Argon Detector Research Articles

The science and technology of liquid argon detectors

The science and technology of liquid argon detectors

Unpaired Image Translation to Mitigate Domain Shift in Liquid Argon Time Projection Chamber Detector Responses

Abstract Deep learning algorithms often are developed and trained on a training dataset and deployed on test datasets. Any systematic difference between the training and a test dataset may severely degrade the final algorithm performance on the test dataset -- what is known as the domain shift problem. This issue is prevalent in many scientific domains where algorithms are trained on simulated data but applied to real-world datasets. Typically, the domain shift problem is solved through various domain adaptation methods. However, these methods are often tailored to a specific downstream task and may not easily generalize to different tasks. This work explores the feasibility of using an alternative way to solve the domain shift problem that is not specific to any downstream algorithm. The proposed approach relies on modern Unpaired Image-to-Image translation techniques, designed to find translations between different image domains in a fully unsupervised fashion. In this study, the approach is applied to a domain shift problem commonly encountered in Liquid Argon Time Projection Chamber detector research when seeking a way to translate samples between two differently distributed LArTPC detector datasets deterministically. This translation allows for mapping real-world data into the simulated data domain where the downstream algorithms can be run with much less domain-shift-related performance degradation. Conversely, using the translation from the simulated data in a real-world domain can increase the realism of the simulated dataset and reduce the magnitude of any systematic uncertainties. We adapted several popular UI2I translation algorithms to work on scientific data and demonstrated the viability of these techniques for solving the domain shift problem with LArTPC detector data. To facilitate further development of domain adaptation techniques for scientific datasets, the "Simple Liquid-Argon Track Samples" (SLATS) dataset used in this study also is published.

Imaging systems for the liquid Argon target of SAND at the DUNE Near Detector Complex

The Deep Underground Neutrino Experiment (DUNE) has among its primary goals the determination of the neutrino mass ordering and the possible CP-violating phase in the neutrino mixing matrix. The System for On-Axis Neutrino Detection (SAND) is one of the three components of the DUNE Near Detector complex, permanently located on-axis to monitor the neutrino beam stability and measure its flux. SAND includes a novel liquid Argon detector - GRAIN - designed to image neutrino interactions using scintillation light produced in Ar by charged particles, eliminating the dependence on slow charge collection of LAr TPCs. Two optical systems are currently being developed for GRAIN, both based on Silicon Photomultiplier (SiPM) matrices, coupled either to UV cryogenic lenses or Coded Aperture masks. This contribution will present the design of GRAIN optical systems and their spatial resolution performance in preliminary simulation studies.

Assessment of few-hits machine learning classification algorithms for low-energy physics in liquid argon detectors

The physics potential of massive liquid argon TPCs in the low-energy regime is still to be fully reaped because few-hits events encode information that can hardly be exploited by conventional classification algorithms. Machine learning (ML) techniques give their best in these types of classification problems. In this paper, we evaluate their performance against conventional (deterministic) algorithms. We demonstrate that both Convolutional Neural Networks (CNN) and Transformer-Encoder methods outperform deterministic algorithms in one of the most challenging classification problems of low-energy physics (single- versus double-beta events). We discuss the advantages and pitfalls of Transformer-Encoder methods versus CNN and employ these methods to optimize the detector parameters, with an emphasis on the DUNE Phase II detectors (“Module of Opportunity”).

Determination of the Photon Detection Efficiency, Time and Spatial Resolution of the Light Collecting System of the near Liquid Argon Detector (ND-LAr) of the DUNE Experiment

Determination of the Photon Detection Efficiency, Time and Spatial Resolution of the Light Collecting System of the near Liquid Argon Detector (ND-LAr) of the DUNE Experiment

Neural Networks for position reconstruction in liquid argon detectors

This article explores the integration of Deep Learning and Explainable Artificial Intelligence in Particle Physics, focusing on their application in position reconstruction within dual-phase liquid argon detectors for Dark Matter search. Facing challenges like pile-up scenarios, Neural Networks prove crucial for refining algorithms. This article emphasizes Deep Learning's role in addressing regression and classification problems, such as position reconstruction and particle identification, particularly in Time Projection Chambers. Explainable Artificial Intelligence emerges as pivotal in unraveling Deep Learning's complex decisions, exposing biases, and facilitating improvement cycles. Innovations like Evolutionary Neural Networks and topology-driven dataset reduction offer potential efficiency gains. The conclusion highlights challenges in analyzing massive data volumes, emphasizing the need for adaptability and ethical considerations in the pursuit of understanding Dark Matter.

