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Presentation to be presented during the first annual Prompt Radiation Detection and Imaging Workshop, hosted by the NNSS Dynamic Instruments Team, April 25–28, 2022, at NLV C-01 Auditorium and via Webex.
Artificial Intelligence is poised to transform the design of complex, large-scale detectors like ePIC at the future Electron Ion Collider. Featuring a central detector with additional detecting systems in the far forward and far backward regions, the ePIC experiment incorporates numerous design parameters and objectives, including performance, physics reach, and cost, constrained by mechanical and geometric limits. This project aims to develop a scalable, distributed AI-assisted detector design for the EIC (AID(2)E), employing state-of-the-art multiobjective optimization to tackle complex designs. Supported by the ePIC software stack and using G EANT 4 simulations, our approach benefits from transparent parameterization and advanced AI features. The workflow leverages the PanDA and iDDS systems, used in major experiments such as ATLAS at CERN LHC, the Rubin Observatory, and sPHENIX at RHIC, to manage the compute intensive demands of ePIC detector simulations. Tailored enhancements to the PanDA system focus on usability, scalability, automation, and monitoring. Ultimately, this project aims to establish a robust design capability, apply a distributed AI-assisted workflow to the ePIC detector, and extend its applications to the design of the second detector (Detector-2) in the EIC, as well as to calibration and alignment tasks. Additionally, we are developing advanced data science tools to efficiently navigate the complex, multidimensional trade-offs identified through this optimization process.
The NNBAR experiment for the European Spallation Source will search for free neutrons converting to antineutrons with a sensitivity improvement of three orders of magnitude compared to the last such search. This paper describes progress towards a conceptual design report for NNBAR. The design of a moderator, neutron reflector, beamline, shielding and annihilation detector is reported. The simulations used form part of a model which will be used for optimisation of the experiment design and quantification of its sensitivity.
The results of a newly developed annular cryogenic phonon-mediated active veto detector are discussed which shows a significant reduction of radioactivity-induced backgrounds in rare event search experiments. The veto detector is made up of germanium weighing ∼500 g with an outer diameter of 76 mm and an inner diameter of 28 mm. The detector can host a 25 mm diameter germanium inner target detector of mass ∼10 g. A GEANT4 simulation with the active veto and inner target detector shows a gamma background reduction of 50 - 80% which is further improved (> 90%) with 4pi veto coverage. Experimental measurements with the detector assembly agree well with the simulation.
Conclusions: Elements other than detector array can heavily impact performance; Must consider a variety of alterations to an imaging array to determine an optimal system.
Current neutrino detectors will observe hundreds to thousands of neutrinos from Galactic supernovae, and future detectors will increase this yield by an order of magnitude or more. With such a data set comes the potential for a huge increase in our understanding of the explosions of massive stars, nuclear physics under extreme conditions, and the properties of the neutrino. However, there is currently a large gap between supernova simulations and the corresponding signals in neutrino detectors, which will make any comparison between theory and observation very difficult. SNEWPY is an open-source software package that bridges this gap. The SNEWPY code can interface with supernova simulation data to generate from the model either a time series of neutrino spectral fluences at Earth, or the total time-integrated spectral fluence. Data from several hundred simulations of core-collapse, thermonuclear, and pair-instability supernovae is included in the package. This output may then be used by an event generator such as sntools or an event rate calculator such as the SuperNova Observatories with General Long Baseline Experiment Simulator (SNOwGLoBES). Additional routines in the SNEWPY package automate the processing of the generated data through the SNOwGLoBES software and collate its output into the observable channels of each detector. In this paper we describe the contents of the package, the physics behind SNEWPY, the organization of the code, and provide examples of how to make use of its capabilities.
