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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

ML Track Fitting in Nuclear Physics

Charged particle tracking represents the largest consumer of CPU resources in high data volume Nuclear Physics (NP) experiments. An effort is underway to develop machine learning (ML) networks that will reduce the resources required for charged particle tracking. Tracking in NP experiments represent some unique challenges compared to high energy physics (HEP). In particular, track finding typically represents only a small fraction of the overall tracking problem in NP. This presentation will outline the differences and similarities between NP and HEP charged particle tracking and areas where ML learning may provide a benefit. The status of the specific effort taking place at Jefferson Lab will also be shown.

Britton, Thomas↗
DOE OSTI2023

Differentiable Earth mover’s distance for data compression at the high-luminosity LHC

Abstract The Earth mover’s distance (EMD) is a useful metric for image recognition and classification, but its usual implementations are not differentiable or too slow to be used as a loss function for training other algorithms via gradient descent. In this paper, we train a convolutional neural network (CNN) to learn a differentiable, fast approximation of the EMD and demonstrate that it can be used as a substitute for computing-intensive EMD implementations. We apply this differentiable approximation in the training of an autoencoder-inspired neural network (encoder NN) for data compression at the high-luminosity LHC at CERN The goal of this encoder NN is to compress the data while preserving the information related to the distribution of energy deposits in particle detectors. We demonstrate that the performance of our encoder NN trained using the differentiable EMD CNN surpasses that of training with loss functions based on mean squared error.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

AGR-5/6/7 Final Release-to-Birth Ratio Data Analysis

AGR-5/6/7 is the last of a series of Advanced Gas Reactor (AGR) experiments conducted in the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL) in support of development and qualification of tristructural isotropic (TRISO) low-enriched fuel for use in the high-temperature gas cooled reactor (HTGR). AGR configuration and irradiation conditions are based on prismatic HTGR technology that is distinguished primarily through the use of helium coolant, a low-power-density ceramic core capable of withstanding very high temperatures, and TRISO-coated particle fuel. The AGR tests provide valuable irradiation-performance data to support fuel process development, qualify fuel for normal operation and accident conditions, and support development and validation of fuel performance and fission-product (FP) transport models and codes. Each AGR test consists of multiple independently controlled and monitored capsules containing fuel compacts placed in a graphite cylinder shrouded by a steel shell. Release-to-birth ratios (R/B) for fission-gas isotopes released from each capsule are calculated from release rates, measured by germanium detectors in the Fission Product Monitoring System (FPMS) installed downstream from each capsule, and birth rates calculated using numerical models of FG generation. The R/Bs are a critical measure of the ability of the fuel kernel, the particle coating layers, and the compact matrix to retain fission-gas atoms, preventing their release into the sweep-gas flow, and the impact of initially defective particles and/or particle-coating failures that occur during irradiation. For fission-gas isotopes, particle failure is defined as failure of all coating layers, allowing gaseous fission atoms to escape from a particle. During the first five cycles (162B ? 165A), R/Bs were stable in the 10-8?10-6 range, and no in-pile particle failures were observed, based on the gross gamma counts. The maximum R/B value of around 2 ? 10-6 for Kr-85m resulted from the presence of as-fabricated exposed kernels (based on the high exposed kernel fraction), the dispersed uranium, and high fuel particle temperatures in Capsule 1. Comparison of capsule-measured R/Bs from these early cycles to predictions using the previously developed AGR R/B model demonstrated FG release from the AGR-5/6/7 TRISO fuel was comparable to that of previous experiments. In addition, the Kr-85m R/B per-exposed-kernel values are comparable to R/B values obtained in AGR-3/4 irradiation experiment and four irradiation experiments performed during 1980s: (1) HRB-17/18, (2) COMEDIE-BD1, (3) HFR B1, and (4) HRB-21. In contrast, all measured R/B values are lower than predictions by the commonly used Richards and German models, which are intentionally conservative. A large number of in-pile particle failures occurred in Capsule 1 by the end of Cycle 166A. During the final four cycles (166A ? 168A), apparent damage to the Capsule 1 gas line appeared to cause FG leakage from that capsule into the other four capsules, resulting in an increase in fission gas (FG) detected in the effluent for all capsules. Isolation of the Capsule 1 gas line during the last three cycles also prevented measurement of its FG release. Thus, R/Bs in all capsules after Cycle 166A are highly uncertain because of undefined amount of leakage from Capsule 1, especially for long-lived isotopes. A few hundred in-pile particle failures were estimated for Capsule 1 before the end of Cycle 166A, but the total number of failures is unknown due to the lack of FG release data in the later cycles. Based primarily on evidence from the gross gamma counts during Cycle 168A, approximately 15 particles failed in Capsule 3 and four particles failed in Capsule 2. In-pile failures in Capsule 3 were anticipated because this capsule was designed to operate beyond the HTGR normal operating temperature range. In contrast, no in-pile failures were identified in the top two capsules (4 and 5) based on the absence of the typical spikes in gross gamma counts and low failure estimates using the AGR model, developed in INL/EXT-14-32970, for R/B of the short-lived isotopes (Kr-89 and Xe-137) with minimal leakage from Capsule 1.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Search for dark matter from the center of the Earth with 10 years of IceCube data

