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At least 829 records · Page 46

Report to NCSP on 2008 DANCE measurements of 233 U($\eta$,$\gamma$)

Uranium-233 plays an important role in the Th-U fuel cycle, with substantial production in the cycle. This cycle has been proposed as an alternative to the U-Pu fuel cycle due to its reduced production of transuranic elements. An accurate measurement of the 233 U($\eta$,$\gamma$) cross section is required by the National Criticality Safety Program (NCSP) to complete the neutron-induced cross section data, where experimental capture cross section data are scarce and were measured decades ago. The most recent capture cross section data available in the literature were measured in 2007 at the n_TOF facility (CERN); in the 60s measurements were performed at Rensselaer Polytechnic Institute (RPI) and at LANL. Finally, as reported by ORNL, a new evaluation with a revised (renormalized) fission cross section is needed on 233 U. The challenge for this measurement lies in the difficulty of measuring the capture cross section data in the competing fission background, as the fission cross section is around one order of magnitude larger than the capture cross section for 233 U. The accuracy of a capture cross section measurement depends on discrimination between $\gamma$’s produced in capture and fission reactions, for which an experimental setup combining capture and fission detectors is needed. For the ($\eta$,$\gamma$) cross section measurement at LANSCE, this discrimination is achieved by combining the Detector for Advanced Neutron Capture Experiments (DANCE), to measure $\gamma$’s from capture reactions, with a Parallel Plate Avalanche Counter (PPAC) to tag the $\gamma$’s produced by fission. This method was successfully used to measure 235 U and 239 Pu capture cross sections. In these measurements, the neutron capture cross section was determined in a large fission background well above 100 keV. As part of the NCSP nuclear data effort, we have looked at past DANCE measurements on 233 U($\eta$,$\gamma$) and evaluated whether existing data is adequate to apply this technique.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Applying Machine Learning to the Classification of DC-DC Converters. NA-22 Final Report

Tools are now available that enable measurement electromagnetic radiation (EMR) from active electronics in an item. This radiation may be intended WIFI or cellular network links, for example or unintended such as the switching noise generated by DC-to-DC converters. It would be extremely valuable to have the capability to discriminate between the low-voltage DC-to-DC converters or other digital noise prevalent in most modern electronics, versus the high-voltage DC-to-DC converters used in utility firesets. Previous work performed under a Sandia Laboratory Directed Research and Development (LDRD) project on Charge State Detection using a deep neural network has been continued in this effort. A state-of-the-art supervised machine learning algorithm has not only been extended to discriminate between low and high voltage converters but has been validated in determining a converters make and model.

42 ENGINEERING↗

CONFIDANTE Demonstration Prototype Report

This report summarizes design optimization and performance evaluation studies for a new prototype CONFIDANTE (CONfirmation using a Fast-neutron Imaging Detector with Anti-image Null-positive Time Encoding) warhead confirmation system. It was found that a spherical mask geometry and a 2” diameter cylindrical central detector is expected to best discriminate between the parametrically varied source distributions that were evaluated. The optimized design as fabricated and its performance was evaluated in a series of laboratory measurements. The performance was in good agreement with the design studies, with demonstrated discrimination between objects with differences in scale on the order of 5 centimeters or better at 1 meter stand-off

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Data Fusion via Neural Network Entropy Minimization for Target Detection and Multi-Sensor Event Classification

Broadly applicable solutions to multimodal and multisensory fusion problems across domains remain a challenge because effective solutions often require substantive domain knowledge and engineering. The chief questions that arise for data fusion are in when to share information from different data sources, and how to accomplish the integration of information. The solutions explored in this work remain agnostic to input representation and terminal decision fusion approaches by sharing information through the learning objective as a compound objective function. The objective function this work uses assumes a one-to-one learning paradigm within a one-to-many domain which allows the assumption that consistency can be enforced across the one-to-many dimension. The domains and tasks we explore in this work include multi-sensor fusion for seismic event location and multimodal hyperspectral target discrimination. We find that our domain- informed consistency objectives are challenging to implement in stable and successful learning because of intersections between inherent data complexity and practical parameter optimization. While multimodal hyperspectral target discrimination was not enhanced across a range of different experiments by the fusion strategies put forward in this work, seismic event location benefited substantially, but only for label-limited scenarios.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Adaptive Sampling for In Situ Cloud Probe (Final Report)