The magnetized near detector (ND280) of the T2K long-baseline neutrino oscillation experiment has been recently upgraded aiming to satisfy the requirement of reducing the systematic uncertainty from measuring the neutrino–nucleus interaction cross section, which is the largest systematic uncertainty in the search for leptonic charge-parity symmetry violation. A key component of the upgrade is SuperFGD, a 3D segmented plastic scintillator detector made of approximately 2,000,000 optically-isolated 1 cm 3 cubes. The SuperFGD cube unit shows promising optical performance, including a high light yield of about 40 photoelectrons (p.e.) per channel, a low cube-to-cube crosstalk rate below 3%, and a sub-nanosecond time resolution of 0.96 ns. By combining tracking and stopping power measurements of final state particles, this novel detector enables precise 3D-imaging of GeV neutrino interactions with reduced systematic uncertainties. A detailed Geant4 based optical simulation of the SuperFGD building block, i.e. a plastic scintillating cube read out by three wavelength shifting fibers, has been developed and validated with the different datasets collected in various beam tests. In this manuscript the description of the optical model as well as the comparison with data are reported.
Current neutrino detectors will observe hundreds to thousands of neutrinos from a Galactic supernovae, and future detectors will increase this yield by an order of magnitude or more. With such a data set comes the potential for a huge increase in our understanding of the explosions of massive stars, nuclear physics under extreme conditions, and the properties of the neutrino. However, there is currently a large gap between supernova simulations and the corresponding signals in neutrino detectors, which will make any comparison between theory and observation very difficult. SNEWPY is an open-source software package which bridges this gap. The SNEWPY code can interface with supernova simulation data to generate from the model either a time series of neutrino spectral fluences at Earth, or the total time-integrated spectral fluence. Data from several hundred simulations of core-collapse, thermonuclear, and pair-instability supernovae is included in the package. This output may then be used by an event generator such as sntools or an event rate calculator such as SNOwGLoBES. Additional routines in the SNEWPY package automate the processing of the generated data through the SNOwGLoBES software and collate its output into the observable channels of each detector. In this paper we describe the contents of the package, the physics behind SNEWPY, the organization of the code, and provide examples of how to make use of its capabilities.
A quantitative theoretical framework has been created to model neutral beam injection and fast ion losses in the Wendelstein 7-X (W7-X) stellarator, including a novel method to develop synthetic diagnostics for fast ion loss detectors (FILDs) of many types, such as scintillating and Faraday Cup FILDs. This is the first time that this has been done in stellarator geometry with this level of fidelity, providing a way for fast ion losses to be predicted more precisely in future stellarator experiments and in W7-X. Simulations of the signal seen by a Faraday Cup FILD have been completed for multiple W7-X plasmas and show close agreement with the measured signals. This method is now applied to an actively water-cooled, scintillator-based FILD, which is currently in development to measure the fast ion loss distribution in W7-X in greater detail. The design makes use of a double slit to measure energy-and-pitch-angle-resolved losses of both co-going and counter-going fast ions. The diagnostic, which can be inserted to different radial positions, has been designed to withstand steady-state heat fluxes of up to 120 kW/m 2 along with additional transient heat loads of 100 kW/m 2 lasting for up to 20 s at a time. Simulations of W7-X standard magnetic configuration show up to 8 × 10 13 (s –1 cm –2 ) ion fluxes onto the sensor from each neutral beam source and no signal from the counter-going slit. Finally, these simulations will help inform experimental proposals for future W7-X campaigns after installation of this diagnostic.
Dual phase xenon detectors are widely used in experimental searches for galactic dark matter particles. The origin of single electron backgrounds following prompt scintillation and proportional scintillation signals in these detectors is not fully understood, although there has been progress in recent years. Here, in this paper, we describe single electron backgrounds in 83m Kr calibration events and their correlation with drift and extraction fields, using the Particle Identification in Xenon at Yale (PIXeY) dual-phase xenon time projection chamber. The single electron background induced by the Fowler-Nordheim (FN) effect is measured, and its electric field dependence is quantified. The photoionization of grids and impurities by prompt scintillation and proportional scintillation also contributes to the single electron background.