The nature of dark matter remains unresolved in fundamental physics. Weakly Interacting Massive Particles (WIMPs), which could explain the nature of dark matter, can be captured by celestial bodies like the Sun or Earth, leading to enhanced self-annihilation into Standard Model particles including neutrinos detectable by neutrino telescopes such as the IceCube Neutrino Observatory. This article presents a search for muon neutrinos from the center of the Earth performed with 10 years of IceCube data using a track-like event selection. We considered a number of WIMP annihilation channels (χχ→τ+τ-$$\chi \chi \rightarrow \tau ^+\tau ^-$$/W+W-$$W^+W^-$$/bb¯$$b\bar{b}$$) and masses ranging from 10 GeV to 10 TeV. No significant excess over background due to a dark matter signal was found while the most significant result corresponds to the annihilation channel χχ→bb¯$$\chi \chi \rightarrow b\bar{b}$$ for the mass mχ=250$$m_{\chi }=250$$ GeV with a post-trial significance of 1.06σ$$1.06\sigma $$. Our results are competitive with previous such searches and direct detection experiments. Our upper limits on the spin-independent WIMP scattering are world-leading among neutrino telescopes for WIMP masses mχ>100$$m_{\chi }>100$$ GeV.

Abbasi, R↗
DOE OSTI2022

Real-Time Interactive 4D-STEM Phase-Contrast Imaging From Electron Event Representation Data: Less computation with the right representation

The arrival of direct electron detectors (DED) with high frame-rates in the field of scanning transmission electron microscopy has enabled many experimental techniques that require collection of a full diffraction pattern at each scan position, a field which is subsumed under the name four dimensional-scanning transmission electron microscopy (4D-STEM). DED frame rates approaching 100 kHz require data transmission rates and data storage capabilities that exceed commonly available computing infrastructure. Current commercial DEDs allow the user to make compromises in pixel bit depth, detector binning or windowing to reduce the per-frame file size and allow higher frame rates. This change in detector specifications requires decisions to be made before data acquisition that may reduce or lose information that could have been advantageous during data analysis. The 4D Camera, a DED with 87 kHz frame-rate developed at Lawrence Berkeley National Laboratory, reduces the raw data to a linear-index encoded electron event representation (EER). Here we show with experimental data from the 4D Camera that linear-index encoded EER and its direct use in 4D-STEM phase contrast imaging methods enables real-time, interactive phase-contrast from large-area 4D-STEM datasets. Furthermore, we detail the computational complexity advantages of the EER and the necessary computational steps to achieve real-time interactive ptychography and center-of-mass differential phase contrast using commonly available hardware accelerators.

4D-STEM↗
DOE OSTI2021

An Indirect Search for Weakly Interacting Massive Particles in the Sun Using Upward-going Muons in NOvA

