fermilab (USA) for sterile neutrino searches (2020–), have demonstrated the huge potential of the LAr-TPC technique, paving the way to future larger LAr-TPCs detectors as DUNE.

Measurement of neutral current single π0 production on argon with the MicroBooNE detector

We report the first measurement of π0 production in neutral current (NC) interactions on argon with average neutrino energy of ≲1 GeV. We use data from the MicroBooNE detector’s 85 metric tons active volume liquid argon time projection chamber situated in Fermilab’s Booster Neutrino Beam and exposed to 5.89×1020 protons on target for this measurement. Measurements of NC π0 events are reported for two exclusive event topologies without charged pions. Those include a topology with two photons from the decay of the π0 and one proton and a topology with two photons and zero protons. Flux-averaged cross sections for each exclusive topology and for their semi-inclusive combination are extracted (efficiency correcting for two-plus proton final states), and the results are compared to predictions from the genie, neut, and nuwro neutrino event generators. We measure cross sections of 1.243±0.185(syst)±0.076(stat), 0.444±0.098±0.047, and 0.624±0.131±0.075 [10−38 cm2/Ar] for the semi-inclusive NCπ0, exclusive NCπ0+1p, and exclusive NCπ0+0p processes, respectively.7 MoreReceived 19 May 2022Accepted 12 December 2022DOI:https://doi.org/10.1103/PhysRevD.107.012004Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Funded by SCOAP3.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasNeutrino interactionsNucleus-neutrino interactionsParticles & Fields

Direct Detection of Dark Matter with DarkSide-20k

DarkSide run since mid 2015 a 50-kg-active-mass dual phase Liquid Argon Time Projection Chamber (TPC), filled with low radioactivity argon from an underground source and produced world class results for both the low mass (< 20 GeV/c2) and high mass ( > 100 GeV/c2) direct detection search for dark matter. The next stage of the DarkSide program will be a new generation experiment involving a global collaboration from all the current Argon based experiments. DarkSide-20k, is designed as a 20-tonne fiducial mass dual phase Liquid Argon TPC with SiPM based cryogenic photosensors, and is expected to be free of any instrumental background for an exposure of >100 tonne year. Like its predecessor, DarkSide-20k will be housed at the INFN Gran Sasso (LNGS) underground laboratory, and it is expected to attain a WIMP-nucleon cross section exclusion sensitivity of 7.4 × 10−48 cm2 for a WIMP mass of 1 TeV/c2 in a 200 t yr run. DarkSide-20k will be installed inside a membrane cryostat containing more than 700 t of liquid Argon and be surrounded by an active neutron veto based on a Gd-loaded acrylic shell. The talk will give the latest updates of the ongoing R&D and prototype tests validating the initial design. A subsequent objective, towards the end of the next decade, will be the construction of the ultimate detector, ARGO, with a 300 t fiducial mass to push the sensitivity to the neutrino floor region for high mass WIMPs.

Application of transfer learning to neutrino interaction classification

Training deep neural networks using simulations typically requires very large numbers of simulated events. This can be a large computational burden and a limitation in the performance of the deep learning algorithm when insufficient numbers of events can be produced. We investigate the use of transfer learning, where a set of simulated images are used to fine tune a model trained on generic image recognition tasks, to the specific use case of neutrino interaction classification in a liquid argon time projection chamber. A ResNet18, pre-trained on photographic images, was fine-tuned using simulated neutrino images and when trained with one hundred thousand training events reached an F1 score of 0.896,pm ,0.002 compared to 0.836,pm ,0.004 from a randomly-initialised network trained with the same training sample. The transfer-learned networks also demonstrate lower bias as a function of energy and more balanced performance across different interaction types.

Gamma Ray Source Localization for Time Projection Chamber Telescopes Using Convolutional Neural Networks

Diverse phenomena such as positron annihilation in the Milky Way, merging binary neutron stars, and dark matter can be better understood by studying their gamma ray emission. Despite their importance, MeV gamma rays have been poorly explored at sensitivities that would allow for deeper insight into the nature of the gamma emitting objects. In response, a liquid argon time projection chamber (TPC) gamma ray instrument concept called GammaTPC has been proposed and promises exploration of the entire sky with a large field of view, large effective area, and high polarization sensitivity. Optimizing the pointing capability of this instrument is crucial and can be accomplished by leveraging convolutional neural networks to reconstruct electron recoil paths from Compton scattering events within the detector. In this investigation, we develop a machine learning model architecture to accommodate a large data set of high fidelity simulated electron tracks and reconstruct paths. We create two model architectures: one to predict the electron recoil track origin and one for the initial scattering direction. We find that these models predict the true origin and direction with extremely high accuracy, thereby optimizing the observatory’s estimates of the sky location of gamma ray sources.

Search for long-lived heavy neutral leptons and Higgs portal scalars decaying in the MicroBooNE detector

We present a search for long-lived Higgs portal scalars (HPS) and heavy neutral leptons (HNL) decaying in the MicroBooNE liquid-argon time projection chamber. The measurement is performed using data collected synchronously with the NuMI neutrino beam from Fermilab's Main Injector with a total exposure corresponding to $7.01 \times 10^{20}$ protons on target. We set upper limits at the $90\%$ confidence level on the mixing parameter $\lvert U_{\mu 4}\rvert^2$ ranging from $\lvert U_{\mu 4}\rvert^2<12.9\times 10^{-8}$ for Majorana HNLs with a mass of $m_{\rm HNL}=246$ MeV to $\lvert U_{\mu 4}\rvert^2<0.92 \times 10^{-8}$ for $m_{\rm HNL}=385$ MeV, assuming $\lvert U_{e 4}\rvert^2 = \lvert U_{\tau 4}\rvert^2 = 0$ and HNL decays into $\mu^\pm\pi^\mp$ pairs. These limits on $\lvert U_{\mu 4}\rvert^2$ represent an order of magnitude improvement in sensitivity compared to the previous MicroBooNE result. We also constrain the scalar-Higgs mixing angle $\theta$ by searching for HPS decays into $\mu^+\mu^-$ final states, excluding a contour in the parameter space with lower bounds of $\theta^2<31.3 \times 10^{-9}$ for $m_{\rm HPS}=212$ GeV and $\theta^2<1.09 \times 10^{-9}$ for $m_{\rm HPS}=275$ GeV. These are the first constraints on the scalar-Higgs mixing angle $\theta$ from a dedicated experimental search in this mass range.