Measurement of ambient radon progeny decay rates and energy spectra in liquid argon using the MicroBooNE detector

We report measurements of radon progeny in liquid argon within the MicroBooNE time projection chamber (LArTPC). The presence of specific radon daughters in MicroBooNE’s 85 metric tons of active liquid argon bulk is probed with newly developed charge-based low-energy reconstruction tools and analysis techniques to detect correlated Bi214−Po214 radioactive decays. Special datasets taken during periods of active radon doping enable new demonstrations of the calorimetric capabilities of single-phase neutrino LArTPCs for β and α particles with electron-equivalent energies ranging from 0.1 to 3.0 MeV. By applying Bi214−Po214 detection algorithms to data recorded over a 46-day period, no statistically significant presence of radioactive Bi214 is detected, and a limit on the activity is placed at <0.35 mBq/kg at the 95% confidence level. This bulk Bi214 radiopurity limit—the first ever reported for a liquid argon detector incorporating liquid-phase purification—is then further discussed in relation to the targeted upper limit of 1 mBq/kg on bulk Rn222 activity for the DUNE neutrino detector. Published by the American Physical Society 2024

Reconstruction of neutrino interactions in SANDwith an innovative liquid Argon imaging detector

The Deep Underground Neutrino Experiment will be a next-generation neutrino oscillation long-baseline accelerator experiment with the aim of determining the still unknown neutrino oscillation parameters, observing proton decay and detecting supernova neutrinos exploiting a Liquid Argon Time Projection Chambers (LArTPC) of unprecedented size. However, despite their successful application in neutrino and DM experiments, the performances of LArTPCs are limited in high intensity environments, such as in near-site detectors on neutrino beams, due to the long drift time needed to collect the ionisation charge. The design of SAND at the DUNE Near Detector complex includes a 1-ton LAr target -GRAIN (Granular Argon for Interaction of Neutrinos)- designed to overcome such limitation by imaging the scintillation light produced in neutrino interactions. By capturing “pictures” of the LAr (or LXe), GRAIN will allow to reconstruct the event topologies and energy deposition. Using this information, and that provided by the SAND electromagnetic calorimeter and target tracker system, SAND will allow on-axis beam monitoring, the control of systematics uncertainties for the oscillation analysis, precision measurements of neutrino cross-sections, and beyond Standard Model searches.

A study of liquid argon detector’s n/$$\gamma $$ discrimination capability with PMT or SiPM readout

A study of liquid argon detector’s n/$$\gamma $$ discrimination capability with PMT or SiPM readout

Challenges for dark matter direct search with SiPMs

Liquid xenon and liquid argon detectors are leading the direct dark matter search and are expected to be the candidate technology for the forthcoming generation of ultra-sensitive large-mass detectors. At present, scintillation light detection in those experiments is based on ultra-pure low-noise photo-multipliers. To overcome the issues in terms of the extreme radio-purity, costs, and technological feasibility of the future dark matter experiments, the novel silicon photomultiplier (SiPM)-based photodetector modules seem to be promising candidates, capable of replacing the present light detection technology. However, the intrinsic features of SiPMs may limit the present expectations. In particular, interfering phenomena, especially related to the optical correlated noise, can degrade the energy and pulse shape resolutions. As a consequence, the projected sensitivity of the future detectors has to be reconsidered accordingly.