I present the first Dark Matter search results using the full data set collected with the upward-going muon trigger in NOvA. Weakly Interactive Massive Particles (WIMPs) are a theoretical non-baryonic form of Dark Matter. The nature of Dark Matter is one of the most exciting open questions in modern physics. Though its existence can be inferred by astrophysical evidence, its properties are not yet understood. If we assume that Dark Matter particles can produce Standard Model particles through their interactions, an indirect search can help shed light on this mystery.The NOvA collaboration has built a 14 kton, fine-grained, low-Z, total absorption tracking calorimeter at an off-axis angle to the NuMI neutrino beam. Even though the detector is optimized to observe electron neutrino appearance from a muon neutrino beam, it has a unique potential for more exotic searches given its excellent granularity and energy resolution and relatively low-energy neutrino thresholds. In fact, with an efficient upward-going muon trigger and sufficient background suppression offline, NOvA is capable of a competitive indirect Dark Matter search for low-mass WIMPs.The idea of the upward-going muon trigger is first to select high-quality muon tracks, then use the timing information of all of the hits of each track to estimate directionality. In this way, the background flux is suppressed by more than a factor of 10^5 at trigger level to a rate of approximately 1 Hz. To further optimize this search, we use only upward-going muons that point to the Sun, so our search occurs at night when the Sun is on the other side of the Earth. This strategy also allows us to use the time when the Sun is above the horizon as a control region to estimate the background. Ultimately, implementation of a cut based and maximum likelihood analysis provides a powerful tool for rejecting background and selecting a sample of neutrino-induced upward-going muons. The overall background rejection power achieved by the analysis is substantial and impressive. Starting with approximately 150,000 events per second, we reduced it to 40 events per year. Since no statistically significant excess was found, a 90\% C.L. upper limit on the expected muon flux of upward-going muons has been set using the upper limit on the number of events given the number of observed events in the signal region. Lastly, by assuming the theory behind the upward-going muon flux, a limit on the WIMP-nucleon spin-dependent cross-section in the Sun was estimated. Although the limits on the spin-dependent cross-section do not appear to be competitive with previous indirect Dark Matter searches, the upward-going muon flux limits are promising. The upward-going muon flux limits could extend these results to a broader class of models that are not specific to the dark matter theory but produce upward-going muons, leading to competitive results.

Principato, Cristiana↗
DOE OSTI2021

A simplified integrated framework for predicting the economic impacts of feedstock variations in a catalytic fast pyrolysis conversion process

Feedstock attributes of lignocellulosic biomass, such as particle size, compositional makeup, and moisture content, can vary substantially even within pre-processed materials and have a significant effect on conversion in fast pyrolysis-based processes. However, the economic impacts of these attributes are not well understood. To address this, biomass deconstruction phenomena captured with a versatile particle-scale simulation were linked to techno-economic impacts via reduced-order models. Parametric analysis of the particle-scale model, which was validated using literature data, was used in combination with multiple linear regression models to develop correlations between feedstock attributes and yields of pyrolysis oil, gas, and char. Yields were then correlated with the minimum fuel selling price (MFSP) using a techno-economic model, bridging the gap between physics-based biomass conversion simulations and predictions of MFSP for a catalytic fast-pyrolysis process. Empirical correlations derived from the literature regarding the impact of mineral matter (ash) on oil yield were also considered. The model correlations deployed in the integrated framework capture the impacts of variation in feedstock attributes on the MFSP. Variations in ash were shown to have the biggest impact, varying MFSP by -13%/+22% due to catalytic effects and lower relative amounts of convertible lignocellulosic material. It was also found that, if ash can be controlled to low levels, the increased extractives in forest residues can help compensate for some yield losses associated with increased ash. As a result, other inputs considered (particle size, moisture content, and reactor temperature) had relatively negligible effects on process economics within the ranges analyzed considering particle-scale effects alone.

BIOMASS FUELS↗
DOE OSTI2023

Size-Dependent Energy and Adhesion of Pd Nanoparticles on Graphene on Ni(111) by Pd Vapor Adsorption Calorimetry

Carbon-supported late transition-metal nanoparticles are promising catalysts and electrocatalysts for wide-ranging applications. However, experimental investigations of the bonding energetics of metal nanoparticles on carbon supports are very limited. Here, in this work, we report heats of adsorption of Pd vapor deposited onto single-layer graphene(0001) supported on Ni(111) at 100 and 300 K as Pd grows particles of well-defined size in the range from three atom clusters to 6 nm diameter. Sizes were determined from He + low-energy ion scattering (LEIS). In this size range, the differential heat of Pd adsorption increases from 228 kJ/mol to within 10 kJ/mol of the heat of sublimation of bulk Pd (377 kJ/mol). The chemical potential of metal atoms in these nanoparticles as a function of average particle size was determined from these results. The adhesion energy at the Pd/graphene(0001)/Ni(111) interface was extracted from these data and found to be 3.5 J/m 2 for the largest Pd particles. For the three metal elements that have now been studied (Pd, Ni, and Ag), we found an increase in metal/graphene(0001)/Ni(111) adhesion energy with metal carbophilicity, which we defined here as the heat of C atom adsorption on that metal’s (111) surface estimated from published density functional theory calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