Xenon-doped liquid argon TPCs as a neutrinoless double beta decay platform

Searches for neutrinoless double-beta decay continue to expand our understanding of the lepton sector, with experiments now pursuing ton-scale target masses with sensitivity to $m_{\beta\beta}$ covering the allowed parameter space for the inverted neutrino mass ordering. Continued searches for this rare decay will require scalable detector technologies to achieve significant increases in the target mass beyond the ton scale, in order to probe the normal ordering region. This work explores the concept of searching for neutrinoless double-beta decay in a 10 kton scale liquid argon time projection chamber (LArTPC) located deep underground and doped with percent-level quantities of xenon. We discuss the design requirements, background mitigation and detector R&D needs, and considerations for deployment in a modified DUNE far detector module. We find that such a detector could reach $m_{\beta\beta}$ sensitivity at the 2-4 meV range with xenon doping at 2% if significant background reductions can be achieved.

Observation of radon mitigation in MicroBooNE by a liquid argon filtration system

The MicroBooNE liquid argon time projection chamber (LArTPC) maintains a high level of liquid argon purity through the use of a filtration system that removes electronegative contaminants in continuously-circulated liquid, recondensed boil off, and externally supplied argon gas. We use the MicroBooNE LArTPC to reconstruct MeV-scale radiological decays. Using this technique we measure the liquid argon filtration system's efficacy at removing radon. This is studied by placing a 500 kBq 222Rn source upstream of the filters and searching for a time-dependent increase in the number of radiological decays in the LArTPC. In the context of two models for radon mitigation via a liquid argon filtration system, a slowing mechanism and a trapping mechanism, MicroBooNE data supports a radon reduction factor of greater than 97% or 99.999%, respectively. Furthermore, a radiological survey of the filters found that the copper-based filter material was the primary medium that removed the 222Rn. This is the first observation of radon mitigation in liquid argon with a large-scale copper-based filter and could offer a radon mitigation solution for future large LArTPCs.

Liquid Argon Time Projection Chamber Trigger Development with MicroBooNE

The Micro Booster Neutrino Experiment (MicroBooNE) is a Liquid Argon Time Projection Chamber (LArTPC) neutrino detector at Fermilab that has been operating since 2015. It aims to perform v-Ar cross-section measurements, explore the low-energy excess reported by the MiniBooNE experiment and perform a combined search for sterile neutrino oscillations as part of three LArTPCs that make up the Short Baseline Neutrino (SBN) Program at Fermilab. Since MicroBooNE is currently in an R&D phase, it offers a unique opportunity for the implementation and testing of TPC-based triggers as R&D towards the Deep Underground Neutrino Experiment (DUNE). One of the technical challenges of DUNE that we aim to address with this study is that of efficient self-triggering of a LArTPC utilizing TPC signal information. This capability will enable searches for rare processes in DUNE, such as neutrino interactions from a potential galactic supernova burst. This talk will describe the MicroBooNE TPC readout system and ongoing R&D efforts to develop and demonstrate TPC-based triggering.

Scintillation light detection in the long-drift ProtoDUNE-DP liquid argon TPC

ProtoDUNE-DP is a 6 × 6 × 6m3 liquid argon time-projection-chamber (LArTPC) operated at the Neutrino Platform at CERN in 2019-2020 as a prototype of the DUNE Far Detector. DUNE is a dual-site experiment for long-baseline neutrino oscillation studies, neutrino astrophysics and nucleon decay searches. The light signal in these detectors is crucial to provide precise timing capabilities. In ProtoDUNE-DP, scintillation light produced by cosmic muons in the LArTPC is collected by the photomultiplier tubes (PMTs) placed up to 7 m away from the point of interaction. The scintillation light production and propagation processes are analyzed and compared to simulations, improving the understanding of some liquid argon properties.

Impact of xenon doping in the scintillation light in a large liquid-argon TPC

The use of xenon-doped liquid argon (Xe-doped LAr) is a promising alternative for large-scale liquid argon Time Projection Chambers (LAr-TPC), since it mitigates the light suppression due to impurities and it also improves the photon-detection efficiency and uniformity with the distance. This study analyses the impact of using Xe-doped LAr in ProtoDUNE Dual-Phase, a 750 ton Dual-Phase LAr-TPC placed at CERN. ProtoDUNE Dual-Phase completed a Xe-doping data-taking campaign in summer 2020 by re-filling the detector with 230 tons of Xe-doped LAr contaminated with nitrogen, and performing dedicated nitrogen injections. The effects of the presence of Xe at 5.8 ppm in the scintillation light production and propagation are analysed in this paper, showing an increase of the collected photons, but a suppression of the light signal amplitude. A 60% increase of the light attenuation length is measured. The impact on the scintillation time profile is also studied. A model to fit the time profile is proposed and the time constants of the physics processes are obtained.