Liquid Argon Time Projection Chamber Research Articles

Calibration of the liquid argon ionization response to low energy electronic and nuclear recoils with DarkSide-50

DarkSide-50 has demonstrated the high potential of dual-phase liquid argon time projection chambers in exploring interactions of WIMPs in the GeV/c$^2$ mass range. The technique, based on the detection of the ionization signal amplified via electroluminescence in the gas phase, allows to explore recoil energies down to the sub-keV range. We report here on the DarkSide-50 measurement of the ionization yield of electronic recoils down to $\sim$180~eV$_{er}$, exploiting $^{37}$Ar and $^{39}$Ar decays, and extrapolated to a few ionization electrons with the Thomas-Imel box model. Moreover, we present a model-dependent determination of the ionization response to nuclear recoils down to $\sim$500~eV$_{nr}$, the lowest ever achieved in liquid argon, using \textit{in situ} neutron calibration sources and external datasets from neutron beam experiments.

Measurement of the flux-averaged inclusive charged-current electron neutrino and antineutrino cross section on argon using the NuMI beam and the MicroBooNE detector

We present a measurement of the combined νe+ν¯e flux-averaged charged-current inclusive cross section on argon using data from the MicroBooNE liquid argon time projection chamber (lartpc) at Fermilab. Using the off-axis flux from the NuMI beam, MicroBooNE has reconstructed 214 candidate νe+ν¯e interactions with an estimated exposure of 2.4×1020 protons on target. Given the estimated purity of 38.6%, this implies the observation of 80 νe+ν¯e events in argon, the largest such sample to date. The analysis includes the first demonstration of a fully automated application of a dE/dx-based particle discrimination technique of electron- and photon-induced showers in a lartpc neutrino detector. The main background for this first νe analysis is cosmic ray contamination. Significantly higher purity is expected in underground detectors, as well as with next-generation reconstruction algorithms. We measure the νe+ν¯e flux-averaged charged-current total cross section to be 6.84±1.51(stat)±2.33(sys)×10−39 cm2/nucleon, for neutrino energies above 250 MeV and an average neutrino flux energy of 905 MeV when this threshold is applied. The measurement is sensitive to neutrino events where the final state electron momentum is above 48 MeV/c, includes the entire angular phase space of the electron, and is in agreement with the theoretical predictions from genie and nuwro. This measurement is also the first demonstration of electron-neutrino reconstruction in a surface lartpc in the presence of cosmic-ray backgrounds, which will be a crucial task for surface experiments like those that comprise the short-baseline neutrino program at Fermilab.10 MoreReceived 11 January 2021Accepted 21 June 2021DOI:https://doi.org/10.1103/PhysRevD.104.052002Published 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 interactionsTechniquesNeutrino detectorsParticle interactionsTime-projection chambersParticles & Fields

Measurement of the longitudinal diffusion of ionization electrons in the MicroBooNE detector

Accurate knowledge of electron transport properties is vital to understanding the information provided by liquid argon time projection chambers (LArTPCs). Ionization electron drift-lifetime, local electric field distortions caused by positive ion accumulation, and electron diffusion can all significantly impact the measured signal waveforms. This paper presents a measurement of the effective longitudinal electron diffusion coefficient, DL, in MicroBooNE at the nominal electric field strength of 273.9 V/cm. Historically, this measurement has been made in LArTPC prototype detectors. This represents the first measurement in a large-scale (85 tonne active volume) LArTPC operating in a neutrino beam. This is the largest dataset ever used for this measurement. Using a sample of ∼70,000 through-going cosmic ray muon tracks tagged with MicroBooNE's cosmic ray tagger system, we measure DL = 3.74+0.28 -0.29 cm2/s.

Cosmic Ray Background Removal With Deep Neural Networks in SBND.