Clouds play a leading role in the Earth's global energy and solar radiation balance and hydrological cycle. Improving cloud models requires detailed information on the cloud microphysical properties, such as droplet size distribution and number density, liquid water content and cloud composition (droplets, ice particles), which can only be provided by aerial in situ measurements. However, for many atmospheric measurement instruments, the lack of flexibility in selecting the operational mode during operation can lead to uncertainties in sampling and measurement characteristics under continuously varying atmospheric conditions. This SBIR project is developing an advanced, compact optical imaging technology for in situ characterization of cloud hydrometeors. The development involves a deep modification of the existing Mesa Photonics’ Cloud Droplet Measurement System (CDMS) in order to implement real-time automatic adaptive sampling based on the acquired in situ data and environmental parameters. The new system, CDMS-2, implements two measurement modes: side-scatter imaging for smaller hydrometeors and direct bright-field-illumination imaging for larger hydrometeors in a significantly larger sample volume. The system measures the droplet size distribution (DSD) and number density with an added capability of discriminating between liquid water and ice hydrometeors (based on polarization-resolved side-scatter imaging). The instrument will implement automatic switching or alternating between the regular side-scatter imaging mode and sparse/large hydrometeor mode (based on the acquired data). Other adaptive sampling capabilities include variable sample volume and dynamic range (based on the measured DSD). The preferred deployment platforms are uncrewed aircraft systems (UAS) and tethered balloon/kite systems (TBS). The Phase I project achieved (or exceeded) the goals listed in the Work Plan. A CDMS-2 laboratory prototype implementing the polarization-resolved side-scatter imaging mode and direct bright-field-illumination imaging mode was designed and built. Additional capabilities included the variable illumination pulse energy and sample volume. The smallest detectable droplet diameter was improved to 3–4 μm (from the nominal 10 μm value specified for the original CDMS). Discrimination between water droplets and ice particles was experimentally demonstrated. The Phase I prototype was extensively tested and calibrated in the laboratory and also tested in the Pi Cloud Chamber at Michigan Technological University (MTU). The two intensive experimental campaigns at MTU provided unique opportunities of testing the CDMS-2 laboratory prototype under realistic warm and mixed-phase cloud conditions (stable for long periods of time), testing different sampling modes and intercomparing the CDMS-2 prototype to other co-located cloud characterization instruments. The Phase I project successfully demonstrated the feasibility of the proposed technology and identified the engineering challenges of designing a field deployable prototype instrument in Phase II. The Phase I study provides a solid basis for development, characterization and field-testing of the proposed advanced cloud probe with adaptive sampling in Phase II followed by commercialization of the technology in Phase III.

47 OTHER INSTRUMENTATION↗

Hybrid Detector (Final Technical Report)

We report updates since the first demonstration of a phonon-mediated silicon detector technology that provides a primary phonon measurement in a low-voltage region, and a simultaneous indirect measurement of the ionization signal through Neganov–Trofimov–Luke amplification in a high voltage region, both in a monolithic crystal. We present characterization of charge and phonon transport between the two stages of the detector and the resulting background discrimination capability at low energies. This new detector technology has the potential to significantly enhance the sensitivity of dark matter and coherent neutrino scattering experiments beyond the capabilities of current technologies that have limited discrimination at low energies.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Technical Report on Waveform Fit Metrics for Global Models

The new WAVEFORMS Initiative in the Ground-based Nuclear Detonation Detection (GNDD) program includes an increased emphasis on the development of Earth models and methods to predict entire seismic and acoustic waveforms more accurately. In general, this increased emphasis is predicated on the need to better characterize seismic events and provide improved model-based discrimination between event types including earthquakes and explosions. More specifically, while current moment tensor inversion methods tend to work well for larger events (M>~4) using tuned 1-D Earth models, the development of state-of-the-art 3-D models and methods is required for the prediction of shorter period waves over large areas for discrimination of smaller events. There is no standard metric for model-based waveform prediction accuracy used in the waveform modeling/inversion community. However, there are several popular waveform misfit definitions; and minimizing the corresponding objective functions is the goal of waveform inversion. Some example misfit definitions employed for adjoint waveform tomography include measures of simple travel time differences (e.g. Tape et al., 2010), cross-correlation travel time differences (e.g. Luo and Schuster, 1991), multi-taper frequency dependent methods (e.g. Lei et al., 2020), time-frequency phase misfit functions (e.g. Fichtner 2010; Rodgers et al., 2022), normalized cross-correlation methods (e.g. Tao et al., 2018), and others. In some cases, these misfit definitions also involve complicated weighting schemes and summations over multiple frequency bands making it difficult to duplicate the misfit measurement with alternative models and datasets. Although each of the misfit definitions mentioned above are useful for developing waveform models, the actual misfit values are not usually meaningful outside of a given project, model, and/or dataset. Therefore, it is difficult to understand and communicate model performance for predicting waveforms and comparing to other models and/or new model iterations with a different dataset. Therefore, there is a need for a generalized method for evaluating overall model performance that is independent from the specific misfit chosen to develop the waveform models that is also intuitive and meaningful. In this report, we describe a new metric we refer to as ‘Percent of Correlated Signal’. The following sections describe and demonstrate the metric with a case study event and a more rigorous test using a random selection of globally distributed events. While the focus here is on global tomography models, the metric is meant to applicable to regional ‘wiggle-for-wiggle’ waveform models/studies as well.