SAIL-Net CloudPuck CCN Data

SAIL-Net is a DOE funded project in the East River Watershed near Crested Butte, Colorado with the goal of advancing our understanding of aerosol-cloud interactions in complex, mountainous regions. Through the deployment of a network of six low cost microphysics nodes in Fall 2021 in the same domain at the SAIL campaign, SAIL-Net provides data on aerosol size distributions, cloud condensation nuclei (CCN), and ice nucleations particles (INP). This network enables the investigation of small-scale variations in complex terrain. This specific dataset provides the cleaned data recorded from the CloudPuck, an in house instrument made by Handix Scientific that counts CCN concentrations. The CloudPuck was deployed at the sites for the summer/fall of 2022 before winter conditions were too harsh to maintain the instrument. For more information on the the CloudPuck or how the raw data are processed, see the read me.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Multiplicity-dependent inclusive J/ ψ production at forward rapidity in pp collisions at $\sqrt{s}$ = 13 TeV

This paper presents a study of the inclusive forward J/ψ yield as a function of forward charged-particle multiplicity in pp collisions at $\sqrt{s}$ = 13 TeV using data collected by the ALICE experiment at the CERN LHC. The results are presented in terms of relative J/ψ yields and relative charged-particle multiplicities with respect to these quantities obtained in inelastic collisions having at least one charged particle in the pseudorapidity range |η| < 1. The J/ψ mesons are reconstructed via their decay into μ + μ − pairs in the forward rapidity region (2.5 < y < 4). The relative multiplicity is estimated in the forward pseudorapidity range which overlaps with the J/ψ rapidity region. The results show a steeper-than-linear increase of the J/ψ yields versus the multiplicity. They are compared with previous measurements and theoretical model calculations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Underlying-event studies with strange hadrons in pp collisions at √s = 13 TeV with the ATLAS detector

Properties of the underlying-event in pp interactions are investigated primarily via the strange hadrons $K^0_S$, $\Lambda$, and $\bar\Lambda$, as reconstructed using the ATLAS detector at the LHC in minimum-bias pp collision data at √s = 13 TeV. The hadrons are reconstructed via the identification of the displaced two-particle vertices corresponding to the decay modes $K^0_S$ → π⁺π⁻, Λ → π⁻p and $\bar\Lambda$ → $\pi^+\overline{p}$. These are used in the construction of underlying-event observables in azimuthal regions computed relative to the leading charged-particle jet in the event. None of the hadronisation and underlying-event physics models considered can describe the data over the full kinematic range considered. Events with a leading charged-particle jet in the range of 10 < p T ≤ 40 GeV are studied using the number of prompt charged particles in the transverse region. The ratio N(Λ + $\bar\Lambda$/N($K^0_S$) as a function of the number of such charged particles varies only slightly over this range. This disagrees with the expectations of some of the considered Monte Carlo models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Aerosol Chemical Speciation Monitor (ACSM) Composition-Dependent Collection Efficiency (CDCE) Value-Added Product Report