Kubernetes for the Deep Underground Neutrino Experiment Data Acquisition

The Deep Underground Neutrino Experiment (DUNE) is a next-generation long-baseline neutrino experiment based in the USA which is expected to start taking data in 2029. DUNE aims to precisely measure neutrino oscillation parameters by detecting neutrinos from the LBNF beamline (Fermilab) at the Far Detector, 1,300 kilometres away, in South Dakota at the Sanford Underground Research Facility. The Far Detector will consist of four cryogenic Liquid Argon Time Projection Chamber detectors of 17 kT, each producing more than 1 TB/sec of data. The main requirements for the data acquisition system are the ability to run continuously for extended periods of time, with a 99% up-time requirement, and the functionality to record both beam neutrinos and low energy neutrinos from the explosion of a neighbouring supernova, should one occur during the lifetime of the experiment. The key challenges are the high data rates that the detectors generate and the deep underground environment, which places constraints on power and space. To overcome these challenges, DUNE plans to use a highly optimised C++ software suite and a server farm of about 110 nodes continuously running about two hundred multicore processes located close to the detector, 1.5 kilometres underground. Thirty nodes will be at the surface and will run around two hundred processes simultaneously. DUNE is studying the use of the Kubernetes framework to manage containerised workloads and take advantage of its resource definitions and high up-time services to run the DAQ system. Progress in deploying these systems at the CERN neutrino platform on the prototype DUNE experiments is reported.

Thermal conductivity-based hydrogen in argon detector (HAD): Design, development of instrumentation, and performance evaluation.

The Hydrogen in Argon Detectors (HADs) working on the principle of thermal conductivity difference between argon (reference) and argon + H2 (sample) in the cover gas of a sodium-cooled fast breeder reactor using the Wheatstone bridge circuit. The output of HAD is very sensitive to the gas flow rate and to the variation in ambient temperature. The RMSnoise of current was brought within ±1 µA by a constant current source across the bridge. The temperature correction factor fed in the processor brought down the RMSnoise in the HAD signal within ±2.4 mV than the uncorrected one having RMSnoise of ±230 mV. The filtered noise of HAD facilitates enhancing the detection limit of HAD down to 30 ppm (3σ) of hydrogen in argon.

Matter effect in presence of a sterile neutrino and resolution of the octant degeneracy using a liquid argon detector

Matter effect in presence of a sterile neutrino and resolution of the octant degeneracy using a liquid argon detector

The cryogenic RWELL: a stable charge multiplier for dual-phase liquid argon detectors

The operation of a cryogenic Resistive WELL (RWELL) in liquid argon vapor is reported for the first time. It comprises a Thick Gas Electron Multiplier (THGEM) structure coupled to a resistive Diamond-Like Carbon (DLC) anode deposited on an insulating substrate. The multiplier was operated at cryogenic temperature (90 K, 1.2 bar) in saturated argon vapor and characterized in terms of charge gain and electrical stability. A comparative study with standard, non-resistive THGEM (a.k.a LEM) and WELL multipliers confirmed the RWELL advantages in terms of discharge quenching – i.e. superior gain and stability.

Thermodynamic stability of xenon-doped liquid argon detectors

Thermodynamic stability of xenon-doped liquid argon detectors

Probing mass orderings in presence of a very light sterile neutrino in a liquid argon detector

Results from experiments like LSND and MiniBooNE hint towards the possible presence of an extra eV scale sterile neutrino. The addition of such a neutrino will significantly impact the standard three flavor neutrino oscillations. In particular, it can give rise to additional degeneracies due to additional sterile parameters. For an eV scale sterile neutrino, the cosmological constraints dictate that the sterile state is heavier than the three active states. However, for lower masses of sterile neutrinos, the sterile state can be lighter than one and/or more of the three states. In such cases, the mass ordering of the sterile neutrinos also becomes unknown, along with the mass ordering of the active states. In this paper, we explore the mass ordering sensitivity in the presence of a sterile neutrino assuming the mass squared difference |Δ41| to be in the range 10−4−1 eV2. We study (i) how the ordering of the active states, i.e. the determination of the sign of Δ31 gets affected by the presence of a sterile neutrino in the above mass range, (ii) the possible determination of the sign of Δ41 for Δ41 in the range 10−4−0.1 eV2. This analysis is done in the context of a liquid argon detector using beam neutrinos traveling a distance of 1300 km and atmospheric neutrinos that propagate through a distance ranging from 10 - 10000 km, allowing resonant matter effects. Apart from presenting separate results from these sources, we also do a combined study and probe the synergy between these two in giving an enhanced sensitivity.