In liquid argon time projection chambers exposed to neutrino beams and running on or near surface levels, cosmic muons, and other cosmic particles are incident on the detectors while a single neutrino-induced event is being recorded. In practice, this means that data from surface liquid argon time projection chambers will be dominated by cosmic particles, both as a source of event triggers and as the majority of the particle count in true neutrino-triggered events. In this work, we demonstrate a novel application of deep learning techniques to remove these background particles by applying deep learning on full detector images from the SBND detector, the near detector in the Fermilab Short-Baseline Neutrino Program. We use this technique to identify, on a pixel-by-pixel level, whether recorded activity originated from cosmic particles or neutrino interactions.

Point proposal network for reconstructing 3D particle endpoints with subpixel precision in liquid argon time projection chambers

Liquid Argon Time Projection Chambers are used as precision detectors in ongoing and upcoming neutrino experiments. In this paper the authors adapt machine learning techniques to better identify the beginning- and end-points of particle tracks in LarTPCs, offering significant improvement over traditional methods in reconstructing the true event.

Performance study of a 3×1×1 m3 dual phase liquid Argon Time Projection Chamber exposed to cosmic rays

We report the results of the analyses of the cosmic ray data collected with a 4 tonne (3×1×1 m3) active mass (volume) Liquid Argon Time-Projection Chamber (TPC) operated in a dual-phase mode. We present a detailed study of the TPC's response, its main detector parameters and performance. The results are important for the understanding and further developments of the dual-phase technology, thanks to the verification of key aspects, such as the extraction of electrons from liquid to gas and their amplification through the entire one square metre readout plain, gain stability, purity and charge sharing between readout views.

Radioactive background for ProtoDUNE detector

The Deep Underground Neutrino Experiment (DUNE) is a leading-edge, international experiment for neutrino science and proton decay studies. This experiment is looking for answers regarding several fundamental questions about the nature of matter and the evolution of the universe: origin of matter, unification of forces, physics of black holes. Two far detector prototypes using two distinct technologies have been developed at CERN. The prototypes are testing and validating the liquid argon time projection chamber technology (LArTPC). In neutrino physics, as well as in any experiment with rare interaction rate, the good knowledge of the radioactive backgrounds is important to the success of the study.Muons and neutrons represent the main sources of background for this kind of experiments. In this paper, we have considered two sources of neutrons: cosmic neutrons and neutrons coming from the accelerating tunnel. Also, cosmic muons are taken into account. The contribution of these particles to the production of radioactive isotopes inside the active volume of the detector in comparison to the one corresponding to muons is shown. Also, simulations of nuclear reactions for the processes of interest for investigating the radioactive background due to the lack of measurements or insufficient experimental data are presented. Most of the results presented in this paper will be of interest for the future underground DUNE experiment.

Neutral pion reconstruction using machine learning in the experiment at 〈E ν〉 6 GeV

This paper presents a novel neutral-pion reconstruction that takes advantage of the machine learning technique of semantic segmentation using MINERvA data collected between 2013–2017, with an average neutrino energy of 6 GeV. Semantic segmentation improves the purity of neutral pion reconstruction from two γs from 70.7 ± 0.9% to 89.3 ± 0.7% and improves the efficiency of the reconstruction by approximately 40%. We demonstrate our method in a charged current neutral pion production analysis where a single neutral pion is reconstructed. This technique is applicable to modern tracking calorimeters, such as the new generation of liquid-argon time projection chambers, exposed to neutrino beams with 〈E ν〉 between 1–10 GeV. In such experiments it can facilitate the identification of ionization hits which are associated with electromagnetic showers, thereby enabling improved reconstruction of charged-current νe events arising from νμ → νe appearance.