Aerosol particles influence the Earth’s radiation balance directly by absorbing and scattering light and indirectly by influencing cloud formation, properties, and lifetimes. Measurements of aerosol particle optical properties, mass loading, size distributions, microphysical properties, cloud formation properties, and chemical composition are important for understanding the aerosol life cycle and for validating earth system models that predict these quantities. The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s Aerosol Observing System (AOS) is a highly instrumented platform that measures many of these aerosol properties in situ. The Aerodyne aerosol chemical speciation monitor (ACSM) is a baseline instrument deployed in the AOS. The ACSM provides a quantitative measurement of aerosol particle chemical composition for non-refractory (operationally defined as components that evaporate on the 600ºC vaporizer) aerosol components in real time. Standard output is the mass concentration of particulate organics, nitrate, sulfate, chloride, and ammonium. One well-known limitation to the accuracy of the ACSM data is in evaluating the fraction of the ambient aerosol particles that are detected by the instrument. This quantity is referred to as the collection efficiency (CE) and is often less than unity. This is attributed to particles rebounding after impaction onto the heated vaporizer rather than being trapped, volatilized rapidly, and detected. Other factors, such as divergence of the aerosol particle beam, may also impact CE, but the particle rebound effect is the dominant factor. Scientists will sometimes assume CE = 0.5 (i.e., one half of sampled particles are detected) for ambient particles collected during field missions. However, parameterizations have been developed that express CE as a function of the measured chemical composition, referred to as the composition-dependent collection efficiency (CDCE). The physical explanation for a CDCE, supported by laboratory studies, is that rebound from the vaporizer is a function of the particle phase, with liquid-like particles “sticking” to the vaporizer and solid-like or crystalline particles “bouncing” from the vaporizer. Thus, particles with compositions are liquids-like under the measurement conditions (e.g., certain organics, acidic particles, particles enriched in nitrate) have CE close to unity while crystalline or solid particles (e.g., deliquesced ammonium sulfate) have lower CE. This value-added product (VAP) implements the procedure described by Middlebrook et al. (2012) to correct the ACSM data for the composition-dependent collection efficiency. Applying this parameterization improves the accuracy of the ACSM data and brings them into better agreement with other co-located aerosol measurements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Zeta potential measurements of SiO 2 and TiO 2 particles in anionic and cationic surfactant solutions

Zeta potential measurements can provide valuable insights into colloidal stability and may reflect changes in the electrochemical double layer that influence electrocatalysis. In this study, we used zeta potential measurements to monitor and investigate the adsorption behavior of anionic sodium dodecyl sulfate (SDS), cationic dodecyl trimethyl ammonium bromide (DTAB), and cationic cetyl trimethyl ammonium bromide (CTAB) surfactants on SiO 2 and TiO 2 particles in DI water. The zeta potentials of SiO 2 and TiO 2 particles were measured as a function of particle concentration in 1.0 mM, 5.0 mM, and 10.0 mM surfactant solutions. Results indicate that there is no significant adsorption of DS - ions on SiO 2 particles across all SDS concentrations investigated in this study. The zeta potential of TiO 2 particles shifts from negative to positive as particle concentration increases at 1.0 mM SDS, indicating a decrease in DS - ions adsorption per TiO 2 particle. Adsorption plateaus at 5.0 mM SDS for all TiO 2 concentrations studied. In contrast, DTA + and CTA + ions showed clear adsorption on SiO 2 particles, but DTA + exhibited no apparent adsorption on TiO 2 particles. Interestingly, CTA + adsorption on TiO 2 was only evident at higher surfactant concentrations. This study presents a systematic investigation of zeta potential behavior as a function of particle and surfactant concentration, highlighting adsorption saturation and electrostatic reversal. Unlike prior work that was limited to single particle concentration measurements, our approach reveals how surfactant adsorption per particle decreases with increasing surface area. Despite the structural similarity between DTAB and CTAB, this study captured the different adsorption behaviors of both surfactants on SiO 2 and TiO 2 , emphasizing the role of the overall structure of the surfactant on surfactant adsorption behavior. These insights refine the interpretation of zeta potential data and advance understanding of surfactant adsorption on oxide particles. The insight provided in this study about surfactant adsorption on SiO 2 and TiO 2 particles could have important implications for the design of more effective colloidal and catalytic systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Design and evaluation of a dilute flow particle-to-air heat exchanger for energy storage applications

The use of inert and redox-active particles for high-temperature energy storage requires the development of components that can efficiently transfer energy to high-pressure working fluids like supercritical carbon dioxide (sCO 2 ). Dilute flow reactors can enable high working fluid outlet temperatures and minimal parasitic losses compared to moving packed bed and fluidized bed reactors. This research uses both computational and experimental methods to explore the design trade-offs and practical challenges of a novel component for transferring energy from dilute flows of hot, reduced metal oxide (MO x ) particles to sCO 2 in tubes. A discretized thermal resistance network model, which accounts for particle hydrodynamics, multi-mode heat transfer, and reaction equilibrium, guides the design of a prototype device. This device is experimentally tested with a surrogate heat transfer fluids and inert particle temperatures up to 400°C and a heat duty exceeding 1 kW. The data are used to validate the thermal hydraulic sub-models, allowing for the simulation of reacting particle scenarios. Under nominal design conditions, the flow rate of reactive particles is predicted to be 30% lower than that of inert particles for the same energy recovered, with over 70% of the stored particle energy transferred to the sCO 2 . Furthermore, these findings can inform the design of more efficient energy recovery reactors for particle-based systems and can be integrated into system-level concentrated solar power models with thermal storage to optimize operating conditions.

