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Scoping Study: Scintillator Array for Fission Correlation Studies
This scoping study uses a simulation based on the PROSPECT (1) detector to assess the conceptual design of a modular system that could efficiently and simultaneously measure the Prompt Fast Neutron Spectrum (PFNS), the prompt fission neutron multiplicity, the prompt fission gamma-ray multiplicity and total energy, as well as temporal and angular correlations between the detected particles. The proposed experiment would require a mono-energetic pulsed neutron beam incident on an actinide target in the center of the detector. Beam induced backgrounds due to elastic and inelastic neutron scattering on the target are considered, while other backgrounds are ignored for this study.
Radiation Design of Ion Mass Spectrometers
In the harsh radiation environment of Jupiter and with the JUpiter ICy moon Explorer (JUICE) mission including two Europa flybys where local intensities are approx. 150 krad/month behind 100 mils of Al shielding, so background from penetrating radiation can be a serious issue for detectors inside an Ion Mass Spectrometer (IMS). This can especially be important for minor ion detection designs. Detectors of choice for time-of-flight (TOF) designs are microchannel plates (MCP) and some designs may include solid state detectors (SSD). The standard approach is to use shielding designs so background event rates are low enough that the detector max rates and lifetimes are first not exceeded and then the more stringent requirement that the desired measurement can successfully be made (i.e., desired signal is sufficiently greater than background noise after background subtraction is made). GEANT codes are typically used along with various electronic techniques, but such designs need to know how the detectors will respond to the simulated primary and secondary radiations produced within the instrument. We will be presenting some preliminary measurements made on the response of MCPs to energetic electrons (20 ke V to 1400 ke V) using a Miniature TOF (MTOF) device and the High Energy Facility at Goddard Space Flight Center which has a Van de Graaff accelerator.
Smart pixel sensors: towards on-sensor filtering of pixel clusters with deep learning
Highly granular pixel detectors allow for increasingly precise measurements of charged particle tracks. Next-generation detectors require that pixel sizes will be further reduced, leading to unprecedented data rates exceeding those foreseen at the High- Luminosity Large Hadron Collider. Signal processing that handles data incoming at a rate of $\mathcal{O}$(40 MHz) and intelligently reduces the data within the pixelated region of the detector at rate will enhance physics performance at high luminosity and enable physics analyses that are not currently possible. Using the shape of charge clusters deposited in an array of small pixels, the physical properties of the traversing particle can be extracted with locally customized neural networks. In this first demonstration, we present a neural network that can be embedded into the on-sensor readout and filter out hits from low momentum tracks, reducing the detector's data volume by 57.1%–75.7%. The network is designed and simulated as a custom readout integrated circuit with 28 nm CMOS technology and is expected to operate at less than 300 μW with an area of less than 0.2 mm 2 . The temporal development of charge clusters is investigated to demonstrate possible future performance gains, and there is also a discussion of future algorithmic and technological improvements that could enhance efficiency, data reduction, and power per area.
Characterization of Single-Sided Charge-Sharing CZT Strip Detectors for Gamma-Ray Astronomy
We report progress in the study of thick single-sided charge-sharing cadmium zinc telluride (CZT) strip detector modules designed to perform spectroscopy and 3-D imaging of gamma-rays. We report laboratory measurements including spectroscopy, efficiency and 3-D imaging capability of prototype detectors (15 15 7.5 cu mm) with 11x11 unit cells. We also report on Monte Carlo simulations (GEANT4 v7.1) to investigate the effect of multihits on detector performance in both spectroscopy and imaging. We compare simulation results with data obtained from laboratory measurements and discuss the implications for future strip detector designs. Keywords: CZT, strip detectors, gamma-ray
Smart pixel sensors Towards on-sensor filtering of pixel clusters with deep learning
High granularity silicon pixel sensors are at the heart of energy frontier particle physics collider experiments. At an collision rate of 40\,MHz, these detectors create massive amounts of data. Signal processing that handles data incoming at those rate and intelligently reduces the data within the pixelated region of the detector \textit{at rate} will enhance physics performance and enable physics analyses that are not currently possible. Using the shape of charge clusters deposited in an array of small pixels, the physical properties of the traversing particle can be extracted with locally customized neural networks. In this first work, we present a neural network that can be embedded into the on-sensor readout and filter out hits from low momentum tracks, reducing the detector's data volume by 54.4-75.4\%. The network is designed and simulated as a custom readout integrated circuit with 28\,nm CMOS technology and is expected to operate at less than 300\,$\mu W$ with an area of less than 0.2\,mm$^2$.
A position sensitive phoswich hard X-ray detector system
A prototype position sensitive phoswich hard X-ray detector, designed for eventual astronomical usage, was tested in the laboratory. The scintillation crystal geometry was designed on the basis of a Monte Carlo simulation of the internal optics and includes a 3mm thick NaI(T1) primary X-ray detector which is actively shielded by a 20 mm thick CsI(T1) scintillation crystal. This phoswich arrangement is viewed by a number two inch photomultipliers. Measured values of the positional and spectral resolution of incident X-ray photons are compared with calculation.