Cosmic Ray Background Rejection with Wire-Cell LArTPC Event Reconstruction in the MicroBooNE Detector

For a large liquid argon time projection chamber (LArTPC) operating on or near the Earth's surface to detect neutrino interactions, the rejection of cosmogenic background is a critical and challenging task because of the large cosmic ray flux and the long drift time of the TPC. We introduce a superior cosmic background rejection procedure based on the Wire-Cell three-dimensional (3D) event reconstruction for LArTPCs. From an initial 1:20,000 neutrino to cosmic-ray background ratio, we demonstrate these tools on data from the MicroBooNE experiment and create a high performance generic neutrino event selection with a cosmic contamination of 14.9\% (9.7\%) for a visible energy region greater than O(200)~MeV. The neutrino interaction selection efficiency is 80.4\% and 87.6\% for inclusive $\nu_\mu$ charged-current and $\nu_e$ charged-current interactions, respectively. This significantly improved performance compared to existing reconstruction algorithms, marks a major milestone toward reaching the scientific goals of LArTPC neutrino oscillation experiments operating near the Earth's surface.

Neutrino energy estimation in neutral current interactions and prospects for sterile neutrino searches

Modern neutrino detectors, particularly the large liquid argon time projection chambers (LAr TPCs) at SBN and DUNE, provide an unprecedented amount of information about GeV-scale interactions. By taking advantage of the excellent spatial and calorimetric resolution as well as the low tracking thresholds provided by LAr TPCs, we present a novel method of estimating the neutrino energy in neutral current interactions. This method has potential implications for the search for a sterile neutrino; it allows for the potential observation of spectral distortions due to sterile neutrino-induced oscillations in the neutral current neutrino energy spectrum. As an example, we use this method to perform an analysis of the statistics-only sensitivity to sterile neutrinos in the neutral current channel at SBN under a 3+1 model.

Neutrino event selection in the MicroBooNE liquid argon time projection chamber using Wire-Cell 3D imaging, clustering, and charge-light matching

An accurate and efficient event reconstruction is required to realize the full scientific capability of liquid argon time projection chambers (LArTPCs). The current and future neutrino experiments that rely on massive LArTPCs create a need for new ideas and reconstruction approaches. Wire-Cell, proposed in recent years, is a novel tomographic event reconstruction method for LArTPCs. The Wire-Cell 3D imaging approach capitalizes on charge, sparsity, time, and geometry information to reconstruct a topology-agnostic 3D image of the ionization electrons prior to pattern recognition. A second novel method, the many-to-many charge-light matching, then pairs the TPC charge activity to the detected scintillation light signal, thus enabling a powerful rejection of cosmic-ray muons in the MicroBooNE detector. A robust processing of the scintillation light signal and an appropriate clustering of the reconstructed 3D image are fundamental to this technique. In this paper, we describe the principles and algorithms of these techniques and their successful application in the MicroBooNE experiment. A quantitative evaluation of the performance of these techniques is presented. Using these techniques, a 95% efficient pre-selection of neutrino charged-current events is achieved with a 30-fold reduction of non-beam-coincident cosmic-ray muons, and about 80% of the selected neutrino charged-current events are reconstructed with at least 70% completeness and 80% purity.

Characterization and QC practice of 16-channel ADC ASIC at cryogenic temperature for Liquid Argon TPC front-end readout electronics system in DUNE experiment

ColdADC is a low-noise 16-channel analog-to-digital converter ASIC designed for cold readout electronics of Liquid Argon Time Projection Chambers (LArTPCs) in the Deep Underground Neutrino Experiment (DUNE). ColdADC was specifically designed for operation at cryogenic temperatures (77 K–89 K). Cold electronics is considered to be an enabling technology for liquid argon detectors in neutrino experiments. The main function of the chip is to digitize signals from the 16-channel charge-sensitive amplifier designed at BNL (LArASIC) and send digitized signals to the data aggregator and serializer chip (COLDATA). ColdADC operates with a resolution of 12 bits and a sampling rate of 2 MS/s per channel. A complete characterization of prototype chips was conducted at cryogenic temperature, involving power consumption, noise and linearity performance. In the first DUNE Far-Detector module, 384000 data channels will be read out, corresponding to 24000 ColdADC chips. Therefore, systematic testing of ColdADC chips at cryogenic temperature is vital for the quality and reliability of DUNE Single-Phase modules. We have developed a Quality Control (QC) test stand that includes ColdADC evaluation and characterization at cryogenic temperature. The QC procedure was applied to a first batch of 33 chips, corresponding to 528 data channels. The obtained results meet all the required DUNE specifications for ColdADC. Moreover, the testing outcome allowed us to identify possibilities of performance optimization that are currently being addressed in the design of the next version of the chip. The Quality Control procedure is proved to be successful and will be a reference design for future large-batch production testing of final ColdADC ASICs.