58 GEOSCIENCES↗

Scintillating Bubble Chambers for Direct Dark Matter Detection, and an Update on SBC-LAr10

The Scintillating Bubble Chamber (SBC) Collaboration aims to use the liquid-noble bubble chamber technology as a low-threshold detector for dark matter particles of 1-10 GeV/c2. The detector combines the remarkable electron recoil (ER) discrimination of the bubble chamber with the event-by-event energy resolution provided by liquid argon (LAr) scintillation, with the crucial added benefit that ER discrimination in liquid-noble bubble chambers extends to much lower thresholds than in past freon-filled bubble chambers, with the potential for quasi-background-free operation at thresholds of 100 eV in nuclear recoil (NR) energy. SBC has developed two functionally identical 10 kg detectors: SBC-LAr10 at Fermilab will calibrate low-threshold performance, while the radio-pure SBC-SNOLAB chamber will execute SBC s first deep-underground dark matter search. SBC-LAr10 at Fermilab was recently installed in the MINOS tunnel 100 meters underground, and the first bubbles are expected this fall. I will present the current status of SBC-LAr10 and describe the suite of gamma and neutron calibrations we will execute. The calibrations will confirm the physics reach of this new technology, motivating not just the SBC-SNOLAB chamber now being assembled but also future searches into the solar neutrino fog at 1 GeV/c2.

Sheng, Zhiheng↗

Electromagnetic shower reconstruction in the ICARUS liquid argon time projection chamber detector

The ICARUS-T600 liquid argon time projection chamber (LArTPC) detector is taking data at shallow depth as the far detector of the Short Baseline Neutrino (SBN) program at Fermilab, to search for a possible sterile neutrino signal at $\Delta m^{2} \approx 1~\text{eV}^{2}$ with the Booster (BNB) and Main Injector (NuMI) neutrino beams at $\GeV{\sim 0.8}$ and $\GeV{\sim 2}$ average energies respectively. The LArTPC technology, developed by the ICARUS collaboration and now a standard in neutrino physics, offers impressive charged-particle imaging capabilities with $\sim1 \ \text{mm}$ spatial resolution, enabling efficient discrimination between track-like signatures (e.g., from muons, pions, and protons) and electromagnetic showers (from electrons and photons). Moreover, electron and photon signatures can be distinguished both with the calorimetric measurement of local energy depositions at the shower start and with the cm-scale conversion gap signature of photons. This contribution discusses event reconstruction at ICARUS focusing on Pandora, a multi-algorithm pattern recognition software widely used in LArTPC experiments. Over a hundred Pandora algorithms and tools are used to reconstruct cosmic rays and neutrino interactions in the ICARUS detector. Recent developments have focused on the reconstruction of electromagnetic shower signatures, crucial to ensure a robust and efficient reconstruction of charged-current $\nu_e$ interactions, which serve as a key signature of sterile neutrino oscillations at SBN. In this contribution, recent improvements to the reconstruction are discussed, focusing on the discrimination between tracks and electromagnetic showers using neutrino simulations and data.

