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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

High-Frequency Gravitational Wave Search with ABRACADABRA-10 cm

ABRACADABRA-10 cm has had great success as a pathfinder lumped-element axion dark matter experiment, setting limits on axion dark matter at the GUT scale. Now, using the interaction of gravitational waves with electrodynamics and a change in readout strategy, we use the ABRA-10 cm detector for the first search for high-frequency gravitational waves using a modified axion detector. Potential sources at these high frequencies (10 kHz to 5 MHz) include merging primordial black hole binaries or superradiance, among other beyond the standard model phenomena. This paper presents the design, results, and challenges from the ABRA-10 cm high-frequency gravitational wave search, showing it is possible to simultaneously look for axions and high-frequency gravitational waves with both searches matching theoretical expectations for sensitivity. Additionally, we conducted the first time series transient search with data from an axion experiment, achieving sensitivity to $10^{-4}$ in strain. Scaled directly to the next generation axion experiment, DMRadio-GUT, this sensitivity would imply a reach to 0.01 $M_{\odot}$ primordial black hole mergers at distances around 3 pc, with prospects to go significantly further with modifications to the readout.

Pappas, Kaliroë M.W. [MIT, LNS] (ORCID:00000003425↗
DOE OSTI2024

ADRIANO2 Calorimeter performance from 2022 Prototypes

A novel high-granularity dual-readout calorimetric technique was developed as part of the T1604 collaboration research program. The building block of ADRIANO2 Calorimeter consists of a pair of optically isolated, small sized tiles made of scintillating plastic and lead glass. The two components of the dual-readout energy compensation technique. Furthermore,Cˇerenkovlightfromtheleadglassareexploitedtoforhighresolutiontimingmeasurements,whilehigh granularity from scintillating plastic can be used to probe the spatial component of the particle shower. This setup works for excellent energy resolution and particle identification for REDTOP as it is crucial for a calorimeter to detect and identify the decay products of eta/eta-prime mesons. Measurements were collected on several ADRIANO2 prototypes between February to December 2022 to evaluate the detector performance. The key metrics extracted from the analyses are presented in this paper are the efficiencies and light-yi eld for various tile configurations. The measured values will be used as input parameters for improved REDTOP Montecarlo simulation.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Smart Pixels: In-pixel AI for on-sensor data filtering

We present a smart pixel prototype readout integrated circuit (ROIC) designed in CMOS 28 nm bulk process, with in-pixel implementation of an artificial intelligence (AI) / machine learning (ML) based data filtering algorithm designed as proof-of-principle for a Phase III upgrade at the Large Hadron Collider (LHC) pixel detector. The first version of the ROIC consists of two matrices of 256 smart pixels, each 25$\times$25 $\mu$m$^2$ in size. Each pixel consists of a charge-sensitive preamplifier with leakage current compensation and three auto-zero comparators for a 2-bit flash-type ADC. The frontend is capable of synchronously digitizing the sensor charge within 25 ns. Measurement results show an equivalent noise charge (ENC) of $\sim$30e$^-$ and a total dispersion of $\sim$100e$^-$ The second version of the ROIC uses a fully connected two-layer neural network (NN) to process information from a cluster of 256 pixels to determine if the pattern corresponds to highly desirable high-momentum particle tracks for selection and readout. The digital NN is embedded in-between analog signal processing regions of the 256 pixels without increasing the pixel size and is implemented as fully combinatorial digital logic to minimize power consumption and eliminate clock distribution, and is active only in the presence of an input signal. The total power consumption of the neural network is $\sim$ 300 $\mu$W. The NN performs momentum classification based on the generated cluster patterns and even with a modest momentum threshold, it is capable of 54.4% - 75.4% total data rejection, opening the possibility of using the pixel information at 40MHz for the trigger. The total power consumption of analog and digital functions per pixel is $\sim$ 6 $\mu$W per pixel, which corresponds to $\sim$ 1 W/cm$^2$ staying within the experimental constraints.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Effects of Proton Irradiation on the Performance of Skipper CCDs