The performance of the Baby Barrel Electromagnetic Calorimeter (Baby BCAL) — a small-scale lead-scintillating-fiber (Pb/ScFi) prototype of the GlueX Barrel Electromagnetic Calorimeter (BCAL) — was tested in a dedicated beam campaign at the Fermilab Test Beam Facility (FTBF). This study provides a benchmark for the Pb/ScFi component of the future Barrel Imaging Calorimeter (BIC) in the ePIC detector at the Electron-Ion Collider (EIC). The detector response to electrons and pions was studied at beam energies between 4 and 10 GeV, extending previous GlueX tests to a higher energy regime. The calibrated detector exhibits good linearity within uncertainties, and its electron energy resolution meets EIC requirements. The data further constrain the constant term in the energy resolution to below 1.9%, improving upon previous constraints at lower energies. Simulations reproduce key features of the electron and pion data within the limitations of the collected dataset and the FTBF test environment. Electron-pion separation in the test beam setup was analyzed using multiple methods, incorporating varying degrees of beam-related effects. The inclusion of longitudinal shower profile information enhanced the separation performance, underscoring its relevance for the full-scale BIC in ePIC. These results provide essential benchmarks for the Pb/ScFi section of the future BIC, validating detector simulations and guiding optimization strategies for electron-pion discrimination.
Sixteen Medipix2 pixel detector based (MPX) devices were operated at various positions within the ATLAS detector and cavern continuously from early 2008 up to 2013. In addition to photons, each MPX detector is capable to detect charged particles, and neutrons as it is covered with a mask of converter materials dividing its area into regions sensitive to thermal or fast neutrons. The MPX detector network was effectively used for real-time measurements of the spectral characteristics and composition of complex radiation fields in ATLAS. This article reports comparison of the results of measurements performed with MPX detectors during the LHC operation period in 2010 and 2011 with Monte Carlo simulations results from the FLUGG and GCALOR codes. For the purpose of this comparison, the MPX detectors were operated in tracking mode with low threshold (8–10 keV) allowing one to distinguish among particle categories based on the recognition of track patterns left by the particles in the MPX sensitive layer. The comparison of measurements with simulations shows that the agreement between measured and simulated data is satisfactory in most cases within a factor of two.
Neutrinos began as theoretical, massless particles, and since their first detection they have continued to be the subject of various experiments. One such experiment is DUNE, which is a long baseline neutrino experiment with the goal of studying neutrino properties, such as neutrino oscillation parameters. In this work, two projects were completed, one dealing with the hardware of DUNE and the other dealing with neutrino simulations. For the hardware project, we designed a quality control method for testing adaptor boards which make up part of the Far Detector circuit boards. This method was completed and prototyped, however was not implemented as planned due to funding changes. For the simulation project, we simulated ντ , νµ, and νe CC interactions using two simulation methods (stand alone GENIE and a full detector simulation) in order to begin to understand what a ντ CC event would look like in the Far Detector and create an initial list of kinematic properties that can be used for this purpose. We found that there is not a complete understanding of what is occurring in the full detector simulation. We also found that there is promise in using kinematic properties to be able to differentiate ντ CC events, however properties beyond those covered here, as well as a more accurate simulation, will be required for any definitive statistical statements.
We describe a novel application of the end-to-end deep learning technique to the task of discriminating top quark-initiated jets from those originating from the hadronization of a light quark or a gluon. The end-to-end deep learning technique combines deep learning algorithms and low-level detector representation of the high-energy collision event. In this study, we use lowlevel detector information from the simulated CMS Open Data samples to construct the top jet classifiers. To optimize classifier performance we progressively add low-level information from the CMS tracking detector, including pixel detector reconstructed hits and impact parameters, and demonstrate the value of additional tracking information even when no new spatial structures are added. Relying only on calorimeter energy deposits and reconstructed pixel detector hits, the end-to-end classifier achieves a ROC-AUC score of 0.975±0.002 for the task of classifying boosted top quark jets. After adding derived track quantities, the classifier ROC-AUC score increases to 0.9824±0.0013, serving as the first performance benchmark for these CMS Open Data samples.