Triozzi, Riccardo [Padua U.; INFN, Padua] (ORCID:0↗

The ETROC2 as the Final Version for CMS Endcap Timing Layer (ETL) Upgrade

The ETROC (Endcap Timing Readout Chip) is being developed for the LGAD-based CMS Endcap Timing Layer (ETL) at HL-LHC. The ETL on each side of the interaction region will be instrumented with a two-disk system of MIP-sensitive LGAD (Low Gain Avalanche Diodes) silicon devices, read out by ETROCs for precision timing measurement with down to ~30 ps timing resolution per track. The ETROC is designed to handle a 16 x 16 pixel cell matrix, with each pixel being 1.3 mm x 1.3 mm to match the LGAD sensor pixel size. The front-end design for preamplifier and discriminator has been specifically optimized for the reduced LGAD signals, with enough flexibilities to meet the ETL specific needs for time resolution, power budget and radiation profile. The ETROC chip is implemented in a commercial 65nm CMOS process. Each channel consists of a preamplifier, a discriminator, a TDC used for TOA (Time Of Arrival) and TOT (Time Over Threshold) measurements, and a memory for data storage and readout. An in-pixel auto threshold calibration is included, along with a self-testing pattern generator. The TOT is used for time-walk correction of the TOA measurement. The detailed hit information (TOA and TOT) from each cell will be read out from a local circular buffer after each Level-1 Accept (about 1 MHz). In addition, a charge injection circuit is implemented to allow for testing and calibration. For more detailed monitoring of the signal pulses, waveform sampling circuits are included for one pixel. The clock distribution is based on a 16x16 H-tree design with a shielding structure to alleviate potential interference. The global peripheral circuits include a PLL, a phase shifter, an I2C slave controller, a fast control block, a global readout, and a data driver along with an efuse and temperature sensor. The ETROC builds event data frames for each L1A selected event and is also capable of providing L1 trigger information for user-defined delayed hits. The main design challenge is how to extract precision timing information from the small LGAD signals in the presence of high irradiation fluence, while keeping the power consumption and digital activity low. The ETL design goal for the time resolution of 50 ps per hit is required to achieve a 35 ps arrival time measurement for a MIP particle, which has its track registered in two ETL disk layers. The LGAD contribution is known to be about 30 ps, this means that the jitter from the ETROC has to be kept below 40 ps. The ETROC2 is the first full size full functionality prototype design fully compatible with the final chip specifications for CMS ETL and now becomes the final version. The ETROC2 chips have been extensively tested. We will present here new testing results including the bump bonding yield improvement study, the time walk correction (TWC) generality study with one pixel TWC applying to all pixels, the final SEU testing using both heavy ion and proton beam, more beam test studies including different sensors, and readiness for the ETROC2 production for CMS ETL upgrade.

Liu, Tiehui [Fermilab] (ORCID:0009000765225605)↗

ArCS: A Magnetized LArTPC in a Test Beam

Over the past few decades, Liquid Argon Time Projection Chambers (LArTPCs) have emerged as a central technology for rare-event detection, due to their calorimetric and imaging capabilities. Adding a magnetic field to LArTPCs would enable charge identification and momentum measurements via curvature. For neutrino experiments, this is crucial for wrong-sign neutrino rejection, electron/positron and electron/photon discrimination, and improved momentum reconstruction. The ArCS (Argon detector with Charge Separation) experiment at Fermilab's Test Beam Facility will place a 47 40 90 cm LArTPC inside a 0.7 T magnet to: (i) establish charge sign discrimination for electrons and positrons, (ii) reconstruct particle momenta via curvature, and (iii) determine the minimal magnetic field needed for these measurements. This poster will present the project status, with updates on installation and simulations of expected performance.

Cicogna, Giulia [Bologna U.]↗

Bipartite chromatin recognition by Hop1 from two diverged Holozoa

In meiosis, ploidy reduction is driven by a complex series of DNA breakage and recombination events between homologous chromosomes, orchestrated by meiotic HORMA domain proteins (HORMADs). Meiotic HORMADs possess a central chromatin binding region (CBR) whose architecture varies across eukaryotic groups. Here, we determine high-resolution crystal structures of the meiotic HORMAD CBR from two diverged aquatic Holozoa,Schistosoma mansoniandPatiria miniata, which reveal tightly associated plant homeodomain (PHD) and winged helix-turn-helix (wHTH) domains. We show that PHD–wHTH CBRs bind duplex DNA through their wHTH domains, and identify key residues that disrupt this interaction. Combining experimental and predicted structures, we show that the CBRs’ PHDs likely interact with the tail of histone H3, and may discriminate between unmethylated and trimethylated H3 lysine 4. Finally, we show that Holozoa Hop1 CBRs bind nucleosomes in vitro in a bipartite manner involving both the PHD and wHTH domain. Our data reveal how meiotic HORMADs with PHD–wHTH CBRs can bind chromatin and potentially discriminate between chromatin states to drive meiotic recombination to specific chromosomal regions.