Skipper CCDs are a mature detector technology that has been suggested for future space telescope instruments requiring sub-electron readout noise in the near-ultraviolet to the near-infrared. While modern skipper CCDs inherit from the radiation-tolerant p-channel detectors developed by LBNL, the effects of high doses of ionizing radiation on skipper CCDs (such as those expected in space) remains largely unmeasured. We report preliminary results on the performance of p-channel skipper CCDs following irradiation with 217-MeV protons at the Northwestern Medicine Proton Center. The total nonionizing energy loss (NIEL) experienced by the detectors exceeds 6 years at the Sun-Earth Lagrange Point 2 (L2). We demonstrate that the skipper amplifier continues to function as expected following this irradiation. Owing to the low readout noise of these detectors, controlled irradiation tests can be used to sensitively characterize the charge transfer inefficiency, dark current, and the density and time constants of charge traps as a function of proton fluence. We conclude with a brief outlook toward future tests of these detectors at other proton and gamma-ray facilities.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

TinyTPC: A TEST STAND FOR LAr DOPING

TinyTPC is a small scale liquid-argon time projection chamber (LArTPC) that studies how photosensitive dopants can improve the energy resolution of low energy events in LArTPCs. Bench tests to resolve high voltage breakdown issues and to improve the low voltage system readout are described. Experimental data with a radioactive source of Th-228 for the calibration of the pixelated readout and the characterization of TinyTPC's energy resolution is explained. Provisional results from the addition of isobutylene (photosensitive dopant) and xenon (wavelength shifter) to liquid-argon (LAr) are also presented. Further steps to improve voltage system and enhance the data collection are discussed.

Rodriguez Thorne, Paloma↗
DOE OSTI2024

Real-time Anomaly Detection at the L1 Trigger of CMS Experiment

We present the preparation, deployment, and testing of an autoencoder trained for unbiased detection of new physics signatures in the CMS experiment Global Trigger (GT) test crate FPGAs during LHC Run 3. The GT makes the final decision whether to readout or discard the data from each LHC collision, which occur at a rate of 40 MHz, within a 50 ns latency. The Neural Network makes a prediction for each event within these constraints, which can be used to select anomalous events for further analysis. The GT test crate is a copy of the main GT system, receiving the same input data, but whose output is not used to trigger the readout of CMS, providing a platform for thorough testing of new trigger algorithms on live data, but without interrupting data taking. We describe the methodology to achieve ultra low latency anomaly detection, and present the integration of the DNN into the GT test crate, as well as the monitoring, testing, and validation of the algorithm during proton collisions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Intelligent Pixel Detectors: Towards a Radiation Hard ASIC with On-Chip Machine Learning in 28 nm CMOS

Detectors at future high energy colliders will face enormous technical challenges. Disentangling the unprecedented numbers of particles expected in each event will require highly granular silicon pixel detectors with billions of readout channels. With event rates as high as 40 MHz, these detectors will generate petabytes of data per second. To enable discovery within strict bandwidth and latency constraints, future trackers must be capable of fast, power efficient, and radiation hard data-reduction at the source. We are developing a radiation hard readout integrated circuit (ROIC) in 28nm CMOS with on-chip machine learning (ML) for future intelligent pixel detectors. We will show track parameter predictions using a neural network within a single layer of silicon and hardware tests on the first tape-outs produced with TSMC. Preliminary results indicate that reading out featurized clusters from particles above a modest momentum threshold could enable using pixel information at 40 MHz.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Sensor Co-design for $\textit{smartpixels}$

Pixel tracking detectors at upcoming collider experiments will see unprecedented charged-particle densities. Real-time data reduction on the detector will enable higher granularity and faster readout, possibly enabling the use of the pixel detector in the first level of the trigger for a hadron collider. This data reduction can be accomplished with a neural network (NN) in the readout chip bonded with the sensor that recognizes and rejects tracks with low transverse momentum (p$_T$) based on the geometrical shape of the charge deposition (``cluster''). To design a viable detector for deployment at an experiment, the dependence of the NN as a function of the sensor geometry, external magnetic field, and irradiation must be understood. In this paper, we present first studies of the efficiency and data reduction for planar pixel sensors exploring these parameters. A smaller sensor pitch in the bending direction improves the p$_T$ discrimination, but a larger pitch can be partially compensated with detector depth. An external magnetic field parallel to the sensor plane induces Lorentz drift of the electron-hole pairs produced by the charged particle, broadening the cluster and improving the network performance. The absence of the external field diminishes the background rejection compared to the baseline by $\mathcal{O}$(10%). Any accumulated radiation damage also changes the cluster shape, reducing the signal efficiency compared to the baseline by $\sim$ 30 - 60%, but nearly all of the performance can be recovered through retraining of the network and updating the weights. Finally, the impact of noise was investigated, and retraining the network on noise-injected datasets was found to maintain performance within 6% of the baseline network trained and evaluated on noiseless data.