SOLPOL: A Solar Polarimeter for Hard X-Rays and Gamma-Rays
Th goal of this project was to continue the development of a hard X-ray polarimeter for studying solar flares. In earlier work (funded by a previous SR&T grant), we had already achieved several goals, including the following: 1) development of a means of producing a polarized radiation source in the lab that could be used for prototype development; 2) demonstrated the basic Compton scatter polarimeter concept using a simple laboratory setup; 3) used the laboratory results to verify our Monte Carlo simulations; and 4) investigated various detector technologies that could be incorporated into the polarimeter design. For the current one-year program, we wanted to fabricate and test a laboratory science model based on our SOLPOL (Solar Polarimeter) design. The long-term goal of this effort is to develop and test a prototype design that could be used to study flare emissions from either a balloon- or space-borne platform. The current program has achieved its goal of fabricating and testing a science model of the SOLPOL design, although additional testing of the design (and detailed comparison with Monte Carlo simulations) is still desired. This one-year program was extended by six months (no-cost extension) to cover the summer of 1999, when undergraduate student support was available to complete some of the laboratory testing.
Modeling backgrounds for the majorana demonstrator
The MAJORANA DEMONSTRATOR is a neutrinoless double-beta decay (0?ß ß ) experiment containing ~30 kg of p-type point contact germanium detectors enriched to 88% in 76Ge and ~14 kg of natural germanium detectors. The detectors are housed in two electroformed copper cryostats and surrounded by a graded passive shield with active muon veto. An extensive radioassay campaign was performed prior to installation to insure the use of ultra-clean materials. The DEMONSTRATOR achieved one of the lowest background rates in the region of the 0?ß ß Q-value, 15.7 ± 1.4 cts/(FWHM t y) from the low-background configuration spanning most of the 64.5 kg-yr active exposure. Nevertheless this background rate is a factor of five higher than the projected background rate. This discrepancy arises from an excess of events from the 232Th decay chain. Background model fits aim to understand this deviation from assay-based projections, potentially determine the source(s) of observed backgrounds, and allow a precision measurement of the two-neutrino double-beta decay half-life. The fits agree with earlier simulation studies, which indicate the origin of the 232Th excess is not from a near-detector component and have informed design decisions for the next-generation LEGEND experiment. Recent findings have narrowed the suspected locations for the excess activity, motivating a final simulation and assay campaign to complete the background model.
A science-driven approach to optimize the design for a biological small-angle neutron scattering instrument
Biological small-angle neutron scattering (SANS) instruments facilitate critical analysis of the structure and dynamics of complex biological systems. However, with the growth of experimental demands and the advances in optical systems design, a new neutron optical concept is necessary to overcome the limitations of current instruments. This work presents an approach to include experimental objectives ( i.e. the science to be supported by a specific neutron scattering instrument) in the optimization of the neutron optical concept. The approach for a proposed SANS instrument at the Second Target Station of the Spallation Neutron Source at Oak Ridge National Laboratory, USA, is presented here. Further, the instrument is simulated with the McStas software package. The optimization process is driven by an evolutionary algorithm using McStas output data, which are processed to calculate an objective function designed to quantify the expected performance of the simulated neutron optical configuration for the intended purpose. Each McStas simulation covers the complete instrument, from source to detector, including realistic sample scattering functions. This approach effectively navigates a high-dimensional parameter space that is otherwise intractable; it allows the design of next-generation SANS instruments to address specific scientific cases and has the potential to increase instrument performance compared with traditional design approaches.
Artificial Intelligence for imaging Cherenkov detectors at the EIC
Abstract Imaging Cherenkov detectors form the backbone of particle identification (PID) at the future Electron Ion Collider (EIC). Currently all the designs for the first EIC detector proposal use a dual Ring Imaging CHerenkov (dRICH) detector in the hadron endcap, a Detector for Internally Reflected Cherenkov (DIRC) light in the barrel, and a modular RICH (mRICH) in the electron endcap. These detectors involve optical processes with many photons that need to be tracked through complex surfaces at the simulation level, while for reconstruction they rely on pattern recognition of ring images. This proceeding summarizes ongoing efforts and possible applications of AI for imaging Cherenkov detectors at EIC. In particular we will provide the example of the dRICH for the AI-assisted design and of the DIRC for simulation and particle identification from complex patterns and discuss possible advantages of using AI.
Celeritas: GPU-accelerated particle transport for detector simulation in High Energy Physics experiments
Within the next decade, experimental High Energy Physics (HEP) will enter a new era of scientific discovery through a set of targeted programs recommended by the Particle Physics Project Prioritization Panel (P5), including the upcoming High Luminosity Large Hadron Collider (LHC) HL-LHC upgrade and the Deep Underground Neutrino Experiment (DUNE). These efforts in the Energy and Intensity Frontiers will require an unprecedented amount of computational capacity on many fronts including Monte Carlo (MC) detector simulation. In order to alleviate this impending computational bottleneck, the Celeritas MC particle transport code is designed to leverage the new generation of heterogeneous computer architectures, including the exascale computing power of U.S. Department of Energy (DOE) Leadership Computing Facilities (LCFs), to model targeted HEP detector problems at the full fidelity of Geant4. This paper presents the planned roadmap for Celeritas, including its proposed code architecture, physics capabilities, and strategies for integrating it with existing and future experimental HEP computing workflows.