Life Sciences & Biomedicine - Other Topics↗

Sampling Microbial Dynamics in the Salish Sea Estuary: Evaluating Methods to Capture Cyanobacteria and Cyanophage

Introduction: Picocyanobacteria from the genera Prochlorococcus and Synechococcus thrive across the globe in aquatic environments, have relatively small genomes, and have growth dynamics regulated by both viral interactions and abiotic conditions, making them excellent model organisms for exploring host-pathogencoevolution. Methods: We developed and refined methods to sample and sequence cyanobacteria, cyanophages, and measured features of their abiotic environment. Results: The protocol described herein can successfully discriminate large-cell eukaryotic organisms, but size fractionation of picocyanobacteria appears to be affected by the presence of free DNA, multicellular structures, and abundant tycheposons. Our preferred final protocol from this exploratory effort included a combination of in-line and single vacuum flask filtrations, which reduced filtration processing time by over threefold in some cases compared to other tested methods, such as a fully in-line sequence or in-site filtrations. We successfully extracted an average of approximately 400–1200 ng for all filter fractions, with some variations between kits. Discussion: The protocol described herein can successfully discriminate large-cell eukaryotic organisms, but size fractionation of picocyanobacteria appears to be affected by the presence of free DNA, multicellular structures, and abundant tycheposons.

Salish Sea↗

Mapping the Spatial Distribution of Fibrillar Polymorphs in Human Brain Tissue

Alzheimer’s disease (AD) is a neurodegenerative disorder defined by the progressive formation and spread of fibrillar aggregates of Aβ peptide and tau protein. Polymorphic forms of these aggregates may contribute to disease in varying ways since different neuropathologies appear to be associated with different sets of fibrillar structures and follow distinct pathological trajectories that elicit characteristic clinical phenotypes. The molecular mechanisms underlying the spread of these aggregates in disease may include nucleation, replication, and migration all of which could vary with polymorphic form, stage of disease, and region of brain. Given the linkage between mechanisms of progression and distribution of polymorphs, mapping the distribution of fibrillar structures in situ has the potential to discriminate between mechanisms of progression. However, the means of carrying out this mapping are limited. Optical microscopy lacks the resolution to discriminate between polymorphs in situ, and higher resolution tools such as ssNMR and cryoEM require the isolation of fibrils from tissue, destroying relevant spatial information. Here, we demonstrate the use of scanning x-ray microdiffraction (XMD) to map the locations of fibrillar polymorphs of Aβ peptides and tau protein in histological thin sections of human brain tissue. Coordinated examination of serial sections by immunohistochemistry was used to aid in the interpretation of scattering patterns and to put the observations in a broader anatomical context. Scattering from lesions in tissue shown to be rich in Aβ fibrils by immunohistochemistry exhibited scattering patterns with a prototypical 4.7 Å cross-β peak, and overall intensity distribution that compared well with that predicted from high resolution structures. Scattering from lesions in tissue with extensive tau pathology also exhibited a 4.7 Å cross-β peak but with intensity distributions that were distinct from those seen in Aβ-rich regions. In summary, these observations demonstrate that XMD is a rich source of information on the distribution of fibrillar polymorphs in diseased human brain tissue. When used in coordination with neuropathological examination it has the potential to provide novel insights into the molecular mechanisms underlying disease.

59 BASIC BIOLOGICAL SCIENCES↗

Predicting SARS-CoV-2 Variant Using Non-Invasive Hand Odor Analysis: A Pilot Study

The adaptable nature of the SARS-CoV-2 virus has led to the emergence of multiple viral variants of concern. This research builds upon a previous demonstration of sampling human hand odor to distinguish SARS-CoV-2 infection status in order to incorporate considerations of the disease variants. This study demonstrates the ability of human odor expression to be implemented as a non-invasive medium for the differentiation of SARS-CoV-2 variants. Volatile organic compounds (VOCs) were extracted from SARS-CoV-2-positive samples using solid phase microextraction (SPME) coupled with gas chromatography–mass spectrometry (GC–MS). Sparse partial least squares discriminant analysis (sPLS-DA) modeling revealed that supervised machine learning could be used to predict the variant identity of a sample using VOC expression alone. The class discrimination of Delta and Omicron BA.5 variant samples was performed with 95.2% (±0.4) accuracy. Omicron BA.2 and Omicron BA.5 variants were correctly classified with 78.5% (±0.8) accuracy. Lastly, Delta and Omicron BA.2 samples were assigned with 71.2% (±1.0) accuracy. This work builds upon the framework of non-invasive techniques producing diagnostics through the analysis of human odor expression, all in support of public health monitoring.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Dynamic linearity measurement technique

Measurement technique involves frequency modulated discriminator which produces an error signal as two signals, one of known and one of unknown frequency. The signals are electronically switched to a discriminator input, allowing independent measuring of dynamic linearity in a frequency modulated subcarrier oscillator.

Merz, K.↗