Shekar, Danush [Illinois U., Chicago]↗
DOE OSTI2025

Enhancing ICARUS and REDTOP Software and Hardware: Event Generator Interface Development and Calorimeter Tile Prototype

ICARUS (Imaging Cosmic And Rare Underground Signals) is a liquid argon time projection chamber (LArTPC) detector that pursues the sterile neutrino, which relies on accurate simulations of neutrino-argon interactions. REDTOP (Rare Eta Decays To Observe new Physics) is a proposed low-energy, high-intensity meson factory designed to explore rare $\eta$/$\eta'$ meson decays and probe physics beyond the Standard Model. As a next-generation experiment, this requires both accurate simulations and innovative detector technologies. This project contributes to both ICARUS, from a simulation perspective, and REDTOP, from both a simulation and detection perspective, through the event generation of lepton-nucleon interactions and the physical enhancement of the calorimeter technology within the REDTOP detector. We developed an interface between ACHILLES (A CHIcago Land Lepton Event Simulator), a theory-driven lepton-level event generator, and GENIE, a robust event generator framework used for neutrino physics. By incorporating the precise theoretical cross-section calculations of ACHILLES into the experimental realism of GENIE, the interface allows for improved accuracy of neutrino-nucleon simulations, which can be adapted for the proton beam specifications of the REDTOP meson factory as well as for the ICARUS experiment. In parallel, we developed an improved prototype for the ADRIANO2 (A Dual Readout Integrally Active Non-segmented Option) dual-readout calorimeter tiles for the REDTOP detector. To improve the efficiency of the lead-glass tiles trapping Cherenkov light for energy reconstruction and particle identification, we optimized the application of a highly reflective coating. Through viscosity and thickness control, masking, and a custom spray technique, we refined the coating process to reduce surface defects and improve light yield. Together, these efforts strengthen the ICARUS neutrino program and REDTOP's capability of detecting rare decay events.

Visser, Erin [Michigan State U.] (ORCID:0009000184↗
DOE OSTI2025

Setup and Testing of PSEC5 ASIC Boards

The PSEC5 ASIC is a high-speed waveform sampling chip developed for precise measurements. This report presents the design review, test board configurations, and results from functional validation of the PSEC5 device. Key aspects examined include power integrity, voltage-controlled oscillator (VCO) behavior, SPI register functionality, clock division characteristics, readout performance, and analog biasing response. Tests were conducted across two boards under varied voltage conditions and signal configurations. Notable observations include robust VCO output, SPI communication irregularities, and anomalous readout conditions. The findings support design iterations for improved ASIC operability and firmware integration.

Fahey, Alexander [North Central Coll.]↗
DOE OSTI2026

Surrogate Neural Architecture Codesign Package (SNAC-Pack)

Neural architecture search (NAS) is a powerful approach for automating model design, but existing methods often optimize for accuracy alone or rely on proxy metrics such as bit operations (BOPs) that correlate poorly with hardware cost. This gap is particularly large for FPGA deployment, where cost is dominated by a multi-dimensional budget of lookup tables, DSPs, flip-flops, BRAM, and latency. We present the Surrogate Neural Architecture Codesign Package (SNAC-Pack), an open-source AutoML framework for hardware-aware neural architecture codesign and end-to-end FPGA deployment. SNAC-Pack runs a multi-objective global search with Optuna and NSGA-II, loading trials to a shared SQLite store that enables parallel workers across compute nodes. A hardware surrogate model outputs per-trial resource and latency estimates, avoiding the synthesis cost that would otherwise dominate the search loop. A local search stage then applies quantization-aware training (QAT) together with iterative magnitude pruning in a combined compression loop, after which the final model is synthesized to FPGA firmware via the hls4ml Python library. A YAML configuration and an optional agentic frontend let users run the pipeline on new datasets without modifying the framework. We demonstrate SNAC-Pack on jet classification at the Large Hadron Collider and superconducting qubit readout, discovering compact architectures that match or exceed strong baselines on the task metric while reducing FPGA resource utilization and, in the qubit readout case, reducing the design space exploration process from months of manual fine-tuning to hours of automated search.