Prospects for detecting boosted dark matter in DUNE through hadronic interactions

Boosted dark matter (BDM) is a well-motivated class of dark matter (DM) candidates in which a small component of DM is relativistic at the present time. We lay the foundation for BDM searches via hadronic interactions in large liquid-argon time-projection chambers (LArTPCs), such as DUNE. We investigate BDM-nucleus scattering in detail by developing new event generation techniques with a parameterized detector simulation. We study the discovery potential in a DUNE-like experiment using the low threshold and directionality of hadron detection in LArTPCs and compare with other experiments.

Convolutional neural network for multiple particle identification in the MicroBooNE liquid argon time projection chamber

We present the multiple particle identification (MPID) network, a convolutional neural network for multiple object classification, developed by MicroBooNE. MPID provides the probabilities that an interaction includes an e−, γ, μ−, π±, and protons in a liquid argon time projection chamber single readout plane. The network extends the single particle identification network previously developed by MicroBooNE [Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber, R. Acciarri et al. J. Instrum. 12, P03011 (2017)]. MPID takes as input an image either cropped around a reconstructed interaction vertex or containing only activity connected to a reconstructed vertex, therefore relieving the tool from inefficiencies in vertex finding and particle clustering. The network serves as an important component in MicroBooNE’s deep-learning-based νe search analysis. In this paper, we present the network’s design, training, and performance on simulation and data from the MicroBooNE detector.29 MoreReceived 23 October 2020Accepted 25 March 2021DOI:https://doi.org/10.1103/PhysRevD.103.092003Published 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)Physical SystemsNeutrinosTechniquesArtificial neural networksMachine learningParticles & Fields

Supernova neutrino burst detection with the Deep Underground Neutrino Experiment

The Deep Underground Neutrino Experiment (DUNE), a 40-kton underground liquid argon time projection chamber experiment, will be sensitive to the electron-neutrino flavor component of the burst of neutrinos expected from the next Galactic core-collapse supernova. Such an observation will bring unique insight into the astrophysics of core collapse as well as into the properties of neutrinos. The general capabilities of DUNE for neutrino detection in the relevant few- to few-tens-of-MeV neutrino energy range will be described. As an example, DUNE’s ability to constrain the nu _e spectral parameters of the neutrino burst will be considered.

Prospects of detecting the reactor $$\overline{\nu }_{\mathrm{e}}$$-Ar coherent elastic scattering with a low-threshold dual-phase argon time projection chamber at Taishan

We propose to measure the coherent elastic neutrino nucleus scattering (CE$\nu$NS) using a dual-phase liquid argon time projection chamber (TPC) with 200kg fiducial mass. The detector is expected to be adjacent to the JUNO-TAO experiment and to be about 35m from a reactor core with 4.6GW thermal power at Taishan. The antineutrino flux is approximately 6$\times10^{12}$cm$^{-1}$s$^{-1}$ at this location, leading to more than 11,000 coherent scattering events per day in the fiducial mass. However, the nuclear recoil energies concentrate in the sub-keV region, corresponding to less than ten ionisation electrons in the liquid argon. The detection of several ionisation electrons can be achieved in the dual-phase TPC due to the large amplification in the gas region. With a feasible detection threshold of four ionisation electrons, the signal rate is 955 per day. The detector is designed to be shielded well from cosmogenic backgrounds and ambient radioactivities to reach a 16% background-to-signal ratio in the energy region of interest. With the large CE$\nu$NS sample, the expected sensitivity of measuring the weak mixing angle $\sin^{2}\theta_{W}$, and of limiting the neutrino magnetic moment are discussed. In addition, a synergy between the reactor antineutrino CE$\nu$NS experiment and the dark matter experiment is foreseen.