Analysis of signal waveform from a midsize liquid argon detector

The midsize single-phase liquid argon prototype detector, operating at the surface laboratory, is designed to measure scintillation light emitted by the liquid argon (LAr). The detector employs 42 8-inch photomultiplier tubes (PMT) to collect the light. By analyzing the waveform of the signal, important detector characteristics such as the slow decay time constant that characterizes the purity of the liquid argon can be obtained. To describe the signal waveform, a model, which takes into account the liquid argon emission decay times together with the TPB re-emission process as well as the signal reflection effects, is used. The TPB re-emission process is introduced using a three-exponential time structure. Additionally, experimental results provide comprehensive validation for a post-peak hump structure, which is attributed to signal reflection.

Influence of sample temperature and ambient argon pressure on LIBS spectra of extracted animal fat

This work explores the influence of sample temperature, ambient gas pressure, laser energy, and detector gate delay (DGD) on the LIBS emissions of extracted animal fat. The emissions from laser-induced plasma of extracted animal fat were acquired with varying sample temperatures, ambient argon pressure, laser energy, and detector gate delay. A thermoelectric system fitted with a temperature-controlling sensor was used to control the sample temperature. For maximum optical emission intensity, the detector gate delay (DGD) shifted toward a lower value with the increase in sample temperature for all ambient argon pressures. The nine-fold signal enhancement, ten times improvement in signal-to-noise ratio (SNR), and significantly reduced self-absorption were observed in all detected emission lines recorded at 200 mJ laser energy –2 ºC sample temperature, 375 Torr ambient argon pressure, and 2 µs DGD. The repeatability of emission signals was evaluated by calculating the relative standard deviation (RSD) of the emission line (Zn I (636.23 nm)) at different sample temperatures, which improved from 35 % to 6 % when the sample changed from liquid to solid (frozen). During the semi-liquid form of fat, the decreasing ambient gas pressure did not significantly affect signal repeatability. In addition to diagnosing the fat plasma, the plasma temperature and electron number density have been evaluated as a function of sample temperature and ambient argon pressure.

Developing a single-phase liquid argon detector with SiPM readout

Developing a single-phase liquid argon detector with SiPM readout

Prospects for detecting axionlike particles at the Coherent CAPTAIN-Mills experiment

We show results from the Coherent CAPTAIN Mills (CCM) 2019 engineering run which begin to constrain regions of parameter space for axionlike particles (ALPs) produced in electromagnetic particle showers in an 800 MeV proton beam dump, and further investigate the sensitivity of ongoing data-taking campaigns for the CCM200 upgraded detector. Based on beam-on background estimates from the engineering run, we make realistic extrapolations for background reduction based on expected shielding improvements, reduced beam width, and analysis-based techniques for background rejection. We obtain reach projections for two classes of signatures; ALPs coupled primarily to photons can be produced in the tungsten target via the Primakoff process, and then produce a gamma-ray signal in the liquid argon CCM detector either via inverse Primakoff scattering or decay to a photon pair. ALPs with significant electron couplings have several additional production mechanisms (Compton scattering, e+e− annihilation, ALP-bremsstrahlung) and detection modes (inverse Compton scattering, external e+e− pair conversion, and decay to e+e−). In some regions, the constraint is marginally better than both astrophysical and terrestrial constraints. With the beginning of a three year run, CCM will be more sensitive to this parameter space by up to an order of magnitude for both ALP-photon and ALP-electron couplings. The CCM experiment will also have sensitivity to well-motivated parameter space of QCD axion models. It is only a recent realization that accelerator-based large volume liquid argon detectors designed for low-energy coherent neutrino and dark matter scattering searches are also ideal for probing ALPs in the unexplored ∼MeV mass scale.8 MoreReceived 20 January 2022Accepted 25 April 2023DOI:https://doi.org/10.1103/PhysRevD.107.095036Published 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 AreasAxionsExtensions of gauge sectorHypothetical particle physics modelsParticle dark matterParticle decaysParticle interactionsParticle productionParticles & Fields