Weitz, Jason [UC, San Diego]↗
DOE OSTI2026

Long-term stability-degradation analysis of DUNE SiPMs in Liquid Nitrogen

We developed a cryogenic photon readout system to monitor arrays of DUNE SiPMs operating at various over-voltages in liquid nitrogen (LN2) for over three months. Photoelectron signals were read out simultaneously via weak capacitive coupling to the micro-FEMB, a 32-channel charge sensitive readout board designed for 77 K to 300 K operation in liquid argon time projection chambers (LAr-TPCs). A waveform snippet acquisition scheme was implemented to capture waveform signals whenever they exceeded a predetermined trigger level; consequently, empty waveforms were discarded. Selected SiPM parameters were monitored to detect any deviations beyond statistical fluctuations. While some parameters exhibited ~1σ variations over the 3-month test period, there was no evidence of drift being enhanced when operating at 4 V or 5 V compared to a 3 V over-voltage. However, a discernible PDE drop was observed across all channels, which warrants further investigation. In addition, we demonstrated that our system can simultaneously read out single photons from a selected group of SiPMs at room temperature.

Tsang, Thomas [Brookhaven]↗
DOE OSTI2026

Quality Control of Silicon Sensor Modules for Particle Detectors

The High-Luminosity Large Hadron Collider (HL-LHC) will produce a higher rate of particle collisions than the current Large Hadron Collider (LHC), requiring significant upgrades to the Compact Muon Solenoid (CMS) to handle the increased amount of data. An important upgrade is the Phase-2 Outer Tracker Upgrade, which consists of 13,000 silicon sensor modules made of two parallel silicon sensors and readout electronics. These modules undergo careful quality control checks both during and after module assembly to ensure precise and reliable detector performance. This project focuses on precision testing for quality control of silicon sensor modules at Fermilab. Hands-on work includes visual inspection, current-voltage testing, module testing, and ultraviolet (UV) light exposure of modules showing abnormal current-voltage behavior. The ultraviolet exposure process improves the abnormal sensor readout data by placing the selected sensor side of the module directly under the UV light inside a controlled box. In addition to laboratory testing and ultraviolet experiments, I developed a Python-based data tool that connects to a module database and allows selected testing conditions and module information to be retrieved and displayed efficiently. These different testing procedures, experimental processes, and computational tools support the broader goal of identifying module issues and improving modules that will be used in the CMS Outer Tracker Phase-2 Upgrade.

Siddiqui, Hooriya [DuPage Coll.; Fermilab] (ORCID:↗
DOE OSTI2026

Graphical User Interface for a Biasing Board for the PSEC6

The PSEC6 is an application-specific integrated circuit (ASIC) designed for a readout system for a large area picosecond photodetector (LAPPD). The PSEC6 is currently in fabrication and pending testing. The testing system for the PSEC5, the previous iteration of the ASIC, required expensive and non-portable equipment, because the ASIC needs twelve adjustable reference voltages. The new testing system consists of an low-cost, open-source, cross-platform graphical user interface (GUI), a digital system, and a biasing board. The digital system is the interface between the GUI and biasing board, and can be implemented on a microcontroller or field-programmable gate array (FPGA). The biasing board contains twelve digital-to-analog converters (DACs) that are configurable via the GUI, which gives users the ability to write voltage values to all or specific DACs. The GUI was developed in C on Linux using the widget library GTK4 and cross-compiled for Windows compatibility. I2C and SPI protocols were implemented on an Adafruit Feather ESP32-S3 microcontroller to write commands to the DACs and PSEC6. A hardware implementation of the I2C protocol is in development on an FPGA. Since LAPPDs will be used by the Accelerator Neutrino Neutron Interaction Experiment (ANNIE) at Fermilab, the PSEC6 testing system in this internship project can potentially benefit future neutrino research. The project is relevant to the Department of Energy’s microelectronics mission, because the PSEC6 is an ASIC that will handle fast time signals arriving from the detector for readout. It also provided experience with building a cross-platform user interface, practicing digital design and implementation in hardware description language (HDL), and using simulations to inform new design iterations.