Computer simulation study of multiple germanium gamma-ray sensor arrays
Design studies of large high resolution gamma-ray spectrometer systems employing multiple sensors are greatly facilitated by the use of computer simulation techniques. A study has been made by interfacing a detector geometry code with the EGS-3 version of the Stanford Linear Accelerator Center Monte-Carlo Code which has been modified to run on our VAX computer. The detector responses have been simulated for input photons with energies between 0.1 and 10 MeV. Examples are given to illustrate the performance of complex sensor arrays with different configurations of the anticoincidence system. Of particular interest is a design composed of an array of 7 detector elements each consisting of a thin planar Ge (1 cm thick) detector mounted in front of and in tandem with a large coaxial Ge detector.
Developing Advanced Charge Readout Techniques for nEXO and Future Liquid Xenon OVBB Detectors (Final Report)
This award supported the Yale group’s efforts on nEXO, which included the following: 1) Developed techniques for measuring the outgassing of materials into liquid xenon (LXe) and a model for predicting the electron lifetime in nEXO and future LXe TPCs 2) Work to develop the conceptual design for the nEXO Photon Detector subsystem and TPC subsystem interconnections 3) Developed simulation of charge and light propagation and readout in nEXO.
Next Generation Sensor Development (CRADA Final Report)
B. Project Scope This was a collaborative effort between Lawrence Livermore National Security, LLC (LLNS), as manager and operator of Lawrence Livermore National Laboratory (LLNL) and The Federal Reserve Bank of San Francisco ("FRBSF" or "Participant"), to research and develop advanced processing techniques to measure the fitness and authenticity of U.S. currency. The FRBSF previously partnered with LLNL to evaluate end-of-life predictions, review sensor requirements, develop communications protocols, and perform thermal analysis. These Strategic Partnership Projects (SPP) included SPP No. L20960 - Federal Reserve Currency Technology Office Studies, SPP No. L15900 - Development of a Common Detector Interface (CDI 2.0) Specification and Hardware Simulators, and SPP No. L15610 - Limited Design Review of a Second-Generation E-Material Authentication Sensor (EMAS2).
Machine Learning-Based Technique for Automated Sensor Characterization
The development of novel instrumentation requires an iterative cycle with three stages: design, prototyping, and testing. Recent advancements in simulation and nanofabrication techniques have significantly accelerated the design and prototyping phases. Nonetheless, detector characterization continues to be a major bottleneck in device development. During the testing phase, a significant time investment is required to characterize the device in different operating conditions and find optimal operating parameters. The total effort spent on characterization and parameter optimization can occupy a year or more of an expert s time. In this work, we present a novel technique for automated sensor calibration that aims to accelerate the testing stage of the development cycle. This technique leverages closed-loop Bayesian optimization (BO), using real-time measurements to guide parameter selection and identify optimal operating states. We demonstrate the method with a novel low-noise CCD, showing that the machine learning-driven tool can efficiently characterize and optimize operation of the sensor in a couple of days without supervision of a device expert.
Automating Sensor Characterization with Bayesian Optimization
The development of novel instrumentation requires an iterative cycle with three stages: design, prototyping, and testing. Recent advancements in simulation and nanofabrication techniques have significantly accelerated the design and prototyping phases. Nonetheless, detector characterization continues to be a major bottleneck in device development. During the testing phase, a significant time investment is required to characterize the device in different operating conditions and find optimal operating parameters. The total effort spent on characterization and parameter optimization can occupy a year or more of an expert's time. In this work, we present a novel technique for automated sensor calibration that aims to accelerate the testing stage of the development cycle. This technique leverages closed-loop Bayesian optimization (BO), using real-time measurements to guide parameter selection and identify optimal operating states. We demonstrate the method with a novel low-noise CCD, showing that the machine learning-driven tool can efficiently characterize and optimize operation of the sensor in a couple of days without supervision of a device expert.
Exclusive J/$\psi$ detection and physics with ECCE
The EIC Comprehensive Chromodynamics Experiment (ECCE) detector has been recommended as a reference design for the proposed Electron-Ion Collider (EIC) program. Here, this paper presents simulation studies of exclusive J/$\psi$ detection and selected physics impact results in EIC using the projected ECCE detector concept. Exclusive quarkonium photoproduction is one of the most popular processes in EIC, which has a large cross section and a simple final state. Due to the gluonic nature of the exchange Pomeron, this process can be related to the gluon distributions in the nucleus. Preliminary results estimate the excellent statistics benefited from the large cross section of J/$\psi$ photoproduction and superior performance of ECCE detector concept. The precise measurement of exclusive J/$\psi$ photoproduction at EIC will help us to more deeply understand nuclear gluon distributions, near threshold production mechanism and nucleon mass structure.