14 SOLAR ENERGY↗
DOE OSTI2022

Detector Simulation Challenges for Future Accelerator Experiments

Detector simulation is a key component for studies on prospective future high-energy colliders, the design, optimization, testing and operation of particle physics experiments, and the analysis of the data collected to perform physics measurements. This review starts from the current state of the art technology applied to detector simulation in high-energy physics and elaborates on the evolution of software tools developed to address the challenges posed by future accelerator programs beyond the HL-LHC era, into the 2030–2050 period. New accelerator, detector, and computing technologies set the stage for an exercise in how detector simulation will serve the needs of the high-energy physics programs of the mid 21st century, and its potential impact on other research domains.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Intercomparison of Aerosol Instrumental Responses Using AEROTRAK, SMPS, and APS in a Simulated Building

In this work, three aerosol instruments were intercompared by using data collected from field campaigns in a simulated building environment during the last quarter of 2020 from October to December at Oak Ridge National Laboratory. The three aerosol instruments—AeroTrak Model 9110, Scanning Mobility Particle Spectrometer (SMPS) 3080L + 3025A, and Aerodynamic Particle Sizer (APS) 3320—were manufactured by TSI Inc. to perform in situ measurements of particle number concentration in the air. These instruments were co-located in one room in which an aerosol generator was used to produce a defined aerosol population for the instruments to sample and detect in each campaign. The SMPS measured aerosol particles with diameters from 0.01 to 0.7 µm, APS measured aerosol particles with diameters from 0.5 to 20 µm, and AeroTrak measured aerosol particles with diameters from 0.1 to 10 µm. The results suggest that although AeroTrak is fast and easy to use, it does not provide measurements comparable with those obtained by SMPS and APS. The differences in particle counts were orders of magnitude in size (i.e., the AeroTrak data were unreasonably small in the tested scenarios). The results clearly indicate that AeroTrak should not be used in ambient applications in which the level of particle concentration is expected to be high. This would cause difficulty to a campaign in which the dilution factor is unknow a priori. The high particle concentration could also lead to an erroneous aerosol size distribution (e.g., a bimodal size distribution was measured as opposed to a single-mode one). The number concentration limit that AeroTrak is expected to run reliably (i.e., with a counting coincidence error less than ±10%) is approximately three to four per cubic centimeter. This suggests that the instrument is best used in a clean room environment, not in an ambient or indoor application, such as the Flexible Research Platform campaigns.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

SuperCDMS Event Reconstruction Using Convolutional Neural Networks [Thesis]

The SuperCDMS experiment uses cryogenic silicon and germanium detectors to search for dark matter candidates such as WIMPs (Weakly Interacting Massive Particles) streaming through the Earth. Collisions in the silicon and germanium crystals are expected to produce phonons whose thermal signatures can be measured. This thesis first describes the integration of a new Signal Distribution Unit (SDU) to the SuperCDMS data acquisition system, which allows for synchronization of multiple detectors and electronic/mechanical noise characterization via accelerometer, antenna, and AC phase measurements. From SuperCDMS detector data it is necessary to reconstruct the energies of the particle events. This thesis explores the use of Convolutional Neural Networks (CNNs) to perform this reconstruction and finds that, although they perform well, changing the noise model breaks the model and requires the neural network to be retrained. In order to mitigate this issue, a new CNN model is proposed which includes the noise Power Spectral Density (PSD) of the data as an additional input to the CNN. While it proves to be effective as a denoising algorithm, it still fails for data with a different noise model. However, including data from multiple PSDs in the neural network training sample allows it to handle data with different types of noise while still maintaining the quality of the reconstruction. Nevertheless, neural networks trained even on multiple PSDs do not robustly handle data taken with PSDs dissimilar to those in the training sample, suggesting that CNNs may need to be retrained whenever the noise environment changes in a significant way.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