Guerrero, Sasha Camila [North Central Coll.]↗
DOE OSTI2025

Online and Offline Data Quality Monitoring for the Mu2e Calorimeter

This thesis presents the design, implementation, and validation of a calorimeter Data Quality Monitoring (DQM) toolchain for the Mu2e experiment at Fermilab. Mu2e searches for charged lepton flavor violation via coherent muon-to-electron conversion in the field of an aluminum nucleus, $\mu^- Al \rightarrow e^-Al$, a process whose observation would constitute clear evidence of physics beyond the Standard Model. Achieving target sensitivity requires stringent control of detector performance and data integrity during acquisition, as subtle issues in readout configuration, data formatting, or electronics behavior can compromise reconstruction and bias downstream analyzes. To address these challenges, this work develops a multi-layer DQM approach spanning both raw data validation and reconstructed digi-level diagnostics. At the low level, a fragment analysis component performs word- and bit-field decoding of calorimeter readout blocks, enabling sanity checks of the expected structure and producing detailed error and integrity statistics useful for commissioning and troubleshooting. At the digi level, the CaloDigiDQM analyzer is implemented within the art framework and transforms each CaloDigiCollection into a structured hierarchy of ROOT histograms designed for fast drill-down diagnostics. The module generates coherent monitoring views at global, disk, board, and channel granularity, including occupancy, waveform-derived features (baseline, RMS, peak amplitude and position), and left-right sensor consistency metrics. Detector-aware channel-to-electronics mapping is performed through the conditions system (CaloDAQMap), ensuring that diagnostics remain aligned with hardware identifiers used in operations. For end-to-end testing without reliance on live DAQ data, a synthetic CaloDigi producer is developed to generate realistic waveforms with controlled noise and pulse shapes. The resulting system supports both offline ROOT-file production and online operation, including optional histogram streaming through otsdaq via ots::HistoSender. This toolchain provides a practical and scalable foundation for calorimeter commissioning and stable data collection, enabling early detection of anomalies and reducing operational risk for Mu2e.

Vakulenko, Mark [Drew U.] (ORCID:0009000276197818)↗
DOE OSTI2026

TTDAQ: A Continuous Flow, Timing and Trigger DAQ System

Final Scientific/Technical Report for DOE Award DE-SC0019581, “TTDAQ: A Continuous Flow, Timing and Trigger DAQ System.” The report summarizes Telluric Labs’ Phase II STTR work developing silicon-photonic building blocks for a software-defined, continuous-flow, trigger-less data acquisition system for next-generation high-energy and nuclear-physics detectors. The project focused on radiation-hard photonic integrated circuits, remote optical illumination, dense wavelength-division multiplexing, and a differential microring-resonator transceiver architecture designed to improve high-speed optical link stability and bandwidth. The report describes project objectives, technical accomplishments, AIM Photonics tape-outs, bench characterization, radiation-hardness testing, deferred integration work, and potential applications beyond physics readout.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

A virtual Frisch-grid geometry-based CZT gamma detector for in-field radioisotope identification

Here, we present a Virtual Frisch-Grid geometry-based CZT gamma detector developed for identifying different radioisotopes over an energy range from a few keV up to 2 MeV, and useful for efficient characterization of CZT crystals. The detector is built with a 3 x 3 matrix of CZT crystals, each measuring approximately 6 mm x 6 mm x 15 mm. The charge generated within the sensor’s active volume is read out via an anode connected directly to the AVG3_Dev integrated circuit. A current signal induced by charge drift is collected on side pads of the crystals, enabling reconstruction of a 3D interaction position. This paper discusses the design, development, and performance of the standalone, mobile detector system, which integrates the AVG3_Dev readout IC developed at Brookhaven National Laboratory, a high-speed FPGA-based with per-channel digital signal processing, and embedded system capabilities. The device is compact, battery-powered, and supports wireless data streaming, making it suitable for field operations for radioisotope identification.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

First operation of a multi-channel Q-Pix prototype: measuring transverse electron diffusion in a gas time projection chamber

We report measurements of the transverse diffusion of electrons in P-10 gas (90% Ar, 10% CH 4 ) in a laboratory-scale time projection chamber (TPC) utilizing a novel pixelated signal capture and digitization technique known as Q-Pix. The Q-Pix method incorporates a precision switched integrating transimpedance amplifier whose output is compared to a threshold voltage. Upon reaching the threshold, a comparator sends a 'reset' signal, initiating a discharge of the integrating capacitor. The time difference between successive resets is inversely proportional to the average current at the pixel in that time interval, and the number of resets is directly proportional to the total collected charge. We developed a 16-channel Q-Pix prototype fabricated from commercial off-the-shelf components and coupled them to 16 concentric annular anode electrodes to measure the spatial extent of the electron swarm that reaches the anode after drifting through the uniform field of the TPC. The swarm is produced at a gold photocathode using pulsed UV light. The measured transverse diffusion agrees with simulations in PyBoltz across a range of operating pressures (200–1500 Torr). These results demonstrate that a Q-Pix readout can successfully reconstruct the ionization topology in a TPC.

47 OTHER INSTRUMENTATION↗