Neural-network-driven proton decay sensitivity in the p \u2192 overline{nu} K+ channel using large liquid argon time projection chambers

We report on an updated sensitivity for proton decay via p → overline{nu} K+ at large, dual phase liquid argon time projection chambers (LAr TPCs). Our work builds on a previous study in which several nucleon decay modes have been simulated and analyzed [1]. At the time several assumptions were needed to be made on the detector and the backgrounds. Since then, the community has made progress in defining these, and the computing power available enables us to fully simulate and reconstruct large samples in order to perform a better estimate of the sensitivity to proton decay. In this work, we examine the benchmark channel p → overline{nu} K+, which was previously found to be one of the cleanest channels. Using an improved neutrino event generator and a fully simulated LAr TPC detector response combined with a dedicated neural network for kaon identification, we demonstrate that a lifetime sensitivity of τ /Br (p → overline{nu} K+) > 7 × 1034 years at 90% confidence level can be reached at an exposure of 1 megaton · year in quasi-background-free conditions, confirming the superiority of the LAr TPC over other technologies to address the challenging proton decay modes.

Measurement of the atmospheric muon rate with the MicroBooNE Liquid Argon TPC

is a near-surface liquid argon (LAr) time projection chamber (TPC) located at Fermilab. We measure muons originating from cosmic interactions in the atmosphere using both the charge collection and light readout detectors. The data is compared with the cosmic-ray simulation. Agreement is found between the observation, simulation and previous results. Furthermore, the angular resolution of the reconstructed muons inside the TPC is studied in simulation.

Predicting transport effects of scintillation light signals in large-scale liquid argon detectors

Liquid argon is being employed as a detector medium in neutrino physics and Dark Matter searches. A recent push to expand the applications of scintillation light in Liquid Argon Time Projection Chamber neutrino detectors has necessitated the development of advanced methods of simulating this light. The presently available methods tend to be prohibitively slow or imprecise due to the combination of detector size and the amount of energy deposited by neutrino beam interactions. In this work we present a semi-analytical model to predict the quantity of argon scintillation light observed by a light detector with a precision better than 10%, based only on the relative positions between the scintillation and light detector. We also provide a method to predict the distribution of arrival times of these photons accounting for propagation effects. Additionally, we present an equivalent model to predict the number of photons and their arrival times in the case of a wavelength-shifting, highly-reflective layer being present on the detector cathode. Our proposed method can be used to simulate light propagation in large-scale liquid argon detectors such as DUNE or SBND, and could also be applied to other detector mediums such as liquid xenon or xenon-doped liquid argon.

Semantic segmentation with a sparse convolutional neural network for event reconstruction in MicroBooNE

We present the performance of a semantic segmentation network, SparseSSNet, that provides pixel-level classification of MicroBooNE data. The MicroBooNE experiment employs a liquid argon time projection chamber for the study of neutrino properties and interactions. SparseSSNet is a submanifold sparse convolutional neural network, which provides the initial machine learning based algorithm utilized in one of MicroBooNE's $\nu_e$-appearance oscillation analyses. The network is trained to categorize pixels into five classes, which are re-classified into two classes more relevant to the current analysis. The output of SparseSSNet is a key input in further analysis steps. This technique, used for the first time in liquid argon time projection chambers data and is an improvement compared to a previously used convolutional neural network, both in accuracy and computing resource utilization. The accuracy achieved on the test sample is $\geq 99\%$. For full neutrino interaction simulations, the time for processing one image is $\approx$ 0.5 sec, the memory usage is at 1 GB level, which allows utilization of most typical CPU worker machine.