At millimeter wavelengths, the atmospheric emission is circularly polarized owing to the Zeeman splitting of molecular oxygen by the Earth's magnetic field. We report a measurement of the signal in the 150 GHz band using 3 yr of observational data with the POLARBEAR project. Nonidealities of a continuously rotating half-wave plate (HWP) partially convert circularly polarized light to linearly polarized light. While POLARBEAR detectors are sensitive to linear polarization, this effect makes them sensitive to circular polarization. Although this was not the intended use, we utilized this conversion to measure circular polarization. We reconstruct the azimuthal gradient of the circular polarization signal and measure its dependency from the scanning direction and the detector bandpass. We compare the signal with a simulation based on atmospheric emission theory, the detector bandpass, and the HWP leakage spectrum model. We find the ratio of the observed azimuthal slope to the simulated slope is 0.92 ± 0.01(stat) ± 0.07(sys). This ratio corresponds to a brightness temperature of 3.8 mK at the effective band center of 121.8 GHz and bandwidth of 3.5 GHz estimated from representative detector bandpass and the spectrum of Zeeman emission. This result validates our understanding of the instrument and reinforces the feasibility of measuring the circular polarization using the imperfection of continuously rotating HWP. Continuously rotating HWP is popular in ongoing and future cosmic microwave background experiments to modulate the polarized signal. This work shows a method for signal extraction and leakage subtraction that can help measure circular polarization in such experiments.
The design of microwave cavity detectors for axion dark matter research is often accomplished using advanced full-wave electromagnetic simulation software tools. These tools provide a cost-effective approach to evaluate a wide variety of cavity configurations, frequency tuning mechanisms, and conductive or dielectric materials and coatings. One simulation software package used for this application is Ansys High Frequency Structure Simulator (HFSS), which is based upon the well-established finite element method. HFSS includes numerous features useful for microwave cavity design such as parametric geometry modeling, adaptive meshing algorithm, curvilinear mesh elements, driven modal and eigenmode matrix solvers, and optimization algorithms. This paper describes the use of the HFSS software to simulate microwave cavities for axion haloscope detectors, with an example tutorial for a cylindrical cavity. Excellent agreement between the simulated and analytical results is shown for the resonant frequency, quality factor, and form factor.
The Short Baseline Near Detector (SBND) is a liquid argon time projection chamber (LArTPC) located 110 meters downstream of the Booster Neutrino Beam target at Fermilab \cite{SBN}. The LArTPC is a detector technology with unprecedented position and calorimetric resolution for precise neutrino studies. The most dominant neutrino interaction topology in SBND is charged-current quasi-elastic muon neutrino-argon interactions, where the final state consists of a muon and a proton. Here, muons often travel long distances, sometimes exiting the detector before stopping. This loss of information creates a challenge for accurate energy estimation. There have been efforts in the MicroBooNE and ICARUS collaborations to improve energy estimation using a phenomenon known as Multiple Coulomb Scattering (MCS), which describes the jagged path taken by muons through a detector due to continuous Coulomb interactions with argon atoms. Building on these efforts, my project aims to improve an MCS-based approach to estimating the energies of exiting muons in SBND.
In this article, we report the growth of Cd 0.9 Zn 0.1 Te 0.97 Se 0.03 (CZTS) wide bandgap semiconductor single crystals for room temperature gamma-ray detection using a modified vertical Bridgman method. Charge transport properties measured in the radiation detectors, fabricated from the grown CZTS crystals, indicated signs of hole trapping. Hole traps inhibit high-resolution radiation detection especially for energetic gamma rays. Machine learning (ML) applications are gaining tremendous mpetus in improving device and sensor performance by compensating for limi tations arising from such intrinsic material properties. In this article, we describe a deep convolutional neural network (CNN) that has demonstrated remarkable efficiency in identifying the energy of a gamma photon detected by a CZTS detector. The CNN has been trained using simulated data that resemble output pulses from actual CZTS detectors when exposed to 662-keV gamma photons. The device properties required for the simulation have been derived from radiation detection measurements on a real Cd 0.9 Zn 0.1 Te 0.97 Se 0.03 detector fabricated in our laboratory. The CNN has been trained with detector pulses arising through photoelectric (PE) and Compton scattering (CS) separately. The percentage error in predicting the detected energies, within an extremely small duration of 0.28 ms, was found to be lower than 0.1% for gamma energies above 50 keV and for training datasets con taining PE and CS events separately. The CNN was also validated for a mixed PE and CS dataset to obtain a prediction error of 1%. Additionally, the effect of detector resolution on the efficiency of the CNN